{"id": "2503.01425", "title": "MeshPad: Interactive Sketch-Conditioned Artist-Reminiscent Mesh Generation and Editing", "abstract": "We introduce MeshPad, a generative approach that creates 3D meshes from sketch inputs. Building on recent advances in artist-reminiscent triangle mesh generation, our approach addresses the need for interactive mesh creation. To this end, we focus on enabling consistent edits by decomposing editing into 'deletion' of regions of a mesh, followed by 'addition' of new mesh geometry. Both operations are invoked by simple user edits of a sketch image, facilitating an iterative content creation process and enabling the construction of complex 3D meshes. Our approach is based on a triangle sequence-based mesh representation, exploiting a large Transformer model for mesh triangle addition and deletion. In order to perform edits interactively, we introduce a vertex-aligned speculative prediction strategy on top of our additive mesh generator. This speculator predicts multiple output tokens corresponding to a vertex, thus significantly reducing the computational cost of inference and accelerating the editing process, making it possible to execute each editing step in only a few seconds. Comprehensive experiments demonstrate that MeshPad outperforms state-of-the-art sketch-conditioned mesh generation methods, achieving more than 22% mesh quality improvement in Chamfer distance, and being preferred by 90% of participants in perceptual evaluations.", "date": "2025-03-03", "categories": []}
{"id": "2508.15228", "title": "Collaborative Multi-Modal Coding for High-Quality 3D Generation", "abstract": "3D content inherently encompasses multi-modal characteristics and can be projected into different modalities (e.g., RGB images, RGBD, and point clouds). Each modality exhibits distinct advantages in 3D asset modeling: RGB images contain vivid 3D textures, whereas point clouds define fine-grained 3D geometries. However, most existing 3D-native generative architectures either operate predominantly within single-modality paradigms-thus overlooking the complementary benefits of multi-modality data-or restrict themselves to 3D structures, thereby limiting the scope of available training datasets. To holistically harness multi-modalities for 3D modeling, we present TriMM, the first feed-forward 3D-native generative model that learns from basic multi-modalities (e.g., RGB, RGBD, and point cloud). Specifically, 1) TriMM first introduces collaborative multi-modal coding, which integrates modality-specific features while preserving their unique representational strengths. 2) Furthermore, auxiliary 2D and 3D supervision are introduced to raise the robustness and performance of multi-modal coding. 3) Based on the embedded multi-modal code, TriMM employs a triplane latent diffusion model to generate 3D assets of superior quality, enhancing both the texture and the geometric detail. Extensive experiments on multiple well-known datasets demonstrate that TriMM, by effectively leveraging multi-modality, achieves competitive performance with models trained on large-scale datasets, despite utilizing a small amount of training data. Furthermore, we conduct additional experiments on recent RGB-D datasets, verifying the feasibility of incorporating other multi-modal datasets into 3D generation.", "date": "2025-08-21", "categories": []}
{"id": "2606.20131", "title": "TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields", "abstract": "We present TriFlow, a new generative approach for producing compact 3D meshes with artist-like triangle topology directly from input geometry conditions such as signed distance fields. Our key insight is to represent mesh topology as a nearest-vertex vector field (NVF) defined over the surface, where each point encodes its association to the nearest triangle vertex in the local barycentric frame. We train a latent flow-matching model to synthesize this field, enabling topology generation conditioned on the input geometry. To extract a coherent mesh, we cluster surface regions using the generated NVF and guide a constrained quadric error metric (QEM) mesh simplification with topology-aware optimization. This yields output meshes that closely match the input geometry while exhibiting structured, artist-like connectivity. Experiments demonstrate that TriFlow achieves stronger generalization and significantly improved topology quality compared to state-of-the-art learning-based approaches, alongside 90% lower Chamfer Distance and an 8x speedup.", "date": "2026-06-18", "categories": []}
{"id": "2506.24121", "title": "TextMesh4D: Zero-shot Text-to-4D Mesh Generation", "abstract": "Large-scale, high-quality dynamic 3D (4D) assets are essential for learning physically grounded representations, but remain costly to capture and annotate at scale. This limits the viability of supervised 4D learning and motivates zero-shot text-to-4D generation leveraging pretrained diffusion priors. To model complex dynamics, prior methods typically adopt implicit 3D representations (e.g., NeRFs or 3DGS) for their deformation capacity. However, their implicit nature provides limited control over surface topology, which hinders high-fidelity geometry and makes temporally coherent surface reconstruction challenging. To address these limitations, we explore zero-shot text-to-4D mesh generation. However, a structural mismatch arises when combining diffusion-based guidance with topology-constrained meshes: the guidance is noisy and spatially inconsistent, while meshes impose severe topological constraints, making direct vertex-level deformation unstable. In this paper, we introduce TextMesh4D, the first zero-shot framework for text-to-4D that directly generates dynamic meshes by addressing the above challenge at two complementary levels. Geometrically, we shift deformation modeling from vertices to faces via a Jacobian Deformation Field (JDF), enabling topology-aware surface reconstruction through an integrability-enforcing integration formulation. Semantically, we propose a Local-Global Semantic Regularizer (LGSR) that preserves identity over time by jointly constraining local deformation plausibility and global shape consistency. Extensive experiments demonstrate state-of-the-art temporal consistency, structural fidelity, and visual quality, while remaining efficient on a single 24GB GPU.", "date": "2025-06-30", "categories": []}
{"id": "2605.07971", "title": "DVD: Discrete Voxel Diffusion for 3D Generation and Editing", "abstract": "We introduce Discrete Voxel Diffusion (DVD), a discrete diffusion framework to generate, assess, and edit sparse voxels for SLat (Structured LATent) based 3D generative pipelines. Although discrete diffusion has not generally displaced continuous diffusion in image-like generation, we show that it can be an effective first-stage prior for sparse voxel scaffolds. By treating voxel occupancy as a native discrete variable, DVD avoids continuous-to-discrete thresholding and provides a simple framework for voxel generation, uncertainty estimation, and editing. Beyond quality gains, DVD provides more interpretable generation dynamics through explicit categorical modeling. Furthermore, we leverage the predictive entropy as a robust uncertainty metric to identify ambiguous voxel regions and complicated samples, facilitating tasks such as data filtering and quality assessment. Finally, we propose a lightweight fine-tuning strategy using block-structured perturbation patterns. This approach empowers the model to inpaint and edit voxels within a single sampling round, requiring negligible auxiliary computation and no additional model evaluations. Code is available at https://github.com/TeCai/DVD.", "date": "2026-05-08", "categories": []}
{"id": "2603.01371", "title": "TIMI: Training-Free Image-to-3D Multi-Instance Generation with Spatial Fidelity", "abstract": "Precise spatial fidelity in Image-to-3D multi-instance generation is critical for downstream real-world applications. Recent work attempts to address this by fine-tuning pre-trained Image-to-3D (I23D) models on multi-instance datasets, which incurs substantial training overhead and struggles to guarantee spatial fidelity. In fact, we observe that pre-trained I23D models already possess meaningful spatial priors, which remain underutilized as evidenced by instance entanglement issues. Motivated by this, we propose TIMI, a novel Training-free framework for Image-to-3D Multi-Instance generation that achieves high spatial fidelity. Specifically, we first introduce an Instance-aware Separation Guidance (ISG) module, which facilitates instance disentanglement during the early denoising stage. Next, to stabilize the guidance introduced by ISG, we devise a Spatial-stabilized Geometry-adaptive Update (SGU) module that promotes the preservation of the geometric characteristics of instances while maintaining their relative relationships. Extensive experiments demonstrate that our method yields better performance in terms of both global layout and distinct local instances compared to existing multi-instance methods, without requiring additional training and with faster inference speed.", "date": "2026-03-02", "categories": []}
{"id": "2604.25371", "title": "PhyloSDF: Phylogenetically-Conditioned Neural Generation of 3D Skull Morphology via Residual Flow Matching", "abstract": "Generating novel, biologically plausible three-dimensional morphological structures is a fundamental challenge in computational evolutionary biology, hampered by extreme data scarcity and the requirement that generated shapes respect phylogenetic relationships among species. In this work, we present PhyloSDF, a phylogenetically-conditioned neural generative model for 3D biological morphology that integrates two innovations: (1) a DeepSDF auto-decoder regularized by a novel Phylogenetic Consistency Loss that structures the latent space to correlate with evolutionary distances (Pearson r=0.993); (2) a Residual Conditional Flow Matching (Residual CFM) architecture that factorizes generation into analytic species-centroid lookup and learned residual prediction, enabling generation from as few as ~4 specimens per species. We evaluate PhyloSDF on 100 micro-CT-scanned skulls of Darwin's Finches and their relatives across 24 species. The model generates novel meshes achieving 88-129% of real intra-species variation at the code level, with all 180 generated meshes verified as non-memorized. Residual CFM surpasses denoising diffusion (which fails entirely at this scale), standard flow matching (which mode-collapses to 3-6% variation), and a Gaussian mixture baseline in both fidelity (Chamfer Distance 0.00181 vs. 0.00190) and morphometric Fr\\'{e}chet distance (10,641 vs. 13,322). Leave-one-species-out experiments across 18 species demonstrate phylogenetic extrapolation capability, and smooth latent interpolations produce biologically plausible ancestral skull reconstructions.", "date": "2026-04-28", "categories": []}
{"id": "2605.19876", "title": "Structural Energy Guidance for View-Consistent Text-to-3D Generation", "abstract": "Text-to-3D generation based on diffusion models often suffers from the Janus problem, leading to inconsistent geometry across viewpoints. This work identifies viewpoint bias in 2D diffusion priors as the main cause and proposes Structural Energy-Guided Sampling (SEGS), a training-free and plug-and-play framework to improve multi-view consistency. SEGS constructs a structural energy in the PCA subspace of U-Net features and injects its gradient into the denoising process. It can be easily integrated into SDS/VSD pipelines without retraining. Experiments show that SEGS reduces the Janus Rate by about 10% on average and improves View-CS scores across multiple baselines, including DreamFusion, Magic3D, and LucidDreamer. This method effectively alleviates viewpoint artifacts while preserving appearance fidelity, providing a flexible solution for high-quality text-to-3D content generation.", "date": "2026-05-19", "categories": []}
{"id": "2606.04621", "title": "MeshFlow: Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer", "abstract": "We present MeshFlow, a new method for generating artist-like 3D meshes. Current mesh generators often adopt Auto-Regressive (AR) next-token prediction, a natural choice given the discrete nature of mesh topology. However, AR methods scale poorly because the inference cost is quadratic in mesh size. They also require discretizing the vertex coordinates, which introduces quantization errors. To address these challenges, we introduce a Variational Autoencoder (VAE) that, supervised with a contrastive loss, represents both continuous vertex positions and discrete connectivity in a continuous latent space. This latent space is significantly more compact than prior token-based mesh representations. We then build a 3D generator based on a Rectified Flow transformer, generating all mesh vertices and edges in parallel. Our model generates meshes 18x faster than the fastest AR generator while also achieving excellent accuracy across standard mesh-generation metrics. Homepage: https://mesh-flow.github.io/, Code: https://github.com/facebookresearch/meshflow", "date": "2026-06-03", "categories": []}
{"id": "2605.21472", "title": "Stream3D: Sequential Multi-View 3D Generation via Evidential Memory", "abstract": "View-conditioned 3D generators such as SAM 3D, TRELLIS, and Hunyuan3D produce high-quality object reconstructions from a single view, but real-world visual observation often arrives as long monocular streams. Naively applying these generators to each streaming frame independently leads to severe temporal inconsistency in the generated results. To address this problem, we propose Stream3D, the first training-free streaming mechanism that turns a frozen view-conditioned 3D generator into a streaming generator with constant cross-chunk memory. Stream3D achieves this by maintaining a compact evidential memory, which selectively caches the most informative historical frames based on a proposed evidence score mechanism. As the stream progresses, the memory dynamically updates to retain a fixed number of informative frames, preventing the memory footprint from growing linearly with sequence length. This also prevents degradation over long sequences and keeps the underlying generator completely unchanged without retraining, architectural modifications, or auxiliary losses. Evaluated on both realistic and synthetic streaming benchmarks, Stream3D outperforms latent-transport baselines, including KV-cache reuse and flow-based feature editing, across both photometric and geometric metrics. More details can be found at: https://stream-3d.github.io/stream3d.github.io/.", "date": "2026-05-20", "categories": []}
{"id": "2602.22629", "title": "CRAG: Can 3D Generative Models Help 3D Assembly?", "abstract": "Most existing 3D assembly methods treat the problem as pure pose estimation, rearranging observed parts via rigid transformations. In contrast, human assembly naturally couples structural reasoning with holistic shape inference. Inspired by this intuition, we reformulate 3D assembly as a joint problem of assembly and generation. We show that these two processes are mutually reinforcing: assembly provides part-level structural priors for generation, while generation injects holistic shape context that resolves ambiguities in assembly. Unlike prior methods that cannot synthesize missing geometry, we propose CRAG, which simultaneously generates plausible complete shapes and predicts poses for input parts. Extensive experiments demonstrate state-of-the-art performance across in-the-wild objects with diverse geometries, varying part counts, and missing pieces. Project Page: https://ai4ce.github.io/CRAG/", "date": "2026-02-26", "categories": []}
{"id": "2606.11805", "title": "TextHOI-3D: Text-to-3D Hand-Object Interaction via Discrete Multi-View Generation and Joint Mesh Optimization", "abstract": "Text-conditioned 3D generation has progressed rapidly for images and isolated objects, but producing a hand-object mesh remains challenging: the output must preserve language semantics, cross-view consistency, object geometry, articulated hand shape, and physically plausible contact. We present TextHOI-3D, a staged framework that uses generated multi-view observations as an explicit interface between text-conditioned visual generation and geometry-aware hand-object recovery. TextHOI-3D learns a compact VQ token space for fixed-camera hand-object observations, predicts multi-view visual tokens from text with a CLIP-conditioned visual autoregressive model, and recovers a unified hand-object mesh through prior initialization, multi-view joint optimization, and anti-penetration refinement. The design separates semantic generation from geometric recovery while keeping both stages connected by a discrete multi-view representation. On HO3D-derived evaluations, the multi-view setting reduces object CD from 17.26 mm to 4.92 mm and penetration volume from 5.3721 cm^3 to 0.2193 cm^3 compared with a single-view counterpart, while improving hand errors and surface F-scores. These results support multi-view visual tokens as an effective intermediate representation for text-driven 3D hand-object mesh creation.", "date": "2026-06-10", "categories": []}
{"id": "2606.12099", "title": "ISAP-3D: Identity-Slot Aligned Part-Aware 3D Generation", "abstract": "Part-aware 3D generation aims to synthesize structured objects with semantically meaningful components, yet often suffers from structural ambiguity due to identity-layout entanglement. Existing methods either infer part identity and spatial layout implicitly, which can lead to unstable part allocation (e.g., slot swapping or part merging), or rely on strong layout conditions that are difficult to obtain in practice. We attribute this ambiguity to identity-slot permutation freedom: without explicit identity-slot alignment, the correspondence between semantic parts and generation slots is not identifiable during training, allowing multiple slot assignments to fit the same supervision and leading to inconsistent decomposition. Based on this insight, we argue that stable part-aware generation requires identity-aligned one-to-one slot modelling. We therefore propose an identity-slot aligned framework, ISAP-3D, which anchors each part with semantic identity tokens and performs identity-conditioned one-to-one layout prediction, followed by layout-conditioned geometry synthesis. Structured local-global conditioning maintains identity alignment across semantic, spatial, and geometric stages. We also construct a part-level dataset with a unified semantic protocol to enable learnable and consistent identity-slot alignment. Extensive experiments demonstrate improved structural stability, controllability, and robustness over state-of-the-art part-aware generation baselines.", "date": "2026-06-10", "categories": []}
{"id": "2601.03256", "title": "Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training", "abstract": "We present Muses, the first training-free method for fantastic 3D creature generation in a feed-forward paradigm. Previous methods, which rely on part-aware optimization, manual assembly, or 2D image generation, often produce unrealistic or incoherent 3D assets due to the challenges of intricate part-level manipulation and limited out-of-domain generation. In contrast, Muses leverages the 3D skeleton, a fundamental representation of biological forms, to explicitly and rationally compose diverse elements. This skeletal foundation formalizes 3D content creation as a structure-aware pipeline of design, composition, and generation. Muses begins by constructing a creatively composed 3D skeleton with coherent layout and scale through graph-constrained reasoning. This skeleton then guides a voxel-based assembly process within a structured latent space, integrating regions from different objects. Finally, image-guided appearance modeling under skeletal conditions is applied to generate a style-consistent and harmonious texture for the assembled shape. Extensive experiments establish Muses' state-of-the-art performance in terms of visual fidelity and alignment with textual descriptions, and potential on flexible 3D object editing. Project page: https://luhexiao.github.io/Muses.github.io/.", "date": "2026-01-06", "categories": []}
{"id": "2606.08957", "title": "Rethinking 3D Shape Generation: Diffusion over Superquadrics", "abstract": "Diffusion models have advanced 3D shape generation, yet most methods still denoise in high-cardinality spaces (e.g., voxel/SDF grids, meshes, or point clouds), which is computationally and memory intensive and makes it difficult to scale in terms of both higher resolution and stronger controllability. We rethink the diffusion representation and propose to move diffusion from dense geometry to compact geometric primitives, representing each shape as a small set of superquadrics. Instead of operating on thousands to millions of geometric representation values, we leverage 7KB superquadric parameters (pose, size, and shape), drastically reducing diffusion-state dimensionality and per-step compute/memory. Our diffusion-over-superquadrics improves scalability by supporting broader capabilities (e.g., resolution-free point-cloud decoding, part-level editing, and constraint-based design) and achieving competitive surface-fidelity and distributional performance on standard benchmarks after point-cloud decoding, while enabling efficient generation within 0.6s per shape for most conditions.", "date": "2026-06-08", "categories": []}
{"id": "2606.06887", "title": "ARAPDiffusion: ARAP Regularization for Diffusion-Based Deformable Shape Space Learning", "abstract": "This paper introduces ARAPDiffusion, a latent diffusion model to learn the underlying continuous shape space of a deformation shape collection. The key innovation is in injecting the as-rigid-as-possible (ARAP) deformation model as regularization losses into latent diffusion (LD), releasing the requirement of having abundant 3D training data for learning generative models. In contrast to the standard LD, we show how the ARAP model can be used to improve both the encoder/decoder and the LD model. The training procedure alternates between using the synthetic distribution defined by the LD model to develop a regularization loss that enhances the shape encoder/decoder and using the shape decoder to develop a regularization loss to improve the LD model. We also show the benefit of the LD paradigm in combining a representation-free LD process and an implicit shape decoder that is applicable to unorganized point clouds. The experimental results of unconditional and conditional shape generation demonstrate the advantages of ARAPDiffusion over baseline approaches.", "date": "2026-06-05", "categories": []}
{"id": "2606.07024", "title": "GuideCAD: A Lightweight Multimodal Framework for 3D CAD Model Generation via Prefix Embedding", "abstract": "Multi-modal approaches used for 3D CAD generation require substantial computational resources, necessitating efficient training. To address this, we propose GuideCAD, which leverages semantically rich visual-textual representations having only a small number of trainable parameters to generate 3D CAD models. Specifically, GuideCAD uses a mapping network that converts image embeddings into prefix embeddings, enabling a pretrained large language model to integrate visual and textual information. As a result, a transformer-based decoder predicts the construction sequence using the visual-textual embeddings in order to generate the 3D CAD model. For experimental evaluation, we construct a new dataset, referred to as GuideCAD, which consists of text-image pairs. Each pair includes a text prompt that represents a 3D CAD construction sequence and its corresponding 3D CAD image. Our experimental results show that GuideCAD generates comparably high-quality 3D CAD models while using approximately four times fewer parameters and achieving twice the training efficiency compared to fine-tuning approaches. We have released the source code and dataset for our method at: https://github.com/mskimS2/GuideCAD", "date": "2026-06-05", "categories": []}
{"id": "2606.04108", "title": "SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation", "abstract": "Single-view 3D generative models have achieved impressive visual quality, yet they are not designed to satisfy structural or functional requirements, and in practice, often fall short. Symmetry is one such requirement: violations, even subtle ones, on symmetry can render a model physically unusable. We present SymTRELLIS, a method that enforces arbitrary finite point group symmetries (rotational, reflectional, and polyhedral) during the flow-based 3D generation of TRELLIS.2, without retraining the underlying VAE or flow model. Our key idea is to approximate the latent-space action of spatial transformations as a learned linear operator on voxel latents, implemented as a lightweight spatial-transform latent mapper trained on generic, non-symmetric 3D data. At generation time, we enforce symmetry by averaging predicted flow velocities across all symmetry-equivalent transformations at each ODE step, a process we call velocity symmetrization. The symmetry specification can be estimated automatically from an initial TRELLIS.2 generation or supplied by the user, enabling deliberate fold manipulation beyond what the input image suggests. On a curated benchmark of 266 strictly symmetric objects spanning 2- to 20-fold rotations and polyhedral symmetry groups, SymTRELLIS substantially reduces all symmetry error metrics compared to TRELLIS.2, Hunyuan3D-2.1, and TripoSG, while maintaining reconstruction accuracy comparable to the base model.", "date": "2026-06-02", "categories": []}
{"id": "2606.04688", "title": "MeshWeaver: Sparse-Voxel-Guided Surface Weaving for Autoregressive Mesh Generation", "abstract": "Autoregressive mesh generation has gained attention by tokenizing meshes into sequences and training models in a language-modeling fashion. However, existing approaches suffer from two fundamental limitations: (i) low tokenization efficiency, which yields long token sequences and prevents scaling to high-poly meshes, and (ii) absence of geometry-aware guidance, as generation is conditioned only on global shape embeddings rather than local surface cues. We introduce MeshWeaver, an autoregressive framework that treats mesh generation as a surface weaving process by directly predicting the next vertex instead of independent coordinates. At its core is a multi-level sparse-voxel encoder that injects geometric context into the generative process in three complementary ways: providing voxel features as vertex representations, guiding token prediction via cross-attention to voxel features, and serving as a structural scaffold that constrains generation around the input surface. Our hierarchical design enables coarse-to-fine vertex prediction in a single decoding step, while tightly coupling the generative model with 3D geometry. Extensive experiments demonstrate that MeshWeaver achieves a state-of-the-art compression ratio of 18%, can generate meshes with up to 16K faces, and significantly improves geometric fidelity over prior approaches.", "date": "2026-06-03", "categories": []}
{"id": "2606.05162", "title": "Controllable Dynamic 3D Shape Generation via 3D Trajectories and Text", "abstract": "We introduce T2Mo, a feed-forward framework for controllable dynamic 3D shape generation conditioned on 3D trajectories and text. Due to the inherent ambiguity of language, generating precisely intended motions using text alone remains challenging. To address this, we adopt 3D trajectories as controllable spatial guidance, specifying the exact paths along which selected points should move. By combining both, T2Mo generates object motions that spatially adhere to the given trajectories while globally reflecting the text semantics. To robustly handle trajectory inputs with arbitrary configurations, ranging from dense to sparse and unevenly distributed, we further propose a shape-grounded trajectory embedding that maps an input trajectory set into a shape-aware token set covering the entire object. We conduct extensive comparisons against text-based baselines and cascaded video-based baselines that combine trajectory-guided video generation with video-to-dynamic mesh generation. Quantitative and qualitative evaluations, along with user studies, demonstrate that our approach produces motions that more faithfully follow the given prompts with higher expressiveness while preserving motion quality.", "date": "2026-06-03", "categories": []}
{"id": "2510.22491", "title": "LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation", "abstract": "Generating high-fidelity 3D geometries under explicit parameter constraints is central to engineering design, yet current methods often require large datasets and fail to provide reliable control beyond the training distribution. We introduce LAMP, a data-efficient framework for controllable and interpretable 3D generation that aligns signed distance function (SDF) decoders by overfitting each exemplar from a shared initialization, then generates new designs by solving a parameter-constrained affine mixing problem in the aligned weight space. To improve reliability, we propose a linearity-mismatch safety metric that detects when mixed decoders leave the valid local regime. We evaluate LAMP on DrivAerNet++, BlendedNet, and additional industry-level vehicle families, including sports cars, SUVs, and convertibles. LAMP enables controlled interpolation with as few as 50 samples, safe extrapolation up to 100% beyond training ranges, and performance-guided optimization under fixed parameters, outperforming conditional autoencoder and Deep Network Interpolation (DNI) baselines in extrapolation, data efficiency, and parameter fidelity. Our results demonstrate that LAMP advances controllable, data-efficient, and safe 3D generation for design exploration, dataset generation, and performance-driven optimization.", "date": "2025-10-26", "categories": []}
{"id": "2605.16813", "title": "QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning", "abstract": "The generation of production-ready quad-dominant meshes is a cornerstone of modern 3D content creation. Generating anisotropic quad-dominant meshes from point clouds is challenging, as existing methods are typically limited to producing either pure triangular meshes or pure quadrilateral meshes with isotropic densities. In this paper, we present QuadLink, a unified framework consisting of three stages for quad-dominant mesh generation by linking points into structured faces. QuadLink formulates polygonal mesh generation as a hybrid centroid-conditioned vertex linking model: it first predicts a unified set of anchors (vertices and face centroids), then learns centroid-conditioned links that associate vertices with face centroids, and finally assembles polygonal faces with a quad-first strategy guided by robust geometric verification strategies. This link-based formulation enables efficient generation of sparse and anisotropic quad-dominant meshes with coherent edge flow and meanwhile supporting hybrid polygonal topology. To construct training data for this model, we further introduce a Tri-to-Quad Operator that converts artistic triangle meshes into quad-dominant training data via global merge selection. Extensive experiments show that QuadLink produces production-ready quad-dominant meshes from point clouds and achieves improved geometric fidelity and topological quality compared to prior baselines. Our method natively supports hybrid polygonal topology, generalizing to arbitrary n-gon meshes without architectural changes.", "date": "2026-05-16", "categories": []}
{"id": "2503.06136", "title": "GSV3D: Gaussian Splatting-based Geometric Distillation with Stable Video Diffusion for Single-Image 3D Object Generation", "abstract": "Image-based 3D generation has vast applications in robotics and gaming, where high-quality, diverse outputs and consistent 3D representations are crucial. However, existing methods have limitations: 3D diffusion models are limited by dataset scarcity and the absence of strong pre-trained priors, while 2D diffusion-based approaches struggle with geometric consistency. We propose a method that leverages 2D diffusion models' implicit 3D reasoning ability while ensuring 3D consistency via Gaussian-splatting-based geometric distillation. Specifically, the proposed Gaussian Splatting Decoder enforces 3D consistency by transforming SV3D latent outputs into an explicit 3D representation. Unlike SV3D, which only relies on implicit 2D representations for video generation, Gaussian Splatting explicitly encodes spatial and appearance attributes, enabling multi-view consistency through geometric constraints. These constraints correct view inconsistencies, ensuring robust geometric consistency. As a result, our approach simultaneously generates high-quality, multi-view-consistent images and accurate 3D models, providing a scalable solution for single-image-based 3D generation and bridging the gap between 2D Diffusion diversity and 3D structural coherence. Experimental results demonstrate state-of-the-art multi-view consistency and strong generalization across diverse datasets. The code will be made publicly available upon acceptance.", "date": "2025-03-08", "categories": []}
{"id": "2605.28995", "title": "GAP3D: Generative Alignment of VLM Latents to Patch-Level Embeddings for 3D Generation", "abstract": "Recent approaches integrating vision-language models (VLMs) as prompt encoders for generative model conditioning typically rely on expensive end-to-end training or map features to compressed representations, discarding the dense spatial structure required for geometry-aware tasks like 3D asset generation. To address this, we propose GAP3D, a modular, diffusion-based approach that aligns VLM-generated latents directly to the complete, patch-level feature space of a pre-trained image encoder, enabling a frozen downstream generative model to utilize a VLM as prompt encoder while maintaining a spatially structured conditioning signal. Evaluated on 3D asset generation, our method bypasses the need for large-scale 3D data by training mainly on general-domain image-text pairs. It also exhibits emergent zero-shot capabilities for multimodal prompts, despite being trained exclusively on text input. Finally, while currently prioritizing high-level semantics over fine-grained detail, GAP3D demonstrates that the representation gap between VLM and image-encoder feature spaces can be partially bridged through diffusion-based alignment, taking the first steps towards a modular integration of foundation models through generative alignment to dense embedding spaces.", "date": "2026-05-27", "categories": []}
{"id": "2606.01021", "title": "Learning Neural Deformation Representation for 4D Dynamic Shape Generation", "abstract": "Recent developments in 3D shape representation opened new possibilities for generating detailed 3D shapes. Despite these advances, there are few studies dealing with the generation of 4D dynamic shapes that have the form of 3D objects deforming over time. To bridge this gap, we focus on generating 4D dynamic shapes with an emphasis on both generation quality and efficiency in this paper. HyperDiffusion, a previous work on 4D generation, proposed a method of directly generating the weight parameters of 4D occupancy fields but suffered from low temporal consistency and slow rendering speed due to motion representation that is not separated from the shape representation of 4D occupancy fields. Therefore, we propose a new neural deformation representation and combine it with conditional neural signed distance fields to design a 4D representation architecture in which the motion latent space is disentangled from the shape latent space. The proposed deformation representation, which works by predicting skinning weights and rigid transformations for multiple parts, also has advantages over the deformation modules of existing 4D representations in understanding the structure of shapes. In addition, we design a training process of a diffusion model that utilizes the shape and motion features that are extracted by our 4D representation as data points. The results of unconditional generation, conditional generation, and motion retargeting experiments demonstrate that our method not only shows better performance than previous works in 4D dynamic shape generation but also has various potential applications.", "date": "2026-05-31", "categories": []}
{"id": "2605.29655", "title": "SuperVoxelGPT: Adaptive and Ordered 3D Tokenization for Autoregressive Shape Generation", "abstract": "Autoregressive multimodal large language models (MLLMs) enable 3D generation but struggle to scale to high-resolution shapes due to inadequate 3D tokenizations. Compact set-based representations discard deterministic spatial ordering, leading to ambiguous sequence prediction, while uniform or octree-based voxel grids preserve ordering at the cost of severe redundancy and excessively long sequences. This structural trade-off limits stable and efficient autoregressive 3D generation. We present SuperVoxelGPT, a representation-first framework that resolves this tension through adaptive and deterministically ordered supervoxel tokenization. Given a prompt, we first predict a coarse geometric saliency distribution and construct a shape-adaptive supervoxel partition using saliency-guided centroidal Voronoi tessellation, allocating fine-grained cells to complex regions and larger cells to smooth regions. Conditioned on the text and ordered supervoxel layout, we introduce a SuperVoxelVAE and fine-tune a pretrained MLLM to autoregressively generate supervoxel tokens. Experiments on Trellis-500K show that SuperVoxelGPT reduces token sequence length to 12.8% of uniform voxel tokenization while achieving state-of-the-art generation quality and an average 10$\\times$ speedup over prior methods.", "date": "2026-05-28", "categories": []}
{"id": "2603.05425", "title": "RelaxFlow: Text-Driven Amodal 3D Generation", "abstract": "Image-to-3D generation faces inherent semantic ambiguity under occlusion, where partial observation alone is often insufficient to determine object category. In this work, we formalize text-driven amodal 3D generation, where text prompts steer the completion of unseen regions while strictly preserving input observation. Crucially, we identify that these objectives demand distinct control granularities: rigid control for the observation versus relaxed structural control for the prompt. To this end, we propose RelaxFlow, a training-free dual-branch framework that decouples control granularity via a Multi-Prior Consensus Module and a Relaxation Mechanism. Theoretically, we prove that our relaxation is equivalent to applying a low-pass filter on the generative vector field, which suppresses high-frequency instance details to isolate geometric structure that accommodates the observation. To facilitate evaluation, we introduce two diagnostic benchmarks, ExtremeOcc-3D and AmbiSem-3D. Extensive experiments demonstrate that RelaxFlow successfully steers the generation of unseen regions to match the prompt intent without compromising visual fidelity.", "date": "2026-03-05", "categories": []}
{"id": "2605.28763", "title": "CubePart: An Open-Vocabulary Part-Controllable 3D Generator", "abstract": "Interactive 3D assets used in games and simulation are typically decomposed into specific semantic parts to support animation, physics, and scripted behaviors, yet most generative 3D models produce either monolithic meshes or arbitrary part decompositions that cannot be aligned with application-specific requirements. We present CubePart, a generative framework for open-vocabulary, part-controllable 3D mesh generation that exposes part structure as an explicit inference-time control signal. Given a global text prompt and a user-defined parts schema expressed as an open-ended list of part names, our method generates a set of meshes - one per schema element - that assemble into a coherent object while respecting the specified semantic structure. To enable this capability, we introduce a scalable data pipeline to construct a large open-vocabulary, part-labeled 3D dataset, along with a two-stage generative architecture that separates global shape synthesis from part-level decoding. We demonstrate that the resulting assets can be directly integrated into game engines and driven by animation and behavior scripts without manual post-processing. Project Page: https://cubepart.github.io/", "date": "2026-05-27", "categories": []}
{"id": "2605.26137", "title": "AssetGen: Deployable 3D Asset Generation at Interactive Speed", "abstract": "While 3D generation is progressing rapidly, recent work has often focused on obtaining high-resolution assets, leaving user experience and deployability as afterthoughts. We present AssetGen, a 3D generator that focuses instead on these two aspects. Given one reference image, in 30 seconds it produces a high-quality mesh with baked normals, a color texture, and a controlled polygon budget suitable for real-time rendering, including mobile use cases. The AssetGen Flash variant further reduces latency to 14 seconds for interactive and agentic creation loops. Our model generates the object geometry with a coarse-to-refine VecSet framework, which implements mesh simplification, cleaning, and normal baking on the GPU, and a fast parallel UV unwrapping. It then generates textures in a multi-view fashion, followed by backprojection and 3D inpainting. Model distillation, kernel optimization, and pipeline parallelization are co-designed to accelerate the system end-to-end. We introduce numerous automated and blind human evaluations and demonstrate competitive visual quality against leading commercial solutions in 30 seconds and preview-quality results in less than 15 seconds. The final result is a system that supports AI-assisted, deployable 3D content creation in interactive workflows.", "date": "2026-05-22", "categories": []}
{"id": "2605.26149", "title": "AnySurf: Any Surface Generation with Directed Edge", "abstract": "Open surface components prevail in real industrial 3D content and support rendering, physical simulation and geometric editing. Garments serve as a typical open surface type, with numerous existing generation methods leveraging sewing patterns to generate 2D panels and stitch them into 3D shapes. Such domain-specific designs lack scalability and cannot generalize to shoes and accessories. Common field-based 3D generators prioritize watertight meshes and tend to create flawed double-layer structures on open surfaces. Though Trellis2 adopts field-free representation, its open surface results still contain normal and topology errors. We present AnySurf, a unified framework generating open, closed and hybrid 3D surfaces with accurate face orientation. Built on directed-edge enhanced Flexible Dual Grid (FDG-D), our representation retains normal direction information via oriented grid edges. We also propose ROS-FT post-training and a lightweight DE-Adapter with merely 1% extra parameters, facilitating directed edge learning while preserving original generation performance. We further construct Outfit3D dataset containing industrial garments and closed accessories. Our work transforms garment modeling into a universal 3D generation task. Experimental results demonstrate superior mesh quality and better practicality for downstream applications.", "date": "2026-05-23", "categories": []}
{"id": "2605.26182", "title": "BrickAnything: Geometry-Conditioned Buildable Brick Generation with Structure-Aware Tokenization", "abstract": "Generating physically buildable brick structures from 3D shapes requires more than geometric reconstruction: the output must also satisfy discrete part constraints and structural stability. Existing brick generation methods either rely on heuristic optimization, which can break down when the target 3D shape does not admit a feasible structure under predefined constraints, or generate brick sequences without explicitly modeling the underlying 3D geometry and assembly relations. In this work, we present BrickAnything, a geometry-conditioned autoregressive framework for generating buildable brick structures from diverse 3D representations. BrickAnything uses point clouds as a unified geometric interface and predicts brick sequences that reconstruct the target shape under assembly constraints. To model structural dependencies among bricks, we introduce a structure-aware tree tokenization, which represents brick structures through local attachment relations. This formulation makes sequence generation more consistent with the physical construction process, and reduces invalid intermediate states. We further introduce preference-based alignment post-training, validity-constrained decoding and adaptive rollback to improve buildability objectives such as stability and geometric fidelity. Extensive experiments demonstrate that BrickAnything produces geometrically faithful and physically realizable brick structures, and that the proposed tokenization effectively reduces rollback and regeneration compared with conventional ordering strategies.", "date": "2026-05-25", "categories": []}
{"id": "2605.21572", "title": "PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects", "abstract": "Simulation-ready physical 3D assets have emerged as a promising direction owing to their broad applicability in downstream tasks. However, most existing 3D generation methods either neglect physical properties or are limited to a single asset category, e.g., rigid, deformable, or articulated objects. To address these limitations, we introduce PhysX-Omni, a unified framework for simulation-ready physical 3D generation across diverse asset types. Specifically, we develop a novel and efficient geometry representation tailored for Vision-Language Models, which directly encodes high-resolution 3D structures without compression, significantly improving generation performance. In addition, we construct the first general simulation-ready 3D dataset, PhysXVerse, covering diverse indoor and outdoor categories. Furthermore, to comprehensively and flexibly evaluate both generative and understanding capabilities in the wild, we propose PhysX-Bench, which encompasses six key attributes: geometry, absolute scale, material, affordance, kinematics, and function description. Extensive experiments with conventional metrics and PhysX-Bench show that PhysX-Omni performs strongly in both generation and understanding. Moreover, additional studies further validate the potential of PhysX-Omni for applications in simulation-ready scene generation and robotic policy learning. We believe PhysX-Omni can significantly advance a wide range of downstream applications, particularly in embodied AI and physics-based simulation.", "date": "2026-05-20", "categories": []}
{"id": "2605.20274", "title": "PolycubeNet: A Dual-latent Diffusion Model for Polycube-Based Hexahedral Mesh Generation", "abstract": "Hexahedral meshes are widely used in simulation pipelines, yet automatic generation remains challenging for complex CAD geometries. Polycube-based hexahedral meshing is a representative approach due to its regular, parameterization-friendly structure, but existing polycube construction methods often rely on intricate surface segmentation and local heuristics, which can produce artifacts or fail on difficult shapes.\n  In this paper, we propose an end-to-end framework for polycube generation based on conditional diffusion models. Given an input geometry represented as a point cloud, our method directly produces a corresponding polycube point cloud, eliminating the need for explicit surface segmentation or predefined polycube templates. At the core of our approach is a dual-latent conditional diffusion architecture that confines computationally expensive self-attention operations to a fixed-capacity, low-dimensional latent space. This design effectively decouples computational complexity from the resolution of both the input geometry and the output polycube, thereby avoiding the quadratic cost typical of point cloud self-attention mechanisms while supporting flexible input and output resolutions.\n  To obtain a hexahedral mesh, the generated polycube is aligned to the input shape via rigid and non-rigid point cloud registration to establish surface correspondence, followed by a polycube-to-hex pipeline. We additionally create and release a paired dataset of CAD meshes and their corresponding polycube meshes, together with the core implementation of our model. Experiments show that PolycubeNet generalizes to complex CAD models with arbitrary genus and produces high-quality polycube structures within seconds, improving robustness and efficiency over prior learning-based approaches.", "date": "2026-05-19", "categories": []}
{"id": "2605.20733", "title": "Sketch2MinSurf: Vision-Language Guided Generation of Editable Minimal Surfaces from Hand-Drawn Sketches", "abstract": "Converting hand-drawn sketches into structured 3D geometries remains challenging due to the difficulty of representing non-Euclidean surfaces and maintaining topological consistency. Existing generative models such as GANs, NeRFs, and diffusion architectures often fail to produce editable manifolds directly usable in downstream design workflows. We present Sketch2MinSurf, a hybrid vision-language and geometric optimization framework that integrates vision-language guidance with minimal-surface theory to generate smooth and editable 3D surfaces from hand-drawn sketches. The core of our approach is a spatial-topological encoding that represents geometry as tuples of node coordinates and real/virtual edge skeletons, enabling stable topological control during generation. We further introduce the Sketch2MinSurf Structural Loss (S2MS-Loss), a reward-modulated objective that jointly constrains geometric reconstruction and topological coherence. On a test set of 100 sketches, Sketch2MinSurf achieves a topological similarity score of 0.844, outperforming existing sketch-to-shape baselines. The generated manifolds are directly editable and free from non-manifold artifacts. A public art installation at a university showcases the method's potential for human-intent-driven 3D form generation. The dataset and code are available at https://anonymous.4open.science/r/Sketch2MinSurf/.", "date": "2026-05-20", "categories": []}
{"id": "2605.21121", "title": "ROAR-3D: Routing Arbitrary Views for High-Fidelity 3D Generation", "abstract": "Single-image-to-3D generative models can now produce high-quality geometry, yet conditioning on a single view inevitably introduces ambiguity about unseen regions. Multi-view conditioning can reduce this ambiguity, but existing methods either require fixed canonical viewpoints or rely on external reconstruction modules that impose heavy training costs and limit generation quality. We observe that pretrained single-view models already possess strong 2D-to-3D grounding that can be reused for multi-view conditioning. However, a closer analysis reveals that their conditioning mechanism entangles orientation control with geometry transfer, two functions that conflict when images from different viewpoints are naively combined. Based on this analysis, we propose ROAR-3D, a lightweight method that upgrades a pretrained single-view model to accept an arbitrary number of unposed images. A token-wise view router assigns each 3D latent token to its most relevant view, implicitly establishing 2D-to-3D correspondences without explicit pose input. A dual-stream attention design preserves the pretrained primary-view behavior while routing auxiliary views through a separate path dedicated to geometric enrichment. An orientation perturbation strategy ensures the auxiliary path learns orientation-independent geometry transfer. These components introduce minimal trainable parameters and add negligible inference overhead relative to the single-view baseline. ROAR-3D achieves state-of-the-art multi-view 3D generation quality and supports test-time view scaling from 1 to 12+ views with consistent improvements.", "date": "2026-05-20", "categories": []}
{"id": "2605.19350", "title": "CompoSE: Compositional Synthesis and Editing of 3D Shapes via Part-Aware Control", "abstract": "Creating and editing high-quality 3D content remains a central challenge in computer graphics. We address this challenge by introducing CompoSE, a novel method for Compositional Synthesis and Editing of 3D shapes via part-aware control. Our method takes as input a set of coarse geometric primitives (e.g., bounding boxes) that represent distinct object parts arranged in a particular spatial configuration, and synthesizes as output part-separated 3D objects that support localized granular (i.e., compositional) editing of individual parts. The key insight that enables our method is our use of a diffusion transformer architecture that alternates between processing each part locally and aggregating contextual information across parts globally, and features a novel conditioning technique that ensures strong adherence to the user's input. Importantly, our method learns to infer part semantics and symmetries directly from the user's coarse layout guidance, and does not require part-level text prompts. We demonstrate that our method enables powerful part-level editing capabilities, including context-aware substitution, addition, deletion, and style-preserving resizing operations. We show through extensive experiments that our method significantly outperforms existing approaches on guided synthesis, as measured by objective metrics and LLM-based evaluations.", "date": "2026-05-19", "categories": []}
{"id": "2511.18801", "title": "PartDiffuser: Part-wise 3D Mesh Generation via Discrete Diffusion", "abstract": "Existing autoregressive (AR) methods for generating artist-designed meshes struggle to balance global structural consistency with high-fidelity local details, and are susceptible to error accumulation. To address this, we propose PartDiffuser, a novel semi-autoregressive diffusion framework for point-cloud-to-mesh generation. The method first performs semantic segmentation on the mesh and then operates in a \"part-wise\" manner: it employs autoregression between parts to ensure global topology, while utilizing a parallel discrete diffusion process within each semantic part to precisely reconstruct high-frequency geometric features. PartDiffuser is based on the DiT architecture and introduces a part-aware cross-attention mechanism, using point clouds as hierarchical geometric conditioning to dynamically control the generation process, thereby effectively decoupling the global and local generation tasks. Experiments demonstrate that this method significantly outperforms state-of-the-art (SOTA) models in generating 3D meshes with rich detail, exhibiting exceptional detail representation suitable for real-world applications.", "date": "2025-11-24", "categories": []}
{"id": "2512.18953", "title": "Symmetry Matters: Auditing and Symmetrizing 3D Generative Models", "abstract": "Symmetry is a strong prior present in many object categories, yet standard benchmarks for 3D generative models rarely report whether this prior is preserved. We study symmetry preservation in unconditional point cloud generation. We first audit the symmetry of generated shapes by several 3D generative models and compute a normalized symmetry score based on the Chamfer Distance (CD). We show that although current 3D generative models achieve competitive results under standard evaluation, they reveal a persistent symmetry gap when a symmetry-aware evaluation protocol is applied. To test whether this gap is merely inherited from the training data, we evaluate these models over a mirrored-objects dataset derived from ShapeNet and analyze symmetry dynamics during training. Mechanistic interpretability techniques were employed at the sampling and latent levels to further show that reflection symmetry is not reliably encoded in the learned generative process. Finally, to address this gap, we propose a data-centric symmetry-aware intervention: training generative models on a half-objects dataset and reconstructing full objects by reflection during sampling. Across multiple backbones, this intervention substantially improves geometric consistency and visual plausibility while remaining competitive under standard metrics. These findings suggest that symmetry-aware evaluation is needed alongside standard benchmarks, and incoming 3D generative models should incorporate this prior explicitly, either during training or sampling.", "date": "2025-12-22", "categories": []}
{"id": "2604.28134", "title": "MeshReGen: A Unified 3D Geometry Regeneration Framework", "abstract": "We consider the problem of regenerating 3D objects from 2D images and initial 3D shapes. Most 3D generators operate in a one-shot fashion, converting text or images to a 3D object with limited controllability. We introduce instead MeshReGen, a 3D regenerator that is conditioned on an initial 3D shape. This conceptually simple formulation allows us to support numerous useful tasks, including 3D enhancement, reconstruction, and editing. MeshReGen uses a new conditioning mechanism based on VecSet, which allows the regenerator to update or improve the input geometry with consistent fine-grained details. MeshReGen learns a widely applicable regeneration prior from off-the-shelf 3D datasets via self-supervised pretext tasks and augmentations, without additional annotations. We evaluate both the geometric consistency and fine-grained quality of MeshReGen, achieving state-of-the-art performance in controllable 3D generation across several tasks.", "date": "2026-04-30", "categories": []}
{"id": "2605.16355", "title": "Generative 3D Gaussians with Learned Density Control", "abstract": "We present Density-Sampled Gaussians (DeG), a novel 3D representation designed to bridge the gap between adaptive rendering primitives and scalable generative modeling. Unlike existing approaches that constrain 3D Gaussians to fixed voxel grids or arrays, DeG models Gaussian centers as samples from a learnable probability density function defined over an octree. This formulation provides a rigorous mathematical framework for adaptive density control: by jointly optimizing the spatial density and Gaussian attributes under rendering supervision, our model naturally concentrates primitives in regions of high geometric complexity. We achieve this via a new render loss contribution gradient that serves as a fully differentiable analogue to the discrete densification and pruning heuristics used in standard Gaussian Splatting. The resulting representation is highly flexible, supporting variable-resolution decoding from a single latent code by simply adjusting the sampling budget. To enable generative synthesis, we train a latent diffusion model on DeG. We identify a critical challenge in applying diffusion to unordered set-structured latents, which can significantly slow convergence, and propose VecSeq, a canonical re-indexing mechanism that anchors latent tokens to a deterministic 3D Sobol sequence. This transforms the ambiguous set-generation problem into a robust sequence modeling task. Extensive experiments demonstrate that our pipeline achieves state-of-the-art quality in single-image-to-3D generation, combining the structural adaptivity of unstructured primitives with the training stability of grid-based methods.", "date": "2026-05-08", "categories": []}
{"id": "2605.16745", "title": "EVA01: Unified Native 3D Understanding and Generation via Mixture-of-Transformers", "abstract": "This paper addresses the challenge of integrating 3D meshes as a native modality within Multimodal Large Language Models (MLLMs). Diffusion-based large reconstruction models decouple semantic understanding from geometric reasoning, operating as stateless reconstructors conditioned on dense 2D pixel priors. Recent MLLM-based methods treat the 3D modality as an external output rather than a native component of the multimodal sequence, making incremental adaptations without a systematic analysis of how geometric manifolds align with MLLM feature spaces. We introduce EVA01, a unified framework that extends the modality boundary of MLLMs to natively incorporate 3D mesh understanding, generation, and context-aware editing. Built upon a Mixture-of-Transformers (MoT) architecture, EVA01 decouples the model into a pre-trained Understanding Expert ($E_{\\mathrm{und}}$) and a structurally mirrored Generation Expert ($E_{\\mathrm{gen}}$), coupled through shared global self-attention with hard modality routing. This design aligns the semantic latent space of the MLLM backbone with the geometric manifold, enabling direct transfer of multimodal priors without intermediate 2D representations. Results show that EVA01 achieves state-of-the-art native text-to-3D generation fidelity and unlocks robust long-context multi-turn geometric editing with identity preservation, a capability fundamentally inaccessible to stateless reconstruction pipelines. Our findings further offer architectural insights for integrating 2D foundation models with 3D tasks, informing the design of 3D-native multimodal systems. Project Page: https://www.seeles.ai/research/pages/EVA01", "date": "2026-05-16", "categories": []}
{"id": "2605.16990", "title": "DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing", "abstract": "While 2D diffusion models have achieved remarkable success in identity-preserving personalization, extending this capability to 3D assets remains a significant challenge due to the complexities of multi-view consistency and spatial control. Inspired by these 2D advancements, we present a novel personalization method for text-guided 3D editing that enables compositional, object-level control through natural language. Given a 3D input, we render orthogonal views and extract object-level segmentation masks to isolate semantic components. We then learn distinct token embeddings for each component through a tailored two-phase optimization strategy: multi-view textual inversion with attention alignment, followed by full fine-tuning of multi-view diffusion model. During inference, these disentangled tokens seamlessly compose with editing prompts to generate multi-view consistent images, which are subsequently lifted into high-fidelity textured 3D meshes. Extensive evaluations across diverse editing scenarios demonstrate that our method successfully transfers the flexibility of 2D personalization to 3D, achieving state-of-the-art edit faithfulness and identity preservation compared to existing baselines.", "date": "2026-05-16", "categories": []}
{"id": "2605.18010", "title": "Functionalization via Structure Completion and Motion Rectification", "abstract": "Acquisition and creation of 3D assets have been largely view- or appearance-driven. As a result, existing digital 3D models often lack the requisite structural components to function as intended, such as joints, supports, interiors, or interaction elements. At the same time, even human-annotated motions are frequently error-prone, leading to physically implausible behavior. We introduce object functionalization, a novel task aimed at transforming visually plausible but non-functional 3D models into functional and physically operable ones. We formulate functionalization as a graph completion problem over a new functional graph representation, where labeled nodes represent object parts, labeled edges encode functional and contact relations, and movable nodes carry motion attributes, so that structural functional deficiencies manifest as missing nodes or incorrect edges. We develop a neural Graph Functionalizer (GraFu) to complete an incomplete graph representing a non-functional 3D object. The completed graph then drives a geometry realization stage that instantiates predicted connectors and structural elements in 3D, with the compelling side effect of rectifying erroneous human-annotated and predicted motions. To support training and evaluation, focusing on furniture as a rich and challenging target category, we introduce FurFun-233, a dataset of 233 paired non-functional and functionalized furniture models. On PartNet-Mobility (\"zero-shot\") and HSSD test sets, our method matches state-of-the-art methods in motion prediction accuracy while substantially improving functionality in terms of collision and connectivity.", "date": "2026-05-18", "categories": []}
{"id": "2605.18052", "title": "Efficient 3D Content Reconstruction and Generation", "abstract": "Automatic 3D content creation seeks to replace labor-intensive modeling and scanning pipelines with systems that can synthesize or recover 3D assets directly from text or images. Its applications span video games, virtual reality, robotics, and simulation, enabling rapid asset prototyping, diverse interactive world generation, and efficient 3D data collection for training foundation models. Contemporary solutions largely follow two complementary paradigms: (i) text- or image-to-3D generation, which learns priors over 3D geometry and appearance to create novel assets from natural language or a single view image; and (ii) 3D reconstruction, which estimates camera poses and geometry from RGB images. This thesis advances both directions. On the generation side, I introduce Instant3D, which combines multi-view diffusion with feed-forward sparse-view 3D reconstruction to produce high-quality assets in 5-20 seconds. On the reconstruction side, I develop FastMap, a structure-from-motion pipeline that achieves up to 10x speedup over prior state-of-the-art by using first-order optimization with fused GPU kernels extensively, while maintaining comparable pose accuracy and downstream novel view synthesis quality.", "date": "2026-05-18", "categories": []}
{"id": "2601.21798", "title": "CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models", "abstract": "Large Language Models(LLMs) have revolutionized text generation and multimodal perception,but their capabilities in 3D content generation remain underexplored. Existing methods compromise by producing either low-resolution meshes or coarse structural proxies, failing to capture finegrained geometry natively. In this paper, we propose CG-MLLM, a novel Multi-modal Large Language Model (MLLM) capable of 3D captioning and high-resolution 3D generation in a single framework. Leveraging the Mixture-ofTransformer architecture, CG-MLLM decouples disparate modeling needs, where the Token-level Autoregressive (TokenAR) Transformer handles token-level content, and the Block-level Autoregressive (BlockAR) Transformer handles blocklevel content. By integrating a pre-trained visionlanguage backbone with a specialized 3D VAE latent space, CG-MLLM facilitates long-context interactions between standard tokens and spatial blocks within a single integrated architecture. Experimental results show that CG-MLLM significantly outperforms existing MLLMs in generating high-fidelity 3D objects, effectively bringing high-resolution 3D content creation into the mainstream LLM paradigm. Beyond generation, we further observe that learning to produce 3D content transfers back to perception, strengthening the model's image-based 3D understanding.", "date": "2026-01-29", "categories": []}
{"id": "2501.12202", "title": "Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation", "abstract": "We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two foundation components: a large-scale shape generation model -- Hunyuan3D-DiT, and a large-scale texture synthesis model -- Hunyuan3D-Paint. The shape generative model, built on a scalable flow-based diffusion transformer, aims to create geometry that properly aligns with a given condition image, laying a solid foundation for downstream applications. The texture synthesis model, benefiting from strong geometric and diffusion priors, produces high-resolution and vibrant texture maps for either generated or hand-crafted meshes. Furthermore, we build Hunyuan3D-Studio -- a versatile, user-friendly production platform that simplifies the re-creation process of 3D assets. It allows both professional and amateur users to manipulate or even animate their meshes efficiently. We systematically evaluate our models, showing that Hunyuan3D 2.0 outperforms previous state-of-the-art models, including the open-source models and closed-source models in geometry details, condition alignment, texture quality, and etc. Hunyuan3D 2.0 is publicly released in order to fill the gaps in the open-source 3D community for large-scale foundation generative models. The code and pre-trained weights of our models are available at: https://github.com/Tencent/Hunyuan3D-2", "date": "2025-01-21", "categories": []}
{"id": "2605.13862", "title": "Seed3D 2.0: Advancing High-Fidelity Simulation-Ready 3D Content Generation", "abstract": "We present Seed3D 2.0, an advanced 3D content generation system built on Seed3D 1.0, with substantial improvements across generation fidelity, simulation-ready capabilities, and application coverage. For geometry, a coarse-to-fine two-stage pipeline decouples global structure learning from high-frequency detail recovery, while a locality-aware VAE achieves higher spatial compression and more efficient decoding. For texture and material generation, we replace the cascaded pipeline of Seed3D 1.0 with a unified PBR model that directly generates multi-view albedo and metallic-roughness maps, enhanced by Mixture-of-Experts scaling and VLM-based semantic conditioning for improved material precision and visual fidelity. Beyond single-object generation, Seed3D 2.0 introduces a simulation-ready model suite comprising scene layout planning, part-aware decomposition, and training-free articulation generation, enabling coherent scene construction and part-level physical interaction across physics and graphics engines. A large-scale human preference study against five recent commercial models shows that Seed3D 2.0 achieves consistent win rates of 69.0% to 89.9% in textured 3D asset generation. Seed3D 2.0 is available on https://exp.volcengine.com/ark/vision?_vtm_=0.0.c70961.d701978.0&mode=vision&modelId=doubao-seed3d-2-0-260328&tab=Gen3D", "date": "2026-04-22", "categories": []}
{"id": "2605.14594", "title": "TOPOS: High-Fidelity and Efficient Industry-Grade 3D Head Generation", "abstract": "High-fidelity 3D head generation plays a crucial role in the film, animation and video game industries. In industrial pipelines, studios typically enforce a fixed reference topology across all head assets, as such a clean and uniform topology is a prerequisite for production-level rigging, skinning and animation. In this paper, we present TOPOS, a framework tailored for single image conditioned 3D head generation that jointly recovers geometry and appearance under such an industry-standard topology. In contrast to general 3D generative models which produce triangle meshes with inconsistent topology and numerous vertices, hindering semantic correspondence and asset-level reuse, TOPOS generates head meshes with a fixed, studio-style topology, enabling consistent vertex-level correspondence across all generated heads. To model heads under this unified topology, we proposed a novel variational autoencoder structure, termed TOPOS-VAE. Inspired by multi-model large language models (MLLMs), our TOPOS-VAE leverages the Perceiver Resampler to convert input pointclouds sampled from head meshes of diverse topologies into the target reference topology. Building upon TOPOS-VAE's structured latent space, we train a rectified flow transformer, TOPOS-DiT, to efficiently generate high-fidelity head meshes from a single image. We further present TOPOS-Texture, an end-to-end module that produces relightable UV texture maps from the same portrait image via fine-tuning a multimodal image generative model. The generated textures are spatially aligned with the underlying mesh geometry and faithfully preserve high-frequency appearance details. Extensive experiments demonstrate that TOPOS achieves state-of-the-art performance on 3D head generation, surpassing both classical face reconstruction methods and general 3D object generative models, highlighting its effectiveness for digital human creation.", "date": "2026-05-14", "categories": []}
{"id": "2510.11303", "title": "sketch2symm: Symmetry-aware sketch-to-shape generation via semantic bridging", "abstract": "Sketch-based 3D reconstruction remains a challenging task due to the abstract and sparse nature of sketch inputs, which often lack sufficient semantic and geometric information. To address this, we propose Sketch2Symm, a two-stage generation method that produces geometrically consistent 3D shapes from sketches. Our approach introduces semantic bridging via sketch-to-image translation to enrich sparse sketch representations, and incorporates symmetry constraints as geometric priors to leverage the structural regularity commonly found in everyday objects. Experiments on mainstream sketch datasets demonstrate that our method achieves superior performance compared to existing sketch-based reconstruction methods in terms of Chamfer Distance, Earth Mover's Distance, and F-Score, verifying the effectiveness of the proposed semantic bridging and symmetry-aware design.", "date": "2025-10-13", "categories": []}
{"id": "2605.13129", "title": "Rigel3D: Rig-aware Latents for Animation-Ready 3D Asset Generation", "abstract": "Recent 3D generative models can synthesize high-quality assets, but their outputs are typically static: they lack the skeletal rigs, joint hierarchies, and skinning weights required for animation. This limits their use in games, film, simulation, virtual agents, and embodied AI, where assets must not only look plausible but also move plausibly. We introduce Rigel3D, a generative method for animation-ready 3D assets represented as rigged meshes. Unlike post-hoc auto-rigging methods that attach rigs to completed shapes, our method jointly models geometry and rig structure through coupled surface and skeleton structured latent representations. A rig-aware autoencoder decodes these representations into mesh geometry, skeleton topology, joint coordinates, and skinning weights, while a two-stage latent generative model synthesizes both surface and skeleton representations for image-conditioned generation. To support downstream animation workflows, we further introduce an open-vocabulary joint labeling module that embeds generated joints into a shared vision-language space, enabling correspondence to arbitrary retargeting templates. Experiments on large-scale rigged asset datasets demonstrate that our method generates diverse, high-quality animation-ready assets and outperforms existing rigging baselines across multiple metrics.", "date": "2026-05-13", "categories": []}
{"id": "2605.11977", "title": "Optimizing 4D Wires for Sparse 3D Abstraction", "abstract": "We present a unified framework for 3D geometric abstraction using a single continuous 4D wire, parameterized as a B-spline with spatial coordinates and variable width $(x,y,z,w)$. Existing approaches typically represent shapes as collections of many independent curve segments, which often leads to fragmented structures and limited physical realizability. In contrast, we show that a single continuous spline is sufficiently expressive to capture complex volumetric forms while enforcing global topological coherence. By imposing continuity, our method transforms 3D sketching from a local density-accumulation process into a global routing problem, providing a strong inductive bias toward cleaner aesthetics and improved structural coherence. To enable gradient-based optimization, we introduce a differentiable rendering pipeline that efficiently rasterizes variable-width curves with bounded projection error. This formulation supports robust optimization using modern guidance signals such as Score Distillation Sampling (SDS) or CLIP. We demonstrate applications including image-to-3D abstraction, multi-view wire art generation, and differentiable stylized surface filling. Experiments show that our unified representation produces structures with higher semantic fidelity and improved structural coherence compared to approaches based on collections of discrete curves.", "date": "2026-05-12", "categories": []}
{"id": "2605.08744", "title": "MeshFIM: Local Low-Poly Mesh Editing via Fill-in-the-Middle Autoregressive Generation", "abstract": "Autoregressive (AR) models can generate high-quality low-poly meshes from point clouds, but they still operate in an all-or-nothing manner: when a local region is unsatisfactory, the entire mesh must be regenerated, wasting computation and destroying satisfactory mesh structure elsewhere. We introduce MeshFIM, a Fill-in-the-Middle (FIM) framework that regenerates a target region of a low-poly mesh conditioned on the surrounding context. MeshFIM addresses three mesh-specific challenges: enforcing exact attachment along the exposed boundary, preserving topological order in the context, and suppressing overflow beyond the intended region. It does so with five complementary design choices: boundary vertex markers, context positional embeddings, expanded context width, context augmentation, and a low-poly geometry encoder whose gated subtraction mechanism focuses generation on the missing region by leveraging the difference between the reference surface and the existing mesh. Detailed ablation studies are presented to show the effectiveness of every introduced component. Based on MeshFIM, we demonstrate two applications: interactive brush-based editing and automatic defect repair on low-poly mesh (see Figure 1). Last but not least, experiments show that MeshFIM outperforms a range of baselines in mesh refinement, mesh repair and whole mesh generation plus stitch-back scheme.", "date": "2026-05-09", "categories": []}
{"id": "2605.09071", "title": "Probability-Flow Distillation: Exact Wasserstein Gradient Flow for High-Fidelity 3D Generation", "abstract": "Score Distillation Sampling (SDS) and its variants have been widely used for text-to-3D generation by distilling 2D image diffusion priors. However, the standard SDS objective is prone to severe mode collapse, frequently yielding over-smoothed and over-saturated results. Although recent advancements, such as Score Distillation via Inversion (SDI), mitigate these artifacts and produce visually sharper models, they ultimately fail to faithfully capture the full target distribution. In this work, we show that the bottleneck limiting the sampling capacity of SDI stems from its reliance on the posterior mean estimator, which is mathematically equivalent to a single-step Euler approximation of the deterministic reverse DDIM trajectory. To address this, we propose a naturally motivated extension termed Probability-Flow Distillation (PFD). We establish that PFD corresponds exactly to a Wasserstein gradient flow, thereby inducing principled distribution-matching dynamics. Finally, we show that PFD can synthesize 3D assets with fine-grained, high-fidelity details and achieve improved quality compared to existing methods.", "date": "2026-05-09", "categories": []}
{"id": "2605.10922", "title": "Pixal3D: Pixel-Aligned 3D Generation from Images", "abstract": "Recent advances in 3D generative models have rapidly improved image-to-3D synthesis quality, enabling higher-resolution geometry and more realistic appearance. Yet fidelity, which measures pixel-level faithfulness of the generated 3D asset to the input image, still remains a central bottleneck. We argue this stems from an implicit 2D-3D correspondence issue: most 3D-native generators synthesize shape in canonical space and inject image cues via attention, leaving pixel-to-3D associations ambiguous. To tackle this issue, we draw inspiration from 3D reconstruction and propose Pixal3D, a pixel-aligned 3D generation paradigm for high-fidelity 3D asset creation from images. Instead of generating in a canonical pose, Pixal3D directly generates 3D in a pixel-aligned way, consistent with the input view. To enable this, we introduce a pixel back-projection conditioning scheme that explicitly lifts multi-scale image features into a 3D feature volume, establishing direct pixel-to-3D correspondence without ambiguity. We show that Pixal3D is not only scalable and capable of producing high-quality 3D assets, but also substantially improves fidelity, approaching the fidelity level of reconstruction. Furthermore, Pixal3D naturally extends to multi-view generation by aggregating back-projected feature volumes across views. Finally, we show pixel-aligned generation benefits scene synthesis, and present a modular pipeline that produces high-fidelity, object-separated 3D scenes from images. Pixal3D for the first time demonstrates 3D-native pixel-aligned generation at scale, and provides a new inspiring way towards high-fidelity 3D generation of object or scene from single or multi-view images. Project page: https://ldyang694.github.io/projects/pixal3d/", "date": "2026-05-11", "categories": []}
{"id": "2605.06947", "title": "Rollback-Free Stable Brick Structures Generation", "abstract": "While autoregressive models have advanced 3D generation, creating physically stable brick structures remains a challenge due to the strict requirements of gravity and interconnectivity. Existing approaches rely on external physical simulators during inference to perform rejection sampling and brick-by-brick rollbacks, which severely bottlenecks efficiency. To address this, we propose a reinforcement learning paradigm that shifts physical validity enforcement from test-time correction to training-time policy optimization. By utilizing assembly-level rewards, the model optimizes for collision avoidance, global connectivity, structural interlocking, and shape conformity. This paradigm allows the model to internalize physical priors, enabling the first rollback-free generation of stable brick structures. Experimental results demonstrate that our approach achieves state-of-the-art generation quality while accelerating inference speed by orders of magnitude. Our code and dataset are available at https://github.com/miniHuiHui/STABLE. Our models are available at https://huggingface.co/miniHui/STABLE.", "date": "2026-05-07", "categories": []}
{"id": "2605.04412", "title": "Structured 3D Latents Are Surprisingly Powerful: Unleashing Generalizable Style with 2D Diffusion", "abstract": "3D asset generation plays a pivotal role in fields such as gaming and virtual reality, enabling the rapid synthesis of high-fidelity 3D objects from a single or multiple images. Building on this capability, enabling style-controllable generation naturally emerges as an important and desirable direction. However, existing approaches typically rely on style images that lie within or are similar to the training distribution of 3D generation models. When presented with out-of-distribution (OOD) styles, their performance degrades significantly or even fails. To address this limitation, we introduce \\textbf{DiLAST}: 2D Diffusion-based Latent Awakening for 3D Style Transfer. Specifically, we leverage a pretrained 2D diffusion model as a teacher to provide rich and generalizable style priors. By aligning rendered views with the target style under diffusion-based guidance, our method optimizes the structured 3D latent representations for stylization. We observe that this limitation stems not from insufficient model capacity, but from the underutilization of structured 3D latents, which are inherently expressive. Despite being trained on comparatively limited data, 3D generation models can leverage 2D diffusion guidance to steer denoising toward specific directions in latent space, thereby producing diverse, OOD styles. Extensive experiments across diverse data and multiple 3D generation backbones demonstrate the effectiveness and plug-and-play nature of our approach.", "date": "2026-05-06", "categories": []}
{"id": "2401.16764", "title": "BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion", "abstract": "Witnessing the evolution of text-to-image diffusion models, significant strides have been made in text-to-3D generation. Currently, two primary paradigms dominate the field of text-to-3D: the feed-forward generation solutions, capable of swiftly producing 3D assets but often yielding coarse results, and the Score Distillation Sampling (SDS) based solutions, known for generating high-fidelity 3D assets albeit at a slower pace. The synergistic integration of these methods holds substantial promise for advancing 3D generation techniques. In this paper, we present BoostDream, a highly efficient plug-and-play 3D refining method designed to transform coarse 3D assets into high-quality. The BoostDream framework comprises three distinct processes: (1) We introduce 3D model distillation that fits differentiable representations from the 3D assets obtained through feed-forward generation. (2) A novel multi-view SDS loss is designed, which utilizes a multi-view aware 2D diffusion model to refine the 3D assets. (3) We propose to use prompt and multi-view consistent normal maps as guidance in refinement. Our extensive experiment is conducted on different differentiable 3D representations, revealing that BoostDream excels in generating high-quality 3D assets rapidly, overcoming the Janus problem compared to conventional SDS-based methods. This breakthrough signifies a substantial advancement in both the efficiency and quality of 3D generation processes.", "date": "2024-01-30", "categories": []}
{"id": "2604.09132", "title": "Strips as Tokens: Artist Mesh Generation with Native UV Segmentation", "abstract": "Recent advancements in autoregressive transformers have demonstrated remarkable potential for generating artist-quality meshes. However, the token ordering strategies employed by existing methods typically fail to meet professional artist standards, where coordinate-based sorting yields inefficiently long sequences, and patch-based heuristics disrupt the continuous edge flow and structural regularity essential for high-quality modeling. To address these limitations, we propose Strips as Tokens (SATO), a novel framework with a token ordering strategy inspired by triangle strips. By constructing the sequence as a connected chain of faces that explicitly encodes UV boundaries, our method naturally preserves the organized edge flow and semantic layout characteristic of artist-created meshes. A key advantage of this formulation is its unified representation, enabling the same token sequence to be decoded into either a triangle or quadrilateral mesh. This flexibility facilitates joint training on both data types: large-scale triangle data provides fundamental structural priors, while high-quality quad data enhances the geometric regularity of the outputs. Extensive experiments demonstrate that SATO consistently outperforms prior methods in terms of geometric quality, structural coherence, and UV segmentation. Project page: https://ruixu.me/html/SATO/index.html", "date": "2026-04-10", "categories": []}
{"id": "2605.01743", "title": "MOC-3D: Manifold-Order Consistency for Text-to-3D Generation", "abstract": "With the burgeoning development of fields such as the Metaverse, Virtual Reality (VR), and Digital Twins, text-to-3D generation has emerged as a research hotspot in both academia and industry. Currently, optimization methods based on Score Distillation Sampling (SDS) utilizing 2D diffusion priors have become the mainstream technological paradigm in this field. However, due to the view bias of 2D priors and the mode-seeking ambiguity combined with gradient noise induced by high Classifier-Free Guidance (CFG), these methods still suffer from macro-topological inconsistency (e.g., the Janus problem) and micro-geometric discontinuity.\n  To address these challenges, we propose MOC-3D, a text-to-3D generation method based on geometric manifold and semantic view-order consistency. Built upon the ScaleDreamer framework, our method incorporates a Semantic View-Order Constraint Module and a Manifold-based Feature Continuity Module. The former aims to rectify macro-topological inconsistency, while the latter focuses on eliminating micro-geometric discontinuity. Specifically, the Semantic View-Order Constraint Module leverages the prior knowledge of CLIP to impose a Monotonicity Rank Constraint on semantic score representations across different views, thereby providing effective guidance for the global topological structure of 3D objects. Meanwhile, the Manifold-based Feature Continuity Module employs the Riemannian Metric on the Symmetric Positive Definite (SPD) manifold. By measuring the distance of feature statistical distributions in the Riemannian space, it promotes the smooth evolution and continuity of micro-textures across multi-views in a statistical sense. Under the macro-micro synergistic optimization of these two modules, our model can simultaneously improve macro-structural consistency and micro-detail continuity.", "date": "2026-05-03", "categories": []}
{"id": "2605.00345", "title": "Pose-Aware Diffusion for 3D Generation", "abstract": "Generating pose-aligned 3D objects is challenging due to the spatial mismatches and transformation ambiguities inherent in decoupled canonical-then-rotate paradigms. To this end, we introduce Pose-Aware Diffusion (PAD), a novel end-to-end diffusion framework that synthesizes 3D geometry directly within the observation space. By unprojecting monocular depth into a partial point cloud and explicitly injecting it as a 3D geometric anchor, PAD abandons canonical assumptions to enforce rigorous spatial supervision. This native generation intrinsically resolves pose ambiguity, producing high-fidelity pose-aligned assets. Extensive experiments demonstrate that PAD achieves superior geometric alignment and image-to-3D correspondence compared to state-of-the-art methods. Additionally, PAD naturally extends to compositional 3D scene reconstruction via a simple union of independently generated objects, highlighting its robust ability to preserve precise spatial layouts.", "date": "2026-05-01", "categories": []}
{"id": "2503.01448", "title": "Generative Human Geometry Distribution", "abstract": "Realistic human geometry generation is an important yet challenging task, requiring both the preservation of fine clothing details and the accurate modeling of clothing-body interactions. To tackle this challenge, we build upon Geometry distributions, a recently proposed representation that can model a single human geometry with high fidelity using a flow matching model. However, extending a single-geometry distribution to a dataset is non-trivial and inefficient for large-scale learning. To address this, we propose a new geometry distribution model by two key techniques: (1) encoding distributions as 2D feature maps rather than network parameters, and (2) using SMPL models as the domain instead of Gaussian and refining the associated flow velocity field. We then design a generative framework adopting a two staged training paradigm analogous to state-of-the-art image and 3D generative models. In the first stage, we compress geometry distributions into a latent space using a diffusion flow model; the second stage trains another flow model on this latent space. We validate our approach on two key tasks: pose-conditioned random avatar generation and avatar-consistent novel pose synthesis. Experimental results demonstrate that our method outperforms existing state-of-the-art methods, achieving a 57% improvement in geometry quality.", "date": "2025-03-03", "categories": []}
{"id": "2604.27504", "title": "REVIVE 3D: Refinement via Encoded Voluminous Inflated prior for Volume Enhancement", "abstract": "Recent generative models have shown strong performance in generating diverse 3D assets from 2D images, a fundamental research topic in computer vision and graphics. However, these models still struggle to generate voluminous 3D assets when the input is a flat image that provides limited 3D cues. We introduce REVIVE 3D, a two-stage, plug-and-play pipeline for generating voluminous 3D assets from flat images. In Stage 1, we construct an Inflated Prior by inflating the foreground silhouette to recover global volume and superimposing part-aware details to capture local structure. In Stage 2, 3D Latent Refinement injects Gaussian noise into the Inflated Prior's latent and then denoises it, using the prior's geometric cues to leverage the backbone's pretrained 3D knowledge. Furthermore, our framework supports image-conditioned 3D editing. To quantify volume and surface flatness, we propose Compactness and Normal Anisotropy. We validate Compactness and Normal Anisotropy through a user study, showing that these metrics align with human perception of volume and quality. We show that REVIVE 3D achieves state-of-the-art performance on a challenging flat image dataset, based on extensive qualitative and quantitative evaluations.", "date": "2026-04-30", "categories": []}
{"id": "2512.07834", "title": "Voxify3D: Pixel Art Meets Volumetric Rendering", "abstract": "Voxel art is a distinctive stylization widely used in games and digital media, yet automated generation from 3D meshes remains challenging due to conflicting requirements of geometric abstraction, semantic preservation, and discrete color coherence. Existing methods either over-simplify geometry or fail to achieve the pixel-precise, palette-constrained aesthetics of voxel art. We introduce Voxify3D, a differentiable two-stage framework bridging 3D mesh optimization with 2D pixel art supervision. Our core innovation lies in the synergistic integration of three components: (1) orthographic pixel art supervision that eliminates perspective distortion for precise voxel-pixel alignment; (2) patch-based CLIP alignment that preserves semantics across discretization levels; (3) palette-constrained Gumbel-Softmax quantization enabling differentiable optimization over discrete color spaces with controllable palette strategies. This integration addresses fundamental challenges: semantic preservation under extreme discretization, pixel-art aesthetics through volumetric rendering, and end-to-end discrete optimization. Experiments show superior performance (37.12 CLIP-IQA, 77.90% user preference) across diverse characters and controllable abstraction (2-8 colors, 20x-50x resolutions). Project page: https://yichuanh.github.io/Voxify-3D/", "date": "2025-12-08", "categories": []}
{"id": "2604.22847", "title": "Dream-Cubed: Controllable Generative Modeling in Minecraft by Training on Billions of Cubes", "abstract": "We introduce Dream-Cubed, a large-scale dataset of Minecraft worlds at voxel resolution, and a family of models using cubes as powerful compositional units for efficient generation of interactive 3D environments. Dream-Cubed comprises tens of billions of tokens from a carefully curated mixture of procedural biome terrain and high-quality human-authored maps. We use this dataset to conduct the first large-scale study of 3D diffusion models for voxel generation, analyzing discrete and continuous diffusion formulations, data compositions, and architectural design choices. Our models operate directly in the space of blocks, enabling efficient and semantically grounded generation while supporting interactive user workflows such as inpainting and outpainting from user-authored blocks. To quantitatively evaluate our models, we adapt the FID metric to assess semantic differences between real and generated world renderings, and validate generation quality through a human preference study. We release the full dataset, code, and all our pretrained models, which we hope will provide a foundation for future research in efficient generative modeling for structured, interactive 3D environments.", "date": "2026-04-22", "categories": []}
{"id": "2604.23010", "title": "GenAssets: Generating in-the-wild 3D Assets in Latent Space", "abstract": "High-quality 3D assets for traffic participants are critical for multi-sensor simulation, which is essential for the safe end-to-end development of autonomy. Building assets from in-the-wild data is key for diversity and realism, but existing neural-rendering based reconstruction methods are slow and generate assets that render well only from viewpoints close to the original observations, limiting their usefulness in simulation. Recent diffusion-based generative models build complete and diverse assets, but perform poorly on in-the-wild driving scenes, where observed actors are captured under sparse and limited fields of view, and are partially occluded. In this work, we propose a 3D latent diffusion model that learns on in-the-wild LiDAR and camera data captured by a sensor platform and generates high-quality 3D assets with complete geometry and appearance. Key to our method is a \"reconstruct-then-generate\" approach that first leverages occlusion-aware neural rendering trained over multiple scenes to build a high-quality latent space for objects, and then trains a diffusion model that operates on the latent space. We show our method outperforms existing reconstruction and generation based methods, unlocking diverse and scalable content creation for simulation.", "date": "2026-04-24", "categories": []}
{"id": "2604.21592", "title": "Sculpt4D: Generating 4D Shapes via Sparse-Attention Diffusion Transformers", "abstract": "Recent breakthroughs in 3D generative modeling have yielded remarkable progress in static shape synthesis, yet high-fidelity dynamic 4D generation remains elusive, hindered by temporal artifacts and prohibitive computational demand. We present Sculpt4D, a native 4D generative framework that seamlessly integrates efficient temporal modeling into a pretrained 3D Diffusion Transformer (Hunyuan3D 2.1), thereby mitigating the scarcity of 4D training data. At its core lies a Block Sparse Attention mechanism that preserves object identity by anchoring to the initial frame while capturing rich motion dynamics via a time-decaying sparse mask. This design faithfully models complex spatiotemporal dependencies with high fidelity, while sidestepping the quadratic overhead of full attention and reducing network total computation by 56%. Consequently, Sculpt4D establishes a new state-of-the-art in temporally coherent 4D synthesis and charts a path toward efficient and scalable 4D generation.", "date": "2026-04-23", "categories": []}
{"id": "2601.22858", "title": "Learning to Build Shapes by Extrusion", "abstract": "We introduce Text Encoded Extrusions (TEE), a text-based representation that expresses mesh construction as sequences of face extrusions rather than polygon lists, and a method for generating 3D meshes from TEE using a large language model (LLM). By learning extrusion sequences that assemble a mesh, similar to the way artists create meshes, our approach naturally supports arbitrary output face counts and produces manifold meshes by design, in contrast to recent mesh generative transformer based models. The learnt extrusion sequences can also be applied to existing meshes - enabling editing in addition to generation. To train our model, we decompose a library of quadrilateral meshes with non-self-intersecting face loops into constituent loops, which can be viewed as their building blocks, and finetune an LLM on the steps for reassembling the quadrilateral meshes by performing a sequence of extrusions. We demonstrate that our representation enables reconstruction, novel shape synthesis, and the addition of new features to existing meshes.", "date": "2026-01-30", "categories": []}
{"id": "2604.17472", "title": "UniMesh: Unifying 3D Mesh Understanding and Generation", "abstract": "Recent advances in 3D vision have led to specialized models for either 3D understanding (e.g., shape classification, segmentation, reconstruction) or 3D generation (e.g., synthesis, completion, and editing). However, these tasks are often tackled in isolation, resulting in fragmented architectures and representations that hinder knowledge transfer and holistic scene modeling. To address these challenges, we propose UniMesh, a unified framework that jointly learns 3D generation and understanding within a single architecture. First, we introduce a novel Mesh Head that acts as a cross model interface, bridging diffusion based image generation with implicit shape decoders. Second, we develop Chain of Mesh (CoM), a geometric instantiation of iterative reasoning that enables user driven semantic mesh editing through a closed loop latent, prompting, and re generation cycle. Third, we incorporate a self reflection mechanism based on an Actor Evaluator Self reflection triad to diagnose and correct failures in high level tasks like 3D captioning. Experimental results demonstrate that UniMesh not only achieves competitive performance on standard benchmarks but also unlocks novel capabilities in iterative editing and mutual enhancement between generation and understanding. Code: https://github.com/AIGeeksGroup/UniMesh. Website: https://aigeeksgroup.github.io/UniMesh.", "date": "2026-04-19", "categories": []}
{"id": "2504.10466", "title": "Art3D: Training-Free 3D Generation from Flat-Colored Illustration", "abstract": "Large-scale pre-trained image-to-3D generative models have exhibited remarkable capabilities in diverse shape generations. However, most of them struggle to synthesize plausible 3D assets when the reference image is flat-colored like hand drawings due to the lack of 3D illusion, which are often the most user-friendly input modalities in art content creation. To this end, we propose Art3D, a training-free method that can lift flat-colored 2D designs into 3D. By leveraging structural and semantic features with pre-trained 2D image generation models and a VLM-based realism evaluation, Art3D successfully enhances the three-dimensional illusion in reference images, thus simplifying the process of generating 3D from 2D, and proves adaptable to a wide range of painting styles. To benchmark the generalization performance of existing image-to-3D models on flat-colored images without 3D feeling, we collect a new dataset, Flat-2D, with over 100 samples. Experimental results demonstrate the performance and robustness of Art3D, exhibiting superior generalizable capacity and promising practical applicability. Our source code and dataset will be publicly available on our project page: https://joy-jy11.github.io/ .", "date": "2025-04-14", "categories": []}
{"id": "2601.17740", "title": "Learning Sewing Patterns via Latent Flow Matching of Implicit Fields", "abstract": "Sewing patterns define the structural foundation of garments and are essential for applications such as fashion design, fabrication, and physical simulation. Despite progress in automated pattern generation, accurately modeling sewing patterns remains difficult due to the broad variability in panel geometry and seam arrangements. In this work, we introduce a sewing pattern modeling method based on an implicit representation. We represent each panel using a signed distance field that defines its boundary and an unsigned distance field that identifies seam endpoints, and encode these fields into a continuous latent space that enables differentiable meshing. A latent flow matching model learns distributions over panel combinations in this representation, and a stitching prediction module recovers seam relations from extracted edge segments. This formulation allows accurate modeling and generation of sewing patterns with complex structures. We further show that it can be used to estimate sewing patterns from images with improved accuracy relative to existing approaches, and supports applications such as pattern completion and refitting, providing a practical tool for digital fashion design.", "date": "2026-01-25", "categories": []}
{"id": "2604.13905", "title": "Rethinking Image-to-3D Generation with Sparse Queries: Efficiency, Capacity, and Input-View Bias", "abstract": "We present SparseGen, a novel framework for efficient image-to-3D generation, which exhibits low input-view bias while being significantly faster. Unlike traditional approaches that rely on dense volumetric grids, triplanes, or pixel-aligned primitives, we model scenes with a compact sparse set of learned 3D anchor queries and a learned expansion operator that decodes each transformed query into a small local set of 3D Gaussian primitives. Trained under a rectified-flow reconstruction objective without 3D supervision, our model learns to allocate representation capacity where geometry and appearance matter, achieving significant reductions in memory and inference time while preserving multi-view fidelity. We introduce quantitative measures of input-view bias and utilization to show that sparse queries reduce overfitting to conditioning views while being representationally efficient. Our results argue that sparse set-latent expansion is a principled, practical alternative for efficient 3D generative modeling.", "date": "2026-04-15", "categories": []}
{"id": "2604.08746", "title": "AniGen: Unified $S^3$ Fields for Animatable 3D Asset Generation", "abstract": "Animatable 3D assets, defined as geometry equipped with an articulated skeleton and skinning weights, are fundamental to interactive graphics, embodied agents, and animation production. While recent 3D generative models can synthesize visually plausible shapes from images, the results are typically static. Obtaining usable rigs via post-hoc auto-rigging is brittle and often produces skeletons that are topologically inconsistent with the generated geometry. We present AniGen, a unified framework that directly generates animate-ready 3D assets conditioned on a single image. Our key insight is to represent shape, skeleton, and skinning as mutually consistent $S^3$ Fields (Shape, Skeleton, Skin) defined over a shared spatial domain. To enable the robust learning of these fields, we introduce two technical innovations: (i) a confidence-decaying skeleton field that explicitly handles the geometric ambiguity of bone prediction at Voronoi boundaries, and (ii) a dual skin feature field that decouples skinning weights from specific joint counts, allowing a fixed-architecture network to predict rigs of arbitrary complexity. Built upon a two-stage flow-matching pipeline, AniGen first synthesizes a sparse structural scaffold and then generates dense geometry and articulation in a structured latent space. Extensive experiments demonstrate that AniGen substantially outperforms state-of-the-art sequential baselines in rig validity and animation quality, generalizing effectively to in-the-wild images across diverse categories including animals, humanoids, and machinery. Homepage: https://yihua7.github.io/AniGen-web/", "date": "2026-04-09", "categories": []}
{"id": "2604.08760", "title": "SIC3D: Style Image Conditioned Text-to-3D Gaussian Splatting Generation", "abstract": "Recent progress in text-to-3D object generation enables the synthesis of detailed geometry from text input by leveraging 2D diffusion models and differentiable 3D representations. However, the approaches often suffer from limited controllability and texture ambiguity due to the limitation of the text modality. To address this, we present SIC3D, a controllable image-conditioned text-to-3D generation pipeline with 3D Gaussian Splatting (3DGS). There are two stages in SIC3D. The first stage generates the 3D object content from text with a text-to-3DGS generation model. The second stage transfers style from a reference image to the 3DGS. Within this stylization stage, we introduce a novel Variational Stylized Score Distillation (VSSD) loss to effectively capture both global and local texture patterns while mitigating conflicts between geometry and appearance. A scaling regularization is further applied to prevent the emergence of artifacts and preserve the pattern from the style image. Extensive experiments demonstrate that SIC3D enhances geometric fidelity and style adherence, outperforming prior approaches in both qualitative and quantitative evaluations.", "date": "2026-04-09", "categories": []}
{"id": "2603.11633", "title": "MV-SAM3D: Adaptive Multi-View Fusion for Layout-Aware 3D Generation", "abstract": "Recent unified 3D generation models have made remarkable progress in producing high-quality 3D assets from a single image. Notably, layout-aware approaches such as SAM3D can reconstruct multiple objects while preserving their spatial arrangement, opening the door to practical scene-level 3D generation. However, current methods are limited to single-view input and cannot leverage complementary multi-view observations, while independently estimated object poses often lead to physically implausible layouts such as interpenetration and floating artifacts.\n  We present MV-SAM3D, a training-free framework that extends layout-aware 3D generation with multi-view consistency and physical plausibility. We formulate multi-view fusion as a Multi-Diffusion process in 3D latent space and propose two adaptive weighting strategies -- attention-entropy weighting and visibility weighting -- that enable confidence-aware fusion, ensuring each viewpoint contributes according to its local observation reliability. For multi-object composition, we introduce physics-aware optimization that injects collision and contact constraints both during and after generation, yielding physically plausible object arrangements. Experiments on standard benchmarks and real-world multi-object scenes demonstrate significant improvements in reconstruction fidelity and layout plausibility, all without any additional training. Code is available at https://github.com/devinli123/MV-SAM3D.", "date": "2026-03-12", "categories": []}
{"id": "2604.07795", "title": "Image-Guided Geometric Stylization of 3D Meshes", "abstract": "Recent generative models can create visually plausible 3D representations of objects. However, the generation process often allows for implicit control signals, such as contextual descriptions, and rarely supports bold geometric distortions beyond existing data distributions. We propose a geometric stylization framework that deforms a 3D mesh, allowing it to express the style of an image. While style is inherently ambiguous, we utilize pre-trained diffusion models to extract an abstract representation of the provided image. Our coarse-to-fine stylization pipeline can drastically deform the input 3D model to express a diverse range of geometric variations while retaining the valid topology of the original mesh and part-level semantics. We also propose an approximate VAE encoder that provides efficient and reliable gradients from mesh renderings. Extensive experiments demonstrate that our method can create stylized 3D meshes that reflect unique geometric features of the pictured assets, such as expressive poses and silhouettes, thereby supporting the creation of distinctive artistic 3D creations. Project page: https://changwoonchoi.github.io/GeoStyle", "date": "2026-04-09", "categories": []}
{"id": "2603.26588", "title": "From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion", "abstract": "We present ToothCraft, a diffusion-based model for the contextual generation of tooth crowns, trained on artificially created incomplete teeth. Building upon recent advancements in conditioned diffusion models for 3D shapes, we developed a model capable of an automated tooth crown completion conditioned on local anatomical context. To address the lack of training data for this task, we designed an augmentation pipeline that generates incomplete tooth geometries from a publicly available dataset of complete dental arches (3DS, ODD). By synthesising a diverse set of training examples, our approach enables robust learning across a wide spectrum of tooth defects. Experimental results demonstrate the strong capability of our model to reconstruct complete tooth crowns, achieving an intersection over union (IoU) of 81.8% and a Chamfer Distance (CD) of 0.00034 on synthetically damaged testing restorations. Our experiments demonstrate that the model can be applied directly to real-world cases, effectively filling in incomplete teeth, while generated crowns show minimal intersection with the opposing dentition, thus reducing the risk of occlusal interference. Access to the code, model weights, and dataset information will be available at: https://github.com/ikarus1211/VISAPP_ToothCraft", "date": "2026-03-27", "categories": []}
{"id": "2604.05070", "title": "Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation", "abstract": "Simulation is essential for autonomous driving, yet current frameworks often model vehicles as rigid assets and fail to capture part-level articulation. With perception algorithms increasingly leveraging dynamics such as wheel steering or door opening, realistic simulation requires animatable vehicle representations. Existing CAD-based pipelines are limited by library coverage and fixed templates, preventing faithful reconstruction of in-the-wild instances.\n  We propose a generative framework that, from a single image or sparse multi-view input, synthesizes an animatable 3D Gaussian vehicle. Our method addresses two challenges: (i) large 3D asset generators are optimized for static quality but not articulation, leading to distortions at part boundaries when animated; and (ii) segmentation alone cannot provide the kinematic parameters required for motion. To overcome this, we introduce a part-edge refinement module that enforces exclusive Gaussian ownership and a kinematic reasoning head that predicts joint positions and hinge axes of movable parts. Together, these components enable faithful part-aware simulation, bridging the gap between static generation and animatable vehicle models.", "date": "2026-04-06", "categories": []}
{"id": "2509.07435", "title": "DreamLifting: A Plug-in Module Lifting MV Diffusion Models for 3D Asset Generation", "abstract": "The labor- and experience-intensive creation of 3D assets with physically based rendering (PBR) materials demands an autonomous 3D asset creation pipeline. However, most existing 3D generation methods focus on geometry modeling, either baking textures into simple vertex colors or leaving texture synthesis to post-processing with image diffusion models. To achieve end-to-end PBR-ready 3D asset generation, we present Lightweight Gaussian Asset Adapter (LGAA), a novel framework that unifies the modeling of geometry and PBR materials by exploiting multi-view (MV) diffusion priors from a novel perspective. The LGAA features a modular design with three components. Specifically, the LGAA Wrapper reuses and adapts network layers from MV diffusion models, which encapsulate knowledge acquired from billions of images, enabling better convergence in a data-efficient manner. To incorporate multiple diffusion priors for geometry and PBR synthesis, the LGAA Switcher aligns multiple LGAA Wrapper layers encapsulating different knowledge. Then, a tamed variational autoencoder (VAE), termed LGAA Decoder, is designed to predict 2D Gaussian Splatting (2DGS) with PBR channels. Finally, we introduce a dedicated post-processing procedure to effectively extract high-quality, relightable mesh assets from the resulting 2DGS. Extensive quantitative and qualitative experiments demonstrate the superior performance of LGAA with both text- and image-conditioned MV diffusion models. Additionally, the modular design enables flexible incorporation of multiple diffusion priors, and the knowledge-preserving scheme effectively preseves the 2D priors learned on massive image dataset, which leads to data efficient finetuning to lift the MV diffuison models for 3D generation with merely 69k multi-view instances.", "date": "2025-09-09", "categories": []}
{"id": "2604.04425", "title": "HandDreamer: Zero-Shot Text to 3D Hand Model Generation using Corrective Hand Shape Guidance", "abstract": "The emergence of virtual reality has necessitated the generation of detailed and customizable 3D hand models for interaction in the virtual world. However, the current methods for 3D hand model generation are both expensive and cumbersome, offering very little customizability to the users. While recent advancements in zero-shot text-to-3D synthesis have enabled the generation of diverse and customizable 3D models using Score Distillation Sampling (SDS), they do not generalize very well to 3D hand model generation, resulting in unnatural hand structures, view-inconsistencies and loss of details. To address these limitations, we introduce HandDreamer, the first method for zero-shot 3D hand model generation from text prompts. Our findings suggest that view-inconsistencies in SDS is primarily caused due to the ambiguity in the probability landscape described by the text prompt, resulting in similar views converging to different modes of the distribution. This is particularly aggravated for hands due to the large variations in articulations and poses. To alleviate this, we propose to use MANO hand model based initialization and a hand skeleton guided diffusion process to provide a strong prior for the hand structure and to ensure view and pose consistency. Further, we propose a novel corrective hand shape guidance loss to ensure that all the views of the 3D hand model converges to view-consistent modes, without leading to geometric distortions. Extensive evaluations demonstrate the superiority of our method over the state-of-the-art methods, paving a new way forward in 3D hand model generation.", "date": "2026-04-06", "categories": []}
{"id": "2604.01479", "title": "UniRecGen: Unifying Multi-View 3D Reconstruction and Generation", "abstract": "Sparse-view 3D modeling represents a fundamental tension between reconstruction fidelity and generative plausibility. While feed-forward reconstruction excels in efficiency and input alignment, it often lacks the global priors needed for structural completeness. Conversely, diffusion-based generation provides rich geometric details but struggles with multi-view consistency. We present UniRecGen, a unified framework that integrates these two paradigms into a single cooperative system. To overcome inherent conflicts in coordinate spaces, 3D representations, and training objectives, we align both models within a shared canonical space. We employ disentangled cooperative learning, which maintains stable training while enabling seamless collaboration during inference. Specifically, the reconstruction module is adapted to provide canonical geometric anchors, while the diffusion generator leverages latent-augmented conditioning to refine and complete the geometric structure. Experimental results demonstrate that UniRecGen achieves superior fidelity and robustness, outperforming existing methods in creating complete and consistent 3D models from sparse observations. Code is available at https://github.com/zsh523/UniRecGen.", "date": "2026-04-01", "categories": []}
{"id": "2604.02289", "title": "Omni123: Exploring 3D Native Foundation Models with Limited 3D Data by Unifying Text to 2D and 3D Generation", "abstract": "Recent multimodal large language models have achieved strong performance in unified text and image understanding and generation, yet extending such native capability to 3D remains challenging due to limited data. Compared to abundant 2D imagery, high-quality 3D assets are scarce, making 3D synthesis under-constrained. Existing methods often rely on indirect pipelines that edit in 2D and lift results into 3D via optimization, sacrificing geometric consistency. We present Omni123, a 3D-native foundation model that unifies text-to-2D and text-to-3D generation within a single autoregressive framework. Our key insight is that cross-modal consistency between images and 3D can serve as an implicit structural constraint. By representing text, images, and 3D as discrete tokens in a shared sequence space, the model leverages abundant 2D data as a geometric prior to improve 3D representations. We introduce an interleaved X-to-X training paradigm that coordinates diverse cross-modal tasks over heterogeneous paired datasets without requiring fully aligned text-image-3D triplets. By traversing semantic-visual-geometric cycles (e.g., text to image to 3D to image) within autoregressive sequences, the model jointly enforces semantic alignment, appearance fidelity, and multi-view geometric consistency. Experiments show that Omni123 significantly improves text-guided 3D generation and editing, demonstrating a scalable path toward multimodal 3D world models.", "date": "2026-04-02", "categories": []}
{"id": "2601.16148", "title": "ActionMesh: Animated 3D Mesh Generation with Temporal 3D Diffusion", "abstract": "Generating animated 3D objects is at the heart of many applications, yet most advanced works are typically difficult to apply in practice because of their limited setup, their long runtime, or their limited quality. We introduce ActionMesh, a generative model that predicts production-ready 3D meshes \"in action\" in a feed-forward manner. Drawing inspiration from early video models, our key insight is to modify existing 3D diffusion models to include a temporal axis, resulting in a framework we dubbed \"temporal 3D diffusion\". Specifically, we first adapt the 3D diffusion stage to generate a sequence of synchronized latents representing time-varying and independent 3D shapes. Second, we design a temporal 3D autoencoder that translates a sequence of independent shapes into the corresponding deformations of a pre-defined reference shape, allowing us to build an animation. Combining these two components, ActionMesh generates animated 3D meshes from different inputs like a monocular video, a text description, or even a 3D mesh with a text prompt describing its animation. Besides, compared to previous approaches, our method is fast and produces results that are rig-free and topology consistent, hence enabling rapid iteration and seamless applications like texturing and retargeting. We evaluate our model on standard video-to-4D benchmarks (Consistent4D, Objaverse) and report state-of-the-art performances on both geometric accuracy and temporal consistency, demonstrating that our model can deliver animated 3D meshes with unprecedented speed and quality.", "date": "2026-01-22", "categories": []}
{"id": "2604.00494", "title": "ARGS: Auto-Regressive Gaussian Splatting via Parallel Progressive Next-Scale Prediction", "abstract": "Auto-regressive frameworks for next-scale prediction of 2D images have demonstrated strong potential for producing diverse and sophisticated content by progressively refining a coarse input. However, extending this paradigm to 3D object generation remains largely unexplored. In this paper, we introduce auto-regressive Gaussian splatting (ARGS), a framework for making next-scale predictions in parallel for generation according to levels of detail. We propose a Gaussian simplification strategy and reverse the simplification to guide next-scale generation. Benefiting from the use of hierarchical trees, the generation process requires only \\(\\mathcal{O}(\\log n)\\) steps, where \\(n\\) is the number of points. Furthermore, we propose a tree-based transformer to predict the tree structure auto-regressively, allowing leaf nodes to attend to their internal ancestors to enhance structural consistency. Extensive experiments demonstrate that our approach effectively generates multi-scale Gaussian representations with controllable levels of detail, visual fidelity, and a manageable time consumption budget.", "date": "2026-04-01", "categories": []}
{"id": "2503.12052", "title": "A Text-to-3D Framework for Joint Generation of CG-Ready Humans and Compatible Garments", "abstract": "Creating detailed 3D human avatars with fitted garments traditionally requires specialized expertise and labor-intensive workflows. While recent advances in generative AI have enabled text-to-3D human and clothing synthesis, existing methods fall short in offering accessible, integrated pipelines for generating CG-ready 3D avatars with physically compatible outfits; here we use the term CG-ready for models following a technical aesthetic common in computer graphics (CG) and adopt standard CG polygonal meshes and strands representations (rather than neural representations like NeRF and 3DGS) that can be directly integrated into conventional CG pipelines and support downstream tasks such as physical simulation. To bridge this gap, we introduce Tailor, an integrated text-to-3D framework that generates high-fidelity, customizable 3D avatars dressed in simulation-ready garments. Tailor consists of three stages. (1) Seman tic Parsing: we employ a large language model to interpret textual descriptions and translate them into parameterized human avatars and semantically matched garment templates. (2) Geometry-Aware Garment Generation: we propose topology-preserving deformation with novel geometric losses to generate body-aligned garments under text control. (3) Consistent Texture Synthesis: we propose a novel multi-view diffusion process optimized for garment texturing, which enforces view consistency, preserves photorealistic details, and optionally supports symmetric texture generation common in garments. Through comprehensive quantitative and qualitative evaluations, we demonstrate that Tailor outperforms state-of-the-art methods in fidelity, usability, and diversity. Our code will be released for academic use. Project page: https://human-tailor.github.io", "date": "2025-03-15", "categories": []}
{"id": "2603.01142", "title": "ArtLLM: Generating Articulated Assets via 3D LLM", "abstract": "Creating interactive digital environments for gaming, robotics, and simulation relies on articulated 3D objects whose functionality emerges from their part geometry and kinematic structure. However, existing approaches remain fundamentally limited: optimization-based reconstruction methods require slow, per-object joint fitting and typically handle only simple, single-joint objects, while retrieval-based methods assemble parts from a fixed library, leading to repetitive geometry and poor generalization. To address these challenges, we introduce ArtLLM, a novel framework for generating high-quality articulated assets directly from complete 3D meshes. At its core is a 3D multimodal large language model trained on a large-scale articulation dataset curated from both existing articulation datasets and procedurally generated objects. Unlike prior work, ArtLLM autoregressively predicts a variable number of parts and joints, inferring their kinematic structure in a unified manner from the object's point cloud. This articulation-aware layout then conditions a 3D generative model to synthesize high-fidelity part geometries. Experiments on the PartNet-Mobility dataset show that ArtLLM significantly outperforms state-of-the-art methods in both part layout accuracy and joint prediction, while generalizing robustly to real-world objects. Finally, we demonstrate its utility in constructing digital twins, highlighting its potential for scalable robot learning.", "date": "2026-03-01", "categories": []}
{"id": "2603.18782", "title": "Points-to-3D: Structure-Aware 3D Generation with Point Cloud Priors", "abstract": "Recent progress in 3D generation has been driven largely by models conditioned on images or text, while readily available 3D priors are still underused. In many real-world scenarios, the visible-region point cloud are easy to obtain from active sensors such as LiDAR or from feed-forward predictors like VGGT, offering explicit geometric constraints that current methods fail to exploit. In this work, we introduce Points-to-3D, a diffusion-based framework that leverages point cloud priors for geometry-controllable 3D asset and scene generation. Built on a latent 3D diffusion model TRELLIS, Points-to-3D first replaces pure-noise sparse structure latent initialization with a point cloud priors tailored input formulation.A structure inpainting network, trained within the TRELLIS framework on task-specific data designed to learn global structural inpainting, is then used for inference with a staged sampling strategy (structural inpainting followed by boundary refinement), completing the global geometry while preserving the visible regions of the input priors. In practice, Points-to-3D can take either accurate point-cloud priors or VGGT-estimated point clouds from single images as input. Experiments on both objects and scene scenarios consistently demonstrate superior performance over state-of-the-art baselines in terms of rendering quality and geometric fidelity, highlighting the effectiveness of explicitly embedding point-cloud priors for achieving more accurate and structurally controllable 3D generation. Project page: https://jiatongxia.github.io/points2-3D/", "date": "2026-03-19", "categories": []}
{"id": "2603.29387", "title": "Extend3D: Town-Scale 3D Generation", "abstract": "In this paper, we propose Extend3D, a training-free pipeline for 3D scene generation from a single image, built upon an object-centric 3D generative model. To overcome the limitations of fixed-size latent spaces in object-centric models for representing wide scenes, we extend the latent space in the $x$ and $y$ directions. Then, by dividing the extended latent space into overlapping patches, we apply the object-centric 3D generative model to each patch and couple them at each time step. Since patch-wise 3D generation with image conditioning requires strict spatial alignment between image and latent patches, we initialize the scene using a point cloud prior from a monocular depth estimator and iteratively refine occluded regions through SDEdit. We discovered that treating the incompleteness of 3D structure as noise during 3D refinement enables 3D completion via a concept, which we term under-noising. Furthermore, to address the sub-optimality of object-centric models for sub-scene generation, we optimize the extended latent during denoising, ensuring that the denoising trajectories remain consistent with the sub-scene dynamics. To this end, we introduce 3D-aware optimization objectives for improved geometric structure and texture fidelity. We demonstrate that our method yields better results than prior methods, as evidenced by human preference and quantitative experiments.", "date": "2026-03-31", "categories": []}
{"id": "2603.29592", "title": "Bioinspired123D: Generative 3D Modeling System for Bioinspired Structures", "abstract": "Generative AI has made rapid progress in text, image, and video synthesis, yet text-to-3D modeling for scientific design remains particularly challenging due to limited controllability and high computational cost. Most existing 3D generative methods rely on meshes, voxels, or point clouds which can be costly to train and difficult to control. We introduce Bioinspired123D, a lightweight and modular code-as-geometry pipeline that generates fabricable 3D structures directly through parametric programs rather than dense visual representations. At the core of Bioinspired123D is Bioinspired3D, a compact language model finetuned to translate natural language design cues into Blender Python scripts encoding smooth, biologically inspired geometries. We curate a domain-specific dataset of over 4,000 bioinspired and geometric design scripts spanning helical, cellular, and tubular motifs with parametric variability. The dataset is expanded and validated through an automated LLM-driven, Blender-based quality control pipeline. Bioinspired3D is then embedded in a graph-based agentic framework that integrates multimodal retrieval-augmented generation and a vision-language model critic to iteratively evaluate, critique, and repair generated scripts. We evaluate performance on a new benchmark for 3D geometry script generation and show that Bioinspired123D demonstrates a near fourfold improvement over its non-finetuned base model, while also outperforming substantially larger state-of-the-art language models despite using far fewer parameters and compute. By prioritizing code-as-geometry representations, Bioinspired123D enables compute-efficient, controllable, and interpretable text-to-3D generation, lowering barriers to AI driven scientific discovery in materials and structural design.", "date": "2026-02-11", "categories": []}
{"id": "2601.16771", "title": "AutoRegressive Generation with B-rep Holistic Token Sequence Representation", "abstract": "Previous representation and generation approaches for the B-rep relied on graph-based representations that disentangle geometric and topological features through decoupled computational pipelines, thereby precluding the application of sequence-based generative frameworks, such as transformer architectures that have demonstrated remarkable performance. In this paper, we propose BrepARG, the first attempt to encode B-rep's geometry and topology into a holistic token sequence representation, enabling sequence-based B-rep generation with an autoregressive architecture. Specifically, BrepARG encodes B-rep into 3 types of tokens: geometry and position tokens representing geometric features, and face index tokens representing topology. Then the holistic token sequence is constructed hierarchically, starting with constructing the geometry blocks (i.e., faces and edges) using the above tokens, followed by geometry block sequencing. Finally, we assemble the holistic sequence representation for the entire B-rep. We also construct a transformer-based autoregressive model that learns the distribution over holistic token sequences via next-token prediction, using a multi-layer decoder-only architecture with causal masking. Experiments demonstrate that BrepARG achieves state-of-the-art (SOTA) performance. BrepARG validates the feasibility of representing B-rep as holistic token sequences, opening new directions for B-rep generation.", "date": "2026-01-23", "categories": []}
{"id": "2512.09435", "title": "UniPart: Part-Level 3D Generation with Unified 3D Geom-Seg Latents", "abstract": "Part-level 3D generation is essential for applications requiring decomposable and structured 3D synthesis. However, existing methods either rely on implicit part segmentation with limited granularity control or depend on strong external segmenters trained on large annotated datasets. In this work, we observe that part awareness emerges naturally during whole-object geometry learning and propose Geom-Seg VecSet, a unified geometry-segmentation latent representation that jointly encodes object geometry and part-level structure. Building on this representation, we introduce UniPart, a two-stage latent diffusion framework for image-guided part-level 3D generation. The first stage performs joint geometry generation and latent part segmentation, while the second stage conditions part-level diffusion on both whole-object and part-specific latents. A dual-space generation scheme further enhances geometric fidelity by predicting part latents in both global and canonical spaces. Extensive experiments demonstrate that UniPart achieves superior segmentation controllability and part-level geometric quality compared with existing approaches.", "date": "2025-12-10", "categories": []}
{"id": "2603.22782", "title": "Know3D: Prompting 3D Generation with Knowledge from Vision-Language Models", "abstract": "Recent advances in 3D generation have improved the fidelity and geometric details of synthesized 3D assets. However, due to the inherent ambiguity of single-view observations and the lack of robust global structural priors caused by limited 3D training data, the unseen regions generated by existing models are often stochastic and difficult to control, which may sometimes fail to align with user intentions or produce implausible geometries. In this paper, we propose Know3D, a novel framework that incorporates rich knowledge from multimodal large language models into 3D generative processes via latent hidden-state injection, enabling language-controllable generation of the back-view for 3D assets. We utilize a VLM-diffusion-based model, where the VLM is responsible for semantic understanding and guidance. The diffusion model acts as a bridge that transfers semantic knowledge from the VLM to the 3D generation model. In this way, we successfully bridge the gap between abstract textual instructions and the geometric reconstruction of unobserved regions, transforming the traditionally stochastic back-view hallucination into a semantically controllable process, demonstrating a promising direction for future 3D generation models.", "date": "2026-03-24", "categories": []}
{"id": "2512.00422", "title": "PhysGen: Physically Grounded 3D Shape Generation for Industrial Design", "abstract": "Existing generative models for 3D shapes can synthesize high-fidelity and visually plausible shapes. For certain classes of shapes that have undergone an engineering design process, the realism of the shape is tightly coupled with the underlying physical properties, e.g., aerodynamic efficiency for automobiles. Since existing methods lack knowledge of such physics, they are unable to use this knowledge to enhance the realism of shape generation. Motivated by this, we propose a unified physics-based 3D shape generation pipeline, with a focus on industrial design applications. Specifically, we introduce a new flow matching model with explicit physical guidance, consisting of an alternating update process. We iteratively perform a velocity-based update and a physics-based refinement, progressively adjusting the latent code to align with the desired 3D shapes and physical properties. We further strengthen physical validity by incorporating a physics-aware regularization term into the velocity-based update step. To support such physics-guided updates, we build a shape-and-physics variational autoencoder (SP-VAE) that jointly encodes shape and physics information into a unified latent space. The experiments on three benchmarks show that this synergistic formulation improves shape realism beyond mere visual plausibility. Our code and model weights are available at https://github.com/kasvii/PhysGen.", "date": "2025-11-29", "categories": []}
{"id": "2603.21295", "title": "Text-Image Conditioned 3D Generation", "abstract": "High-quality 3D assets are essential for VR/AR, industrial design, and entertainment, motivating growing interest in generative models that create 3D content from user prompts. Most existing 3D generators, however, rely on a single conditioning modality: image-conditioned models achieve high visual fidelity by exploiting pixel-aligned cues but suffer from viewpoint bias when the input view is limited or ambiguous, while text-conditioned models provide broad semantic guidance yet lack low-level visual detail. This limits how users can express intent and raises a natural question: can these two modalities be combined for more flexible and faithful 3D generation? Our diagnostic study shows that even simple late fusion of text- and image-conditioned predictions outperforms single-modality models, revealing strong cross-modal complementarity. We therefore formalize Text-Image Conditioned 3D Generation, which requires joint reasoning over a visual exemplar and a textual specification. To address this task, we introduce TIGON, a minimalist dual-branch baseline with separate image- and text-conditioned backbones and lightweight cross-modal fusion. Extensive experiments show that text-image conditioning consistently improves over single-modality methods, highlighting complementary vision-language guidance as a promising direction for future 3D generation research. Project page: https://jumpat.github.io/tigon-page", "date": "2026-03-22", "categories": []}
{"id": "2603.21557", "title": "From Part to Whole: 3D Generative World Model with an Adaptive Structural Hierarchy", "abstract": "Single-image 3D generation lies at the core of vision-to-graphics models in the real world. However, it remains a fundamental challenge to achieve reliable generalization across diverse semantic categories and highly variable structural complexity under sparse supervision. Existing approaches typically model objects in a monolithic manner or rely on a fixed number of parts, including recent part-aware models such as PartCrafter, which still require a labor-intensive user-specified part count. Such designs easily lead to overfitting, fragmented or missing structural components, and limited compositional generalization when encountering novel object layouts. To this end, this paper rethinks single-image 3D generation as learning an adaptive part-whole hierarchy in the flexible 3D latent space. We present a novel part-to-whole 3D generative world model that autonomously discovers latent structural slots by inferring soft and compositional masks directly from image tokens. Specifically, an adaptive slot-gating mechanism dynamically determines the slot-wise activation probabilities and smoothly consolidates redundant slots within different objects, ensuring that the emergent structure remains compact yet expressive across categories. Each distilled slot is then aligned to a learnable, class-agnostic prototype bank, enabling powerful cross-category shape sharing and denoising through universal geometric prototypes in the real world. Furthermore, a lightweight 3D denoiser is introduced to reconstruct geometry and appearance via unified diffusion objectives. Experiments show consistent gains in cross-category transfer and part-count extrapolation, and ablations confirm complementary benefits of the prototype bank for shape-prior sharing as well as slot-gating for structural adaptation.", "date": "2026-03-23", "categories": []}
{"id": "2506.09814", "title": "DreamCS: Geometry-Aware Text-to-3D Generation with Unpaired 3D Reward Supervision", "abstract": "While text-to-3D generation has attracted growing interest, existing methods often struggle to produce 3D assets that align well with human preferences. Current preference alignment techniques for 3D content typically rely on hardly-collected preference-paired multi-view 2D images to train 2D reward models, when then guide 3D generation -- leading to geometric artifacts due to their inherent 2D bias. To address these limitations, we construct 3D-MeshPref, the first large-scale unpaired 3D preference dataset, featuring diverse 3D meshes annotated by a large language model and refined by human evaluators. We then develop RewardCS, the first reward model trained directly on unpaired 3D-MeshPref data using a novel Cauchy-Schwarz divergence objective, enabling effective learning of human-aligned 3D geometric preferences without requiring paired comparisons. Building on this, we propose DreamCS, a unified framework that integrates RewardCS into text-to-3D pipelines -- enhancing both implicit and explicit 3D generation with human preference feedback. Extensive experiments show DreamCS outperforms prior methods, producing 3D assets that are both geometrically faithful and human-preferred. Code and models will be released publicly.", "date": "2025-06-11", "categories": []}
{"id": "2509.22592", "title": "OT-MeanFlow3D: Bridging Optimal Transport and Meanflow for Efficient 3D Point Cloud Generation", "abstract": "Flow-matching models have recently emerged as a powerful framework for continuous generative modeling, including 3D point cloud synthesis. However, their deployment is limited by the need for multiple sequential sampling steps at inference time. MeanFlow enables single-step generation and significantly accelerates inference, but often struggles to approximate the trajectories of the original multi-step flow, leading to degraded sample quality. In this work, we propose an Optimal Transport-enhanced MeanFlow framework (OT-MF3D) for efficient and accurate 3D point cloud generation and completion. By incorporating optimal transport-based sampling, our method better preserves the geometric and distributional structure of the underlying multi-step flow while retaining single-step inference. Experiments on ShapeNet show improved generation and completion quality compared to recent baselines, while reducing training and inference costs relative to conventional diffusion and flow-based models.", "date": "2025-09-26", "categories": []}
{"id": "2603.19216", "title": "DreamPartGen: Semantically Grounded Part-Level 3D Generation via Collaborative Latent Denoising", "abstract": "Understanding and generating 3D objects as compositions of meaningful parts is fundamental to human perception and reasoning. However, most text-to-3D methods overlook the semantic and functional structure of parts. While recent part-aware approaches introduce decomposition, they remain largely geometry-focused, lacking semantic grounding and failing to model how parts align with textual descriptions or their inter-part relations. We propose DreamPartGen, a framework for semantically grounded, part-aware text-to-3D generation. DreamPartGen introduces Duplex Part Latents (DPLs) that jointly model each part's geometry and appearance, and Relational Semantic Latents (RSLs) that capture inter-part dependencies derived from language. A synchronized co-denoising process enforces mutual geometric and semantic consistency, enabling coherent, interpretable, and text-aligned 3D synthesis. Across multiple benchmarks, DreamPartGen delivers state-of-the-art performance in geometric fidelity and text-shape alignment.", "date": "2026-03-19", "categories": []}
{"id": "2603.17995", "title": "LoST: Level of Semantics Tokenization for 3D Shapes", "abstract": "Tokenization is a fundamental technique in the generative modeling of various modalities. In particular, it plays a critical role in autoregressive (AR) models, which have recently emerged as a compelling option for 3D generation. However, optimal tokenization of 3D shapes remains an open question. State-of-the-art (SOTA) methods primarily rely on geometric level-of-detail (LoD) hierarchies, originally designed for rendering and compression. These spatial hierarchies are often token-inefficient and lack semantic coherence for AR modeling. We propose Level-of-Semantics Tokenization (LoST), which orders tokens by semantic salience, such that early prefixes decode into complete, plausible shapes that possess principal semantics, while subsequent tokens refine instance-specific geometric and semantic details. To train LoST, we introduce Relational Inter-Distance Alignment (RIDA), a novel 3D semantic alignment loss that aligns the relational structure of the 3D shape latent space with that of the semantic DINO feature space. Experiments show that LoST achieves SOTA reconstruction, surpassing previous LoD-based 3D shape tokenizers by large margins on both geometric and semantic reconstruction metrics. Moreover, LoST achieves efficient, high-quality AR 3D generation and enables downstream tasks like semantic retrieval, while using only 0.1%-10% of the tokens needed by prior AR models.", "date": "2026-03-18", "categories": []}
{"id": "2506.23205", "title": "BridgeShape: Latent Diffusion Schr\\\"odinger Bridge for 3D Shape Completion", "abstract": "Existing diffusion-based 3D shape completion methods typically use a conditional paradigm, injecting incomplete shape information into the denoising network via deep feature interactions (e.g., concatenation, cross-attention) to guide sampling toward complete shapes, often represented by voxel-based distance functions. However, these approaches fail to explicitly model the optimal global transport path, leading to suboptimal completions. Moreover, performing diffusion directly in voxel space imposes resolution constraints, limiting the generation of fine-grained geometric details. To address these challenges, we propose BridgeShape, a novel framework for 3D shape completion via latent diffusion Schr\\\"odinger bridge. The key innovations lie in two aspects: (i) BridgeShape formulates shape completion as an optimal transport problem, explicitly modeling the transition between incomplete and complete shapes to ensure a globally coherent transformation. (ii) We introduce a Depth-Enhanced Vector Quantized Variational Autoencoder (VQ-VAE) to encode 3D shapes into a compact latent space, leveraging self-projected multi-view depth information enriched with strong DINOv2 features to enhance geometric structural perception. By operating in a compact yet structurally informative latent space, BridgeShape effectively mitigates resolution constraints and enables more efficient and high-fidelity 3D shape completion. BridgeShape achieves state-of-the-art performance on large-scale 3D shape completion benchmarks, demonstrating superior fidelity at higher resolutions and for unseen object classes.", "date": "2025-06-29", "categories": []}
{"id": "2512.04761", "title": "Order Matters: 3D Shape Generation from Sequential VR Sketches", "abstract": "VR sketching lets users explore and iterate on ideas directly in 3D, offering a faster and more intuitive alternative to conventional CAD tools. However, existing sketch-to-shape models ignore the temporal ordering of strokes, discarding crucial cues about structure and design intent. We introduce VRSketch2Shape, the first framework and multi-category dataset for generating 3D shapes from sequential VR sketches. Our contributions are threefold: (i) an automated pipeline that generates sequential VR sketches from arbitrary shapes, (ii) a dataset of over 20k synthetic and 900 hand-drawn sketch-shape pairs across four categories, and (iii) an order-aware sketch encoder coupled with a diffusion-based 3D generator. Our approach yields higher geometric fidelity than prior work, generalizes effectively from synthetic to real sketches with minimal supervision, and performs well even on partial sketches. All data and models will be released open-source at https://chenyizi086.github.io/VRSketch2Shape_website.", "date": "2025-12-04", "categories": []}
{"id": "2603.16085", "title": "Interact3D: Compositional 3D Generation of Interactive Objects", "abstract": "Recent breakthroughs in 3D generation have enabled the synthesis of high-fidelity individual assets. However, generating 3D compositional objects from single images--particularly under occlusions--remains challenging. Existing methods often degrade geometric details in hidden regions and fail to preserve the underlying object-object spatial relationships (OOR). We present a novel framework Interact3D designed to generate physically plausible interacting 3D compositional objects. Our approach first leverages advanced generative priors to curate high-quality individual assets with a unified 3D guidance scene. To physically compose these assets, we then introduce a robust two-stage composition pipeline. Based on the 3D guidance scene, the primary object is anchored through precise global-to-local geometric alignment (registration), while subsequent geometries are integrated using a differentiable Signed Distance Field (SDF)-based optimization that explicitly penalizes geometry intersections. To reduce challenging collisions, we further deploy a closed-loop, agentic refinement strategy. A Vision-Language Model (VLM) autonomously analyzes multi-view renderings of the composed scene, formulates targeted corrective prompts, and guides an image editing module to iteratively self-correct the generation pipeline. Extensive experiments demonstrate that Interact3D successfully produces promising collsion-aware compositions with improved geometric fidelity and consistent spatial relationships.", "date": "2026-03-17", "categories": []}
{"id": "2511.21945", "title": "GENA3D: Generative Amodal 3D Modeling by Bridging 2D Priors and 3D Coherence", "abstract": "Generating complete 3D objects under partial occlusions (i.e., amodal scenarios) is a practically important yet challenging problem, as large portions of object geometry are unobserved in real-world scenarios. Existing approaches either operate directly in 3D, which ensures geometric consistency but often lacks generative expressiveness, or rely on 2D amodal completion, which provides strong appearance priors but does not guarantee reliable 3D structure. This raises a key question: how can we achieve both generative plausibility and geometric coherence in amodal 3D modeling? To answer this question, we introduce GENA3D (GENarative Amodal 3D), a framework that integrates learned 2D generative priors with explicit 3D geometric reasoning within a conditional 3D generation paradigm. The 2D priors enable the model to plausibly infer diverse occluded content, while the 3D representation enforces multi-view consistency and spatial validity. Our design incorporates a novel View-Wise Cross-Attention for multi-view alignment and a Stereo-Conditioned Cross-Attention to anchor generative predictions in 3D relationships. By combining generative imagination with structural constraints, GENA3D generates complete and coherent 3D objects from limited observations without sacrificing geometric fidelity. Experiments demonstrate that our method outperforms existing approaches in both synthetic and real-world amodal scenarios, highlighting the effectiveness of bridging 2D priors and 3D coherence in generating plausible and geometrically consistent 3D structures in complex environments.", "date": "2025-11-26", "categories": []}
{"id": "2603.14021", "title": "EI-Part: Explode for Completion and Implode for Refinement", "abstract": "Part-level 3D generation is crucial for various downstream applications, including gaming, film production, and industrial design. However, decomposing a 3D shape into geometrically plausible and meaningful components remains a significant challenge. Previous part-based generation methods often struggle to produce well-constructed parts, exhibiting poor structural coherence, geometric implausibility, inaccuracy, or inefficiency.\n  To address these challenges, we introduce EI-Part, a novel framework specifically designed to generate high-quality 3D shapes with components, characterized by strong structural coherence, geometric plausibility, geometric fidelity, and generation efficiency. We propose utilizing distinct representations at different stages: an Explode state for part completion and an Implode state for geometry refinement. This strategy fully leverages spatial resolution, enabling flexible part completion and fine geometric detail generation. To maintain structural coherence between parts, a self-attention mechanism is incorporated in both exploded and imploded states, facilitating effective information perception and feature fusion among components during generation.\n  Extensive experiments on multiple benchmarks demonstrate that EI-Part efficiently produces semantically meaningful and structurally coherent parts with fine-grained geometric details, achieving state-of-the-art performance in part-level 3D generation.\n  Project page: https://cvhadessun.github.io/EI-Part/", "date": "2026-03-14", "categories": []}
{"id": "2603.14152", "title": "SK-Adapter: Skeleton-Based Structural Control for Native 3D Generation", "abstract": "Native 3D generative models have achieved remarkable fidelity and speed, yet they suffer from a critical limitation: inability to prescribe precise structural articulations, where precise structural control within the native 3D space remains underexplored. This paper proposes SK-Adapter, a simple and yet highly efficient and effective framework that unlocks precise skeletal manipulation for native 3D generation. Moving beyond text or image prompts, which can be ambiguous for precise structure, we treat the 3D skeleton as a first-class control signal. SK-Adapter is a lightweight structural adapter network that encodes joint coordinates and topology into learnable tokens, which are injected into the frozen 3D generation backbone via cross-attention. This smart design allows the model to not only effectively \"attend\" to specific 3D structural constraints but also preserve its original generative priors. To bridge the data gap, we contribute Objaverse-TMS dataset, a large-scale dataset of 24k text-mesh-skeleton pairs. Extensive experiments confirm that our method achieves robust structural control while preserving the geometry and texture quality of the foundation model, significantly outperforming existing baselines. Furthermore, we extend this capability to local 3D editing, enabling the region specific editing of existing assets with skeletal guidance, which is unattainable by previous methods. Project Page: https://sk-adapter.github.io/", "date": "2026-03-14", "categories": []}
{"id": "2512.05343", "title": "SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling", "abstract": "Generative methods for 3D assets have recently achieved remarkable progress, yet providing intuitive and precise control over the object geometry remains a key challenge. Existing approaches predominantly rely on text or image prompts, which often fall short in geometric specificity: language can be ambiguous, and images are difficult to manipulate. In this work, we introduce SpaceControl, a training-free test-time method for explicit spatial control of 3D asset generation. Our approach accepts a wide range of geometric inputs, from coarse primitives to detailed meshes, and integrates seamlessly with modern generative models without requiring any additional training. A control parameter lets users trade off between geometric fidelity and output realism. Extensive quantitative evaluation and user studies demonstrate that SpaceControl outperforms both training-based and optimization-based baselines in geometric faithfulness while preserving high visual quality. Finally, we present an interactive interface for real-time superquadric editing and direct 3D asset generation, enabling seamless use in creative workflows. Project page: https://spacecontrol3d.github.io/.", "date": "2025-12-05", "categories": []}
{"id": "2603.07504", "title": "High-Fidelity Medical Shape Generation via Skeletal Latent Diffusion", "abstract": "Anatomy shape modeling is a fundamental problem in medical data analysis. However, the geometric complexity and topological variability of anatomical structures pose significant challenges to accurate anatomical shape generation. In this work, we propose a skeletal latent diffusion framework that explicitly incorporates structural priors for efficient and high-fidelity medical shape generation. We introduce a shape auto-encoder in which the encoder captures global geometric information through a differentiable skeletonization module and aggregates local surface features into shape latents, while the decoder predicts the corresponding implicit fields over sparsely sampled coordinates. New shapes are generated via a latent-space diffusion model, followed by neural implicit decoding and mesh extraction. To address the limited availability of medical shape data, we construct a large-scale dataset, \\textit{MedSDF}, comprising surface point clouds and corresponding signed distance fields across multiple anatomical categories. Extensive experiments on MedSDF and vessel datasets demonstrate that the proposed method achieves superior reconstruction and generation quality while maintaining a higher computational efficiency compared with existing approaches. Code is available at: https://github.com/wlsdzyzl/meshage.", "date": "2026-03-08", "categories": []}
{"id": "2512.08535", "title": "Photo3D: Advancing Photorealistic 3D Generation through Structure-Aligned Detail Enhancement", "abstract": "Although recent 3D-native generators have made great progress in synthesizing reliable geometry, they still fall short in achieving realistic appearances. A key obstacle lies in the lack of diverse and high-quality real-world 3D assets with rich texture details, since capturing such data is intrinsically difficult due to the diverse scales of scenes, non-rigid motions of objects, and the limited precision of 3D scanners. We introduce Photo3D, a framework for advancing photorealistic 3D generation, which is driven by the image data generated by the GPT-4o-Image model. Considering that the generated images can distort 3D structures due to their lack of multi-view consistency, we design a structure-aligned multi-view synthesis pipeline and construct a detail-enhanced multi-view dataset paired with 3D geometry. Building on it, we present a realistic detail enhancement scheme that leverages perceptual feature adaptation and semantic structure matching to enforce appearance consistency with realistic details while preserving the structural consistency with the 3D-native geometry. Our scheme is general to different 3D-native generators, and we present dedicated training strategies to facilitate the optimization of geometry-texture coupled and decoupled 3D-native generation paradigms. Experiments demonstrate that Photo3D generalizes well across diverse 3D-native generation paradigms and achieves state-of-the-art photorealistic 3D generation performance.", "date": "2025-12-09", "categories": []}
{"id": "2603.05845", "title": "Cog2Gen3D: Sculpturing 3D Semantic-Geometric Cognition for 3D Generation", "abstract": "Generative models have achieved success in producing semantically plausible 2D images, but it remains challenging in 3D generation due to the absence of spatial geometry constraints. Typically, existing methods utilize geometric features as conditions to enhance spatial awareness. However, these methods can only model relative relationships and are prone to scale inconsistency of absolute geometry. Thus, we argue that semantic information and absolute geometry empower 3D cognition, thereby enabling controllable 3D generation for the physical world. In this work, we propose Cog2Gen3D, a 3D cognition-guided diffusion framework for 3D generation. Our model is guided by three key designs: 1) Cognitive Feature Embeddings. We encode different modalities into semantic and geometric representations and further extract logical representations. 2) 3D Latent Cognition Graph. We structure different representations into dual-stream semantic-geometric graphs and fuse them via common-based cross-attention to obtain a 3D cognition graph. 3) Cognition-Guided Latent Diffusion. We leverage the fused 3D cognition graph as the condition to guide the latent diffusion process for 3D Gaussian generation. Under this unified framework, the 3D cognition graph ensures the physical plausibility and structural rationality of 3D generation. Moreover, we construct a validation subset based on the Marble World Labs. Extensive experiments demonstrate that our Cog2Gen3D significantly outperforms existing methods in both semantic fidelity and geometric plausibility.", "date": "2026-03-06", "categories": []}
{"id": "2603.06357", "title": "LATO: 3D Mesh Flow Matching with Structured TOpology Preserving LAtents", "abstract": "In this paper, we introduce LATO, a novel topology-preserving latent representation that enables scalable, flow matching-based synthesis of explicit 3D meshes. LATO represents a mesh as a Vertex Displacement Field (VDF) anchored on surface, incorporating a sparse voxel Variational Autoencoder (VAE) to compress this explicit signal into a structured, topology-aware voxel latent. To decapsulate the mesh, the VAE decoder progressively subdivides and prunes latent voxels to instantiate precise vertex locations. In the end, a dedicated connection head queries the voxel latent to predict edge connectivity between vertex pairs directly, allowing mesh topology to be recovered without isosurface extraction or heuristic meshing. For generative modeling, LATO adopts a two-stage flow matching process, first synthesizing the structure voxels and subsequently refining the voxel-wise topology features. Compared to prior isosurface/triangle-based diffusion models and autoregressive generation approaches, LATO generates meshes with complex geometry, well-formed topology while being highly efficient in inference.", "date": "2026-03-06", "categories": []}
{"id": "2510.13454", "title": "Text-to-3D by Stitching a Multi-view Reconstruction Network to a Video Generator", "abstract": "The rapid progress of large, pretrained models for both visual content generation and 3D reconstruction opens up new possibilities for text-to-3D generation. Intuitively, one could obtain a formidable 3D scene generator if one were able to combine the power of a modern latent text-to-video model as \"generator\" with the geometric abilities of a recent (feedforward) 3D reconstruction system as \"decoder\". We introduce VIST3A, a general framework that does just that, addressing two main challenges. First, the two components must be joined in a way that preserves the rich knowledge encoded in their weights. We revisit model stitching, i.e., we identify the layer in the 3D decoder that best matches the latent representation produced by the text-to-video generator and stitch the two parts together. That operation requires only a small dataset and no labels. Second, the text-to-video generator must be aligned with the stitched 3D decoder, to ensure that the generated latents are decodable into consistent, perceptually convincing 3D scene geometry. To that end, we adapt direct reward finetuning, a popular technique for human preference alignment. We evaluate the proposed VIST3A approach with different video generators and 3D reconstruction models. All tested pairings markedly improve over prior text-to-3D models that output Gaussian splats. Moreover, by choosing a suitable 3D base model, VIST3A also enables high-quality text-to-pointmap generation.", "date": "2025-10-15", "categories": []}
{"id": "2601.00204", "title": "MorphAny3D: Unleashing the Power of Structured Latent in 3D Morphing", "abstract": "3D morphing remains challenging due to the difficulty of generating semantically consistent and temporally smooth deformations, especially across categories. We present MorphAny3D, a training-free framework that leverages Structured Latent (SLAT) representations for high-quality 3D morphing. Our key insight is that intelligently blending source and target SLAT features within the attention mechanisms of 3D generators naturally produces plausible morphing sequences. To this end, we introduce Morphing Cross-Attention (MCA), which fuses source and target information for structural coherence, and Temporal-Fused Self-Attention (TFSA), which enhances temporal consistency by incorporating features from preceding frames. An orientation correction strategy further mitigates the pose ambiguity within the morphing steps. Extensive experiments show that our method generates state-of-the-art morphing sequences, even for challenging cross-category cases. MorphAny3D further supports advanced applications such as decoupled morphing and 3D style transfer, and can be generalized to other SLAT-based generative models. Project page: https://xiaokunsun.github.io/MorphAny3D.github.io/.", "date": "2026-01-01", "categories": []}
{"id": "2603.03602", "title": "DM-CFO: A Diffusion Model for Compositional 3D Tooth Generation with Collision-Free Optimization", "abstract": "The automatic design of a 3D tooth model plays a crucial role in dental digitization. However, current approaches face challenges in compositional 3D tooth generation because both the layouts and shapes of missing teeth need to be optimized.In addition, collision conflicts are often omitted in 3D Gaussian-based compositional 3D generation, where objects may intersect with each other due to the absence of explicit geometric information on the object surfaces. Motivated by graph generation through diffusion models and collision detection using 3D Gaussians, we propose an approach named DM-CFO for compositional tooth generation, where the layout of missing teeth is progressively restored during the denoising phase under both text and graph constraints. Then, the Gaussian parameters of each layout-guided tooth and the entire jaw are alternately updated using score distillation sampling (SDS). Furthermore, a regularization term based on the distances between the 3D Gaussians of neighboring teeth and the anchor tooth is introduced to penalize tooth intersections. Experimental results on three tooth-design datasets demonstrate that our approach significantly improves the multiview consistency and realism of the generated teeth compared with existing methods. Project page: https://amateurc.github.io/CF-3DTeeth/.", "date": "2026-03-04", "categories": []}
{"id": "2603.04307", "title": "Dual Diffusion Models for Multi-modal Guided 3D Avatar Generation", "abstract": "Generating high-fidelity 3D avatars from text or image prompts is highly sought after in virtual reality and human-computer interaction. However, existing text-driven methods often rely on iterative Score Distillation Sampling (SDS) or CLIP optimization, which struggle with fine-grained semantic control and suffer from excessively slow inference. Meanwhile, image-driven approaches are severely bottlenecked by the scarcity and high acquisition cost of high-quality 3D facial scans, limiting model generalization. To address these challenges, we first construct a novel, large-scale dataset comprising over 100,000 pairs across four modalities: fine-grained textual descriptions, in-the-wild face images, high-quality light-normalized texture UV maps, and 3D geometric shapes. Leveraging this comprehensive dataset, we propose PromptAvatar, a framework featuring dual diffusion models. Specifically, it integrates a Texture Diffusion Model (TDM) that supports flexible multi-condition guidance from text and/or image prompts, alongside a Geometry Diffusion Model (GDM) guided by text prompts. By learning the direct mapping from multi-modal prompts to 3D representations, PromptAvatar eliminates the need for time-consuming iterative optimization, successfully generating high-fidelity, shading-free 3D avatars in under 10 seconds. Extensive quantitative and qualitative experiments demonstrate that our method significantly outperforms existing state-of-the-art approaches in generation quality, fine-grained detail alignment, and computational efficiency.", "date": "2026-03-04", "categories": []}
{"id": "2603.01515", "title": "FACE: A Face-based Autoregressive Representation for High-Fidelity and Efficient Mesh Generation", "abstract": "Autoregressive models for 3D mesh generation suffer from a fundamental limitation: they flatten meshes into long vertex-coordinate sequences. This results in prohibitive computational costs, hindering the efficient synthesis of high-fidelity geometry. We argue this bottleneck stems from operating at the wrong semantic level. We introduce FACE, a novel Autoregressive Autoencoder (ARAE) framework that reconceptualizes the task by generating meshes at the face level. Our one-face-one-token strategy treats each triangle face, the fundamental building block of a mesh, as a single, unified token. This simple yet powerful design reduces the sequence length by a factor of nine, leading to an unprecedented compression ratio of 0.11, halving the previous state-of-the-art. This dramatic efficiency gain does not compromise quality; by pairing our face-level decoder with a powerful VecSet encoder, FACE achieves state-of-the-art reconstruction quality on standard benchmarks. The versatility of the learned latent space is further demonstrated by training a latent diffusion model that achieves high-fidelity, single-image-to-mesh generation. FACE provides a simple, scalable, and powerful paradigm that lowers the barrier to high-quality structured 3D content creation.", "date": "2026-03-02", "categories": []}
{"id": "2507.02477", "title": "Topology-Preserved Auto-regressive Mesh Generation in the Manner of Weaving Silk", "abstract": "Existing auto-regressive mesh generation approaches suffer from ineffective topology preservation, which is crucial for practical applications. This limitation stems from previous mesh tokenization methods treating meshes as simple collections of equivalent triangles, lacking awareness of the overall topological structure during generation. To address this issue, we propose a novel mesh tokenization algorithm that provides a canonical topological framework through vertex layering and ordering, ensuring critical geometric properties including manifoldness, watertightness, face normal consistency, and part awareness in the generated meshes. Measured by Compression Ratio and Bits-per-face, we also achieved state-of-the-art compression efficiency. Furthermore, we introduce an online non-manifold data processing algorithm and a training resampling strategy to expand the scale of trainable dataset and avoid costly manual data curation. Experimental results demonstrate the effectiveness of our approach, showcasing not only intricate mesh generation but also significantly improved geometric integrity.", "date": "2025-07-03", "categories": []}
{"id": "2509.21420", "title": "QuadGPT: Native Quadrilateral Mesh Generation with Autoregressive Models", "abstract": "The generation of quadrilateral-dominant meshes is a cornerstone of professional 3D content creation. However, existing generative models generate quad meshes by first generating triangle meshes and then merging triangles into quadrilaterals with some specific rules, which typically produces quad meshes with poor topology. In this paper, we introduce QuadGPT, the first autoregressive framework for generating quadrilateral meshes in an end-to-end manner. QuadGPT formulates this as a sequence prediction paradigm, distinguished by two key innovations: a unified tokenization method to handle mixed topologies of triangles and quadrilaterals, and a specialized Reinforcement Learning fine-tuning method tDPO for better generation quality. Extensive experiments demonstrate that QuadGPT significantly surpasses previous triangle-to-quad conversion pipelines in both geometric accuracy and topological quality. Our work establishes a new benchmark for native quad-mesh generation and showcases the power of combining large-scale autoregressive models with topology-aware RL refinement for creating structured 3D assets.", "date": "2025-09-25", "categories": []}
{"id": "2603.00526", "title": "Mesh-Pro: Asynchronous Advantage-guided Ranking Preference Optimization for Artist-style Quadrilateral Mesh Generation", "abstract": "Reinforcement learning (RL) has demonstrated remarkable success in text and image generation, yet its potential in 3D generation remains largely unexplored. Existing attempts typically rely on offline direct preference optimization (DPO) method, which suffers from low training efficiency and limited generalization. In this work, we aim to enhance both the training efficiency and generation quality of RL in 3D mesh generation. Specifically, (1) we design the first asynchronous online RL framework tailored for 3D mesh generation post-training efficiency improvement, which is 3.75$\\times$ faster than synchronous RL. (2) We propose Advantage-guided Ranking Preference Optimization (ARPO), a novel RL algorithm that achieves a better trade-off between training efficiency and generalization than current RL algorithms designed for 3D mesh generation, such as DPO and group relative policy optimization (GRPO). (3) Based on asynchronous ARPO, we propose Mesh-Pro, which additionally introduces a novel diagonal-aware mixed triangular-quadrilateral tokenization for mesh representation and a ray-based reward for geometric integrity. Mesh-Pro achieves state-of-the-art performance on artistic and dense meshes.", "date": "2026-02-28", "categories": []}
{"id": "2603.01594", "title": "Preference Score Distillation: Leveraging 2D Rewards to Align Text-to-3D Generation with Human Preference", "abstract": "Human preference alignment presents a critical yet underexplored challenge for diffusion models in text-to-3D generation. Existing solutions typically require task-specific fine-tuning, posing significant hurdles in data-scarce 3D domains. To address this, we propose Preference Score Distillation (PSD), an optimization-based framework that leverages pretrained 2D reward models for human-aligned text-to-3D synthesis without 3D training data. Our key insight stems from the incompatibility of pixel-level gradients: due to the absence of noisy samples during reward model training, direct application of 2D reward gradients disturbs the denoising process. Noticing that similar issue occurs in the naive classifier guidance in conditioned diffusion models, we fundamentally rethink preference alignment as a classifier-free guidance (CFG)-style mechanism through our implicit reward model. Furthermore, recognizing that frozen pretrained diffusion models constrain performance, we introduce an adaptive strategy to co-optimize preference scores and negative text embeddings. By incorporating CFG during optimization, online refinement of negative text embeddings dynamically enhances alignment. To our knowledge, we are the first to bridge human preference alignment with CFG theory under score distillation framework. Experiments demonstrate the superiority of PSD in aesthetic metrics, seamless integration with diverse pipelines, and strong extensibility.", "date": "2026-03-02", "categories": []}
{"id": "2408.09126", "title": "DreamBarbie: Text to Barbie-Style 3D Avatars", "abstract": "To integrate digital humans into everyday life, there is a strong demand for generating high-quality, fine-grained disentangled 3D avatars that support expressive animation and simulation capabilities, ideally from low-cost textual inputs. Although text-driven 3D avatar generation has made significant progress by leveraging 2D generative priors, existing methods still struggle to fulfill all these requirements simultaneously. To address this challenge, we propose DreamBarbie, a novel text-driven framework for generating animatable 3D avatars with separable shoes, accessories, and simulation-ready garments, truly capturing the iconic ``Barbie doll'' aesthetic. The core of our framework lies in an expressive 3D representation combined with appropriate modeling constraints. Unlike prior methods, we use G-Shell to uniformly model watertight components (e.g., bodies, shoes) and non-watertight garments. By reformulating boundaries as Euclidean field intersections instead of manifold geodesics, we propose an SDF-based initialization and a hole regularization loss that together achieve a 100x speedup and stable open topology without image input. These disentangled 3D representations are then optimized by specialized expert diffusion models tailored to each domain, ensuring high-fidelity outputs. To mitigate geometric artifacts and texture conflicts when combining different expert models, we further propose several effective geometric losses and strategies. Extensive experiments demonstrate that DreamBarbie outperforms existing methods in both dressed human and outfit generation. Our framework further enables diverse applications, including apparel combination, editing, expressive animation, and physical simulation. Project page: https://xiaokunsun.github.io/DreamBarbie.github.io/.", "date": "2024-08-17", "categories": []}
{"id": "2602.20666", "title": "BoxSplitGen: A Generative Model for 3D Part Bounding Boxes in Varying Granularity", "abstract": "Human creativity follows a perceptual process, moving from abstract ideas to finer details during creation. While 3D generative models have advanced dramatically, models specifically designed to assist human imagination in 3D creation -- particularly for detailing abstractions from coarse to fine -- have not been explored. We propose a framework that enables intuitive and interactive 3D shape generation by iteratively splitting bounding boxes to refine the set of bounding boxes. The main technical components of our framework are two generative models: the box-splitting generative model and the box-to-shape generative model. The first model, named BoxSplitGen, generates a collection of 3D part bounding boxes with varying granularity by iteratively splitting coarse bounding boxes. It utilizes part bounding boxes created through agglomerative merging and learns the reverse of the merging process -- the splitting sequences. The model consists of two main components: the first learns the categorical distribution of the box to be split, and the second learns the distribution of the two new boxes, given the set of boxes and the indication of which box to split. The second model, the box-to-shape generative model, is trained by leveraging the 3D shape priors learned by an existing 3D diffusion model while adapting the model to incorporate bounding box conditioning. In our experiments, we demonstrate that the box-splitting generative model outperforms token prediction models and the inpainting approach with an unconditional diffusion model. Also, we show that our box-to-shape model, based on a state-of-the-art 3D diffusion model, provides superior results compared to a previous model.", "date": "2026-02-24", "categories": []}
{"id": "2602.17033", "title": "PartRAG: Retrieval-Augmented Part-Level 3D Generation and Editing", "abstract": "Single-image 3D generation with part-level structure remains challenging: learned priors struggle to cover the long tail of part geometries and maintain multi-view consistency, and existing systems provide limited support for precise, localized edits. We present PartRAG, a retrieval-augmented framework that integrates an external part database with a diffusion transformer to couple generation with an editable representation. To overcome the first challenge, we introduce a Hierarchical Contrastive Retrieval module that aligns dense image patches with 3D part latents at both part and object granularity, retrieving from a curated bank of 1,236 part-annotated assets to inject diverse, physically plausible exemplars into denoising. To overcome the second challenge, we add a masked, part-level editor that operates in a shared canonical space, enabling swaps, attribute refinements, and compositional updates without regenerating the whole object while preserving non-target parts and multi-view consistency. PartRAG achieves competitive results on Objaverse, ShapeNet, and ABO-reducing Chamfer Distance from 0.1726 to 0.1528 and raising F-Score from 0.7472 to 0.844 on Objaverse-with inference of 38s and interactive edits in 5-8s. Qualitatively, PartRAG produces sharper part boundaries, better thin-structure fidelity, and robust behavior on articulated objects. Code: https://github.com/AIGeeksGroup/PartRAG. Website: https://aigeeksgroup.github.io/PartRAG.", "date": "2026-02-19", "categories": []}
{"id": "2507.14841", "title": "Towards Geometric and Textural Consistency 3D Scene Generation via Single Image-guided Model Generation and Layout Optimization", "abstract": "In recent years, 3D generation has made great strides in both academia and industry. However, generating 3D scenes from a single RGB image remains a significant challenge, as current approaches often struggle to ensure both object generation quality and scene coherence in multi-object scenarios. To overcome these limitations, we propose a novel three-stage framework for 3D scene generation with explicit geometric representations and high-quality textural details via single image-guided model generation and spatial layout optimization. Our method begins with an image instance segmentation and inpainting phase, which recovers missing details of occluded objects in the input images, thereby achieving complete generation of foreground 3D assets. Subsequently, our approach captures the spatial geometry of reference image by constructing pseudo-stereo viewpoint for camera parameter estimation and scene depth inference, while employing a model selection strategy to ensure optimal alignment between the 3D assets generated in the previous step and the input. Finally, through model parameterization and minimization of the Chamfer distance between point clouds in 3D and 2D space, our approach optimizes layout parameters to produce an explicit 3D scene representation that maintains precise alignment with input guidance image. Extensive experiments on multi-object scene image sets have demonstrated that our approach not only outperforms state-of-the-art methods in terms of geometric accuracy and texture fidelity of individual generated 3D models, but also has significant advantages in scene layout synthesis.", "date": "2025-07-20", "categories": []}
{"id": "2602.09713", "title": "Stroke3D: Lifting 2D strokes into rigged 3D model via latent diffusion models", "abstract": "Rigged 3D assets are fundamental to 3D deformation and animation. However, existing 3D generation methods face challenges in generating animatable geometry, while rigging techniques lack fine-grained structural control over skeleton creation. To address these limitations, we introduce Stroke3D, a novel framework that directly generates rigged meshes from user inputs: 2D drawn strokes and a descriptive text prompt. Our approach pioneers a two-stage pipeline that separates the generation into: 1) Controllable Skeleton Generation, we employ the Skeletal Graph VAE (Sk-VAE) to encode the skeleton's graph structure into a latent space, where the Skeletal Graph DiT (Sk-DiT) generates a skeletal embedding. The generation process is conditioned on both the text for semantics and the 2D strokes for explicit structural control, with the VAE's decoder reconstructing the final high-quality 3D skeleton; and 2) Enhanced Mesh Synthesis via TextuRig and SKA-DPO, where we then synthesize a textured mesh conditioned on the generated skeleton. For this stage, we first enhance an existing skeleton-to-mesh model by augmenting its training data with TextuRig: a dataset of textured and rigged meshes with captions, curated from Objaverse-XL. Additionally, we employ a preference optimization strategy, SKA-DPO, guided by a skeleton-mesh alignment score, to further improve geometric fidelity. Together, our framework enables a more intuitive workflow for creating ready to animate 3D content. To the best of our knowledge, our work is the first to generate rigged 3D meshes conditioned on user-drawn 2D strokes. Extensive experiments demonstrate that Stroke3D produces plausible skeletons and high-quality meshes.", "date": "2026-02-10", "categories": []}
{"id": "2602.13818", "title": "VAR-3D: View-aware Auto-Regressive Model for Text-to-3D Generation via a 3D Tokenizer", "abstract": "Recent advances in auto-regressive transformers have achieved remarkable success in generative modeling. However, text-to-3D generation remains challenging, primarily due to bottlenecks in learning discrete 3D representations. Specifically, existing approaches often suffer from information loss during encoding, causing representational distortion before the quantization process. This effect is further amplified by vector quantization, ultimately degrading the geometric coherence of text-conditioned 3D shapes. Moreover, the conventional two-stage training paradigm induces an objective mismatch between reconstruction and text-conditioned auto-regressive generation. To address these issues, we propose View-aware Auto-Regressive 3D (VAR-3D), which intergrates a view-aware 3D Vector Quantized-Variational AutoEncoder (VQ-VAE) to convert the complex geometric structure of 3D models into discrete tokens. Additionally, we introduce a rendering-supervised training strategy that couples discrete token prediction with visual reconstruction, encouraging the generative process to better preserve visual fidelity and structural consistency relative to the input text. Experiments demonstrate that VAR-3D significantly outperforms existing methods in both generation quality and text-3D alignment.", "date": "2026-02-14", "categories": []}
{"id": "2602.14965", "title": "PAct: Part-Decomposed Single-View Articulated Object Generation", "abstract": "Articulated objects are central to interactive 3D applications, including embodied AI, robotics, and VR/AR, where functional part decomposition and kinematic motion are essential. Yet producing high-fidelity articulated assets remains difficult to scale because it requires reliable part decomposition and kinematic rigging. Existing approaches largely fall into two paradigms: optimization-based reconstruction or distillation, which can be accurate but often takes tens of minutes to hours per instance, and inference-time methods that rely on template or part retrieval, producing plausible results that may not match the specific structure and appearance in the input observation. We introduce a part-centric generative framework for articulated object creation that synthesizes part geometry, composition, and articulation under explicit part-aware conditioning. Our representation models an object as a set of movable parts, each encoded by latent tokens augmented with part identity and articulation cues. Conditioned on a single image, the model generates articulated 3D assets that preserve instance-level correspondence while maintaining valid part structure and motion. The resulting approach avoids per-instance optimization, enables fast feed-forward inference, and supports controllable assembly and articulation, which are important for embodied interaction. Experiments on common articulated categories (e.g., drawers and doors) show improved input consistency, part accuracy, and articulation plausibility over optimization-based and retrieval-driven baselines, while substantially reducing inference time.", "date": "2026-02-16", "categories": []}
{"id": "2602.12100", "title": "AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer", "abstract": "The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content~(UGC). In this work, we introduce AssetFormer, an autoregressive Transformer-based model designed to generate modular 3D assets from textual descriptions. Our pilot study leverages real-world modular assets collected from online platforms. AssetFormer tackles the challenge of creating assets composed of primitives that adhere to constrained design parameters for various applications. By innovatively adapting module sequencing and decoding techniques inspired by language models, our approach enhances asset generation quality through autoregressive modeling. Initial results indicate the effectiveness of AssetFormer in streamlining asset creation for professional development and UGC scenarios. This work presents a flexible framework extendable to various types of modular 3D assets, contributing to the broader field of 3D content generation. The code is available at https://github.com/Advocate99/AssetFormer.", "date": "2026-02-12", "categories": []}
{"id": "2512.12768", "title": "CoRe3D: Collaborative Reasoning as a Foundation for 3D Intelligence", "abstract": "Recent advances in large multimodal models suggest that explicit reasoning mechanisms play a critical role in improving model reliability, interpretability, and cross-modal alignment. While such reasoning-centric approaches have been proven effective in language and vision tasks, their extension to 3D remains underdeveloped. CoRe3D introduces a unified 3D understanding and generation reasoning framework that jointly operates over semantic and spatial abstractions, enabling high-level intent inferred from language to directly guide low-level 3D content formation. Central to this design is a spatially grounded reasoning representation that decomposes 3D latent space into localized regions, allowing the model to reason over geometry in a compositional and procedural manner. By tightly coupling semantic chain-of-thought inference with structured spatial reasoning, CoRe3D produces 3D outputs that exhibit strong local consistency and faithful alignment with linguistic descriptions.", "date": "2025-12-14", "categories": []}
{"id": "2602.07198", "title": "Condition Matters in Full-head 3D GANs", "abstract": "Conditioning is crucial for stable training of full-head 3D GANs. Without any conditioning signal, the model suffers from severe mode collapse, making it impractical to training. However, a series of previous full-head 3D GANs conventionally choose the view angle as the conditioning input, which leads to a bias in the learned 3D full-head space along the conditional view direction. This is evident in the significant differences in generation quality and diversity between the conditional view and non-conditional views of the generated 3D heads, resulting in global incoherence across different head regions. In this work, we propose to use view-invariant semantic feature as the conditioning input, thereby decoupling the generative capability of 3D heads from the viewing direction. To construct a view-invariant semantic condition for each training image, we create a novel synthesized head image dataset. We leverage FLUX.1 Kontext to extend existing high-quality frontal face datasets to a wide range of view angles. The image clip feature extracted from the frontal view is then used as a shared semantic condition across all views in the extended images, ensuring semantic alignment while eliminating directional bias. This also allows supervision from different views of the same subject to be consolidated under a shared semantic condition, which accelerates training and enhances the global coherence of the generated 3D heads. Moreover, as GANs often experience slower improvements in diversity once the generator learns a few modes that successfully fool the discriminator, our semantic conditioning encourages the generator to follow the true semantic distribution, thereby promoting continuous learning and diverse generation. Extensive experiments on full-head synthesis and single-view GAN inversion demonstrate that our method achieves significantly higher fidelity, diversity, and generalizability.", "date": "2026-02-06", "categories": []}
{"id": "2602.08046", "title": "Enhanced Mixture 3D CGAN for Completion and Generation of 3D Objects", "abstract": "The generation and completion of 3D objects represent a transformative challenge in computer vision. Generative Adversarial Networks (GANs) have recently demonstrated strong potential in synthesizing realistic visual data. However, they often struggle to capture complex and diverse data distributions, particularly in scenarios involving incomplete inputs or significant missing regions. These challenges arise mainly from the high computational requirements and the difficulty of modeling heterogeneous and structurally intricate data, which restrict their applicability in real-world settings. Mixture of Experts (MoE) models have emerged as a promising solution to these limitations. By dynamically selecting and activating the most relevant expert sub-networks for a given input, MoEs improve both performance and efficiency. In this paper, we investigate the integration of Deep 3D Convolutional GANs (CGANs) with a MoE framework to generate high-quality 3D models and reconstruct incomplete or damaged objects. The proposed architecture incorporates multiple generators, each specialized to capture distinct modalities within the dataset. Furthermore, an auxiliary loss-free dynamic capacity constraint (DCC) mechanism is introduced to guide the selection of categorical generators, ensuring a balance between specialization, training stability, and computational efficiency, which is critical for 3D voxel processing. We evaluated the model's ability to generate and complete shapes with missing regions of varying sizes and compared its performance with state-of-the-art approaches. Both quantitative and qualitative results confirm the effectiveness of the proposed MoE-DCGAN in handling complex 3D data.", "date": "2026-02-08", "categories": []}
{"id": "2602.08198", "title": "PEGAsus: 3D Personalization of Geometry and Appearance", "abstract": "We present PEGAsus, a new framework capable of generating Personalized 3D shapes by learning shape concepts at both Geometry and Appearance levels. First, we formulate 3D shape personalization as extracting reusable, category-agnostic geometric and appearance attributes from reference shapes, and composing these attributes with text to generate novel shapes. Second, we design a progressive optimization strategy to learn shape concepts at both the geometry and appearance levels, decoupling the shape concept learning process. Third, we extend our approach to region-wise concept learning, enabling flexible concept extraction, with context-aware and context-free losses. Extensive experimental results show that PEGAsus is able to effectively extract attributes from a wide range of reference shapes and then flexibly compose these concepts with text to synthesize new shapes. This enables fine-grained control over shape generation and supports the creation of diverse, personalized results, even in challenging cross-category scenarios. Both quantitative and qualitative experiments demonstrate that our approach outperforms existing state-of-the-art solutions.", "date": "2026-02-09", "categories": []}
{"id": "2408.04567", "title": "Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches", "abstract": "3D Content Generation is at the heart of many computer graphics applications, including video gaming, film-making, virtual and augmented reality, etc. This paper proposes a novel deep-learning based approach for automatically generating interactive and playable 3D game scenes, all from the user's casual prompts such as a hand-drawn sketch. Sketch-based input offers a natural, and convenient way to convey the user's design intention in the content creation process. To circumvent the data-deficient challenge in learning (i.e. the lack of large training data of 3D scenes), our method leverages a pre-trained 2D denoising diffusion model to generate a 2D image of the scene as the conceptual guidance. In this process, we adopt the isometric projection mode to factor out unknown camera poses while obtaining the scene layout. From the generated isometric image, we use a pre-trained image understanding method to segment the image into meaningful parts, such as off-ground objects, trees, and buildings, and extract the 2D scene layout. These segments and layouts are subsequently fed into a procedural content generation (PCG) engine, such as a 3D video game engine like Unity or Unreal, to create the 3D scene. The resulting 3D scene can be seamlessly integrated into a game development environment and is readily playable. Extensive tests demonstrate that our method can efficiently generate high-quality and interactive 3D game scenes with layouts that closely follow the user's intention.", "date": "2024-08-08", "categories": []}
{"id": "2504.15259", "title": "Bringing Diversity from Diffusion Models to Semantic-Guided Face Asset Generation", "abstract": "Digital modeling and reconstruction of human faces serve various applications. However, its availability is often hindered by the requirements of data capturing devices, manual labor, and suitable actors. This situation restricts the diversity, expressiveness, and control over the resulting models. This work aims to demonstrate that a semantically controllable generative network can provide enhanced control over the digital face modeling process. To enhance diversity beyond the limited human faces scanned in a controlled setting, we introduce a novel data generation pipeline that creates a high-quality 3D face database using a pre-trained diffusion model. Our proposed normalization module converts synthesized data from the diffusion model into high-quality scanned data. Using the 44,000 face models we obtained, we further developed an efficient GAN-based generator. This generator accepts semantic attributes as input, and generates geometry and albedo. It also allows continuous post-editing of attributes in the latent space. Our asset refinement component subsequently creates physically-based facial assets. We introduce a comprehensive system designed for creating and editing high-quality face assets. Our proposed model has undergone extensive experiment, comparison and evaluation. We also integrate everything into a web-based interactive tool. We aim to make this tool publicly available with the release of the paper.", "date": "2025-04-21", "categories": []}
{"id": "2601.21314", "title": "HiFi-Mesh: High-Fidelity Efficient 3D Mesh Generation via Compact Autoregressive Dependence", "abstract": "High-fidelity 3D meshes can be tokenized into one-dimension (1D) sequences and directly modeled using autoregressive approaches for faces and vertices. However, existing methods suffer from insufficient resource utilization, resulting in slow inference and the ability to handle only small-scale sequences, which severely constrains the expressible structural details. We introduce the Latent Autoregressive Network (LANE), which incorporates compact autoregressive dependencies in the generation process, achieving a $6\\times$ improvement in maximum generatable sequence length compared to existing methods. To further accelerate inference, we propose the Adaptive Computation Graph Reconfiguration (AdaGraph) strategy, which effectively overcomes the efficiency bottleneck of traditional serial inference through spatiotemporal decoupling in the generation process. Experimental validation demonstrates that LANE achieves superior performance across generation speed, structural detail, and geometric consistency, providing an effective solution for high-quality 3D mesh generation.", "date": "2026-01-29", "categories": []}
{"id": "2601.19785", "title": "GeoDiff3D: Self-Supervised 3D Scene Generation with Geometry-Constrained 2D Diffusion Guidance", "abstract": "3D scene generation is a core technology for gaming, film/VFX, and VR/AR. Growing demand for rapid iteration, high-fidelity detail, and accessible content creation has further increased interest in this area. Existing methods broadly follow two paradigms - indirect 2D-to-3D reconstruction and direct 3D generation - but both are limited by weak structural modeling and heavy reliance on large-scale ground-truth supervision, often producing structural artifacts, geometric inconsistencies, and degraded high-frequency details in complex scenes. We propose GeoDiff3D, an efficient self-supervised framework that uses coarse geometry as a structural anchor and a geometry-constrained 2D diffusion model to provide texture-rich reference images. Importantly, GeoDiff3D does not require strict multi-view consistency of the diffusion-generated references and remains robust to the resulting noisy, inconsistent guidance. We further introduce voxel-aligned 3D feature aggregation and dual self-supervision to maintain scene coherence and fine details while substantially reducing dependence on labeled data. GeoDiff3D also trains with low computational cost and enables fast, high-quality 3D scene generation. Extensive experiments on challenging scenes show improved generalization and generation quality over existing baselines, offering a practical solution for accessible and efficient 3D scene construction.", "date": "2026-01-27", "categories": []}
{"id": "2601.20425", "title": "Quartet of Diffusions: Structure-Aware Point Cloud Generation through Part and Symmetry Guidance", "abstract": "We introduce the Quartet of Diffusions, a structure-aware point cloud generation framework that explicitly models part composition and symmetry. Unlike prior methods that treat shape generation as a holistic process or only support part composition, our approach leverages four coordinated diffusion models to learn distributions of global shape latents, symmetries, semantic parts, and their spatial assembly. This structured pipeline ensures guaranteed symmetry, coherent part placement, and diverse, high-quality outputs. By disentangling the generative process into interpretable components, our method supports fine-grained control over shape attributes, enabling targeted manipulation of individual parts while preserving global consistency. A central global latent further reinforces structural coherence across assembled parts. Our experiments show that the Quartet achieves state-of-the-art performance. To our best knowledge, this is the first 3D point cloud generation framework that fully integrates and enforces both symmetry and part priors throughout the generative process.", "date": "2026-01-28", "categories": []}
{"id": "2601.17733", "title": "Flatten The Complex: Joint B-Rep Generation via Compositional $k$-Cell Particles", "abstract": "Boundary Representation (B-Rep) is the widely adopted standard\n  in Computer-Aided Design (CAD) and manufacturing. However, generative modeling of B-Reps remains a formidable challenge due to their inherent heterogeneity as geometric cell complexes, which entangles topology with geometry across cells of varying orders (i.e., $k$-cells such as vertices, edges, faces). Previous methods typically rely on cascaded sequences to handle this hierarchy, which fails to fully exploit the geometric relationships between cells, such as adjacency and sharing, limiting context awareness and error recovery. To fill this gap, we introduce a novel paradigm that reformulates B-Reps into sets of compositional $k$-cell particles. Our approach encodes each topological entity as a composition of particles, where adjacent cells share identical latents at their interfaces, thereby promoting geometric coupling along shared boundaries. By decoupling the rigid hierarchy, our representation unifies vertices, edges, and faces, enabling the joint generation of topology and geometry with global context awareness.\n  We synthesize these particle sets using a multi-modal flow matching framework to handle unconditional generation as well as precise conditional tasks, such as 3D reconstruction from single-view or point cloud. Furthermore, the explicit and localized nature of our representation naturally extends to downstream tasks like local in-painting and enables the direct synthesis of non-manifold structures (e.g., wireframes). Extensive experiments demonstrate that our method produces high-fidelity CAD models with superior validity and editability compared to state-of-the-art methods.", "date": "2026-01-25", "categories": []}
{"id": "2511.11074", "title": "Evaluating Latent Generative Paradigms for High-Fidelity 3D Shape Completion from a Single Depth Image", "abstract": "While generative models have seen significant adoption across a wide range of data modalities, including 3D data, a consensus on which model is best suited for which task has yet to be reached. Further, conditional information such as text and images to steer the generation process are frequently employed, whereas others, like partial 3D data, have not been thoroughly evaluated. In this work, we compare two of the most promising generative models--Denoising Diffusion Probabilistic Models and Autoregressive Causal Transformers--which we adapt for the tasks of generative shape modeling and completion. We conduct a thorough quantitative evaluation and comparison of both tasks, including a baseline discriminative model and an extensive ablation study. Our results show that (1) the diffusion model with continuous latents outperforms both the discriminative model and the autoregressive approach and delivers state-of-the-art performance on multi-modal shape completion from a single, noisy depth image under realistic conditions and (2) when compared on the same discrete latent space, the autoregressive model can match or exceed diffusion performance on these tasks.", "date": "2025-11-14", "categories": []}
{"id": "2601.12234", "title": "Proc3D: Procedural 3D Generation and Parametric Editing of 3D Shapes with Large Language Models", "abstract": "Generating 3D models has traditionally been a complex task requiring specialized expertise. While recent advances in generative AI have sought to automate this process, existing methods produce non-editable representation, such as meshes or point clouds, limiting their adaptability for iterative design. In this paper, we introduce Proc3D, a system designed to generate editable 3D models while enabling real-time modifications. At its core, Proc3D introduces procedural compact graph (PCG), a graph representation of 3D models, that encodes the algorithmic rules and structures necessary for generating the model. This representation exposes key parameters, allowing intuitive manual adjustments via sliders and checkboxes, as well as real-time, automated modifications through natural language prompts using Large Language Models (LLMs). We demonstrate Proc3D's capabilities using two generative approaches: GPT-4o with in-context learning (ICL) and a fine-tuned LLAMA-3 model. Experimental results show that Proc3D outperforms existing methods in editing efficiency, achieving more than 400x speedup over conventional approaches that require full regeneration for each modification. Additionally, Proc3D improves ULIP scores by 28%, a metric that evaluates the alignment between generated 3D models and text prompts. By enabling text-aligned 3D model generation along with precise, real-time parametric edits, Proc3D facilitates highly accurate text-based image editing applications.", "date": "2026-01-18", "categories": []}
{"id": "2601.13371", "title": "Spherical Geometry Diffusion: Generating High-quality 3D Face Geometry via Sphere-anchored Representations", "abstract": "A fundamental challenge in text-to-3D face generation is achieving high-quality geometry. The core difficulty lies in the arbitrary and intricate distribution of vertices in 3D space, making it challenging for existing models to establish clean connectivity and resulting in suboptimal geometry. To address this, our core insight is to simplify the underlying geometric structure by constraining the distribution onto a simple and regular manifold, a topological sphere. Building on this, we first propose the Spherical Geometry Representation, a novel face representation that anchors geometric signals to uniform spherical coordinates. This guarantees a regular point distribution, from which the mesh connectivity can be robustly reconstructed. Critically, this canonical sphere can be seamlessly unwrapped into a 2D map, creating a perfect synergy with powerful 2D generative models. We then introduce Spherical Geometry Diffusion, a conditional diffusion framework built upon this 2D map. It enables diverse and controllable generation by jointly modeling geometry and texture, where the geometry explicitly conditions the texture synthesis process. Our method's effectiveness is demonstrated through its success in a wide range of tasks: text-to-3D generation, face reconstruction, and text-based 3D editing. Extensive experiments show that our approach substantially outperforms existing methods in geometric quality, textual fidelity, and inference efficiency.", "date": "2026-01-19", "categories": []}
{"id": "2601.13445", "title": "BladeSDF : Unconditional and Conditional Generative Modeling of Representative Blade Geometries Using Signed Distance Functions", "abstract": "Generative AI has emerged as a transformative paradigm in engineering design, enabling automated synthesis and reconstruction of complex 3D geometries while preserving feasibility and performance relevance. This paper introduces a domain-specific implicit generative framework for turbine blade geometry using DeepSDF, addressing critical gaps in performance-aware modeling and manufacturable design generation. The proposed method leverages a continuous signed distance function (SDF) representation to reconstruct and generate smooth, watertight geometries with quantified accuracy. It establishes an interpretable, near-Gaussian latent space that aligns with blade-relevant parameters, such as taper and chord ratios, enabling controlled exploration and unconditional synthesis through interpolation and Gaussian sampling. In addition, a compact neural network maps engineering descriptors, such as maximum directional strains, to latent codes, facilitating the generation of performance-informed geometry. The framework achieves high reconstruction fidelity, with surface distance errors concentrated within $1\\%$ of the maximum blade dimension, and demonstrates robust generalization to unseen designs. By integrating constraints, objectives, and performance metrics, this approach advances beyond traditional 2D-guided or unconstrained 3D pipelines, offering a practical and interpretable solution for data-driven turbine blade modeling and concept generation.", "date": "2026-01-19", "categories": []}
{"id": "2601.11514", "title": "ShapeR: Robust Conditional 3D Shape Generation from Casual Captures", "abstract": "Recent advances in 3D shape generation have achieved impressive results, but most existing methods rely on clean, unoccluded, and well-segmented inputs. Such conditions are rarely met in real-world scenarios. We present ShapeR, a novel approach for conditional 3D object shape generation from casually captured sequences. Given an image sequence, we leverage off-the-shelf visual-inertial SLAM, 3D detection algorithms, and vision-language models to extract, for each object, a set of sparse SLAM points, posed multi-view images, and machine-generated captions. A rectified flow transformer trained to effectively condition on these modalities then generates high-fidelity metric 3D shapes. To ensure robustness to the challenges of casually captured data, we employ a range of techniques including on-the-fly compositional augmentations, a curriculum training scheme spanning object- and scene-level datasets, and strategies to handle background clutter. Additionally, we introduce a new evaluation benchmark comprising 178 in-the-wild objects across 7 real-world scenes with geometry annotations. Experiments show that ShapeR significantly outperforms existing approaches in this challenging setting, achieving an improvement of 2.7x in Chamfer distance compared to state of the art.", "date": "2026-01-16", "categories": []}
{"id": "2508.19188", "title": "FastMesh: Efficient Artistic Mesh Generation via Component Decoupling", "abstract": "Recent mesh generation approaches typically tokenize triangle meshes into sequences of tokens and train autoregressive models to generate these tokens sequentially. Despite substantial progress, such token sequences inevitably reuse vertices multiple times to fully represent manifold meshes, as each vertex is shared by multiple faces. This redundancy leads to excessively long token sequences and inefficient generation processes. In this paper, we propose an efficient framework that generates artistic meshes by treating vertices and faces separately, significantly reducing redundancy. We employ an autoregressive model solely for vertex generation, decreasing the token count to approximately 23% of that required by the most compact existing tokenizer. Next, we leverage a bidirectional transformer to complete the mesh in a single step by capturing inter-vertex relationships and constructing the adjacency matrix that defines the mesh faces. To further improve the generation quality, we introduce a fidelity enhancer to refine vertex positioning into more natural arrangements and propose a post-processing framework to remove undesirable edge connections. Experimental results show that our method achieves more than 8x faster speed on mesh generation compared to state-of-the-art approaches, while producing higher mesh quality.", "date": "2025-08-26", "categories": []}
{"id": "2601.08015", "title": "Decoder Generates Manufacturable Structures: A Framework for 3D-Printable Object Synthesis", "abstract": "This paper presents a novel decoder-based approach for generating manufacturable 3D structures optimized for additive manufacturing. We introduce a deep learning framework that decodes latent representations into geometrically valid, printable objects while respecting manufacturing constraints such as overhang angles, wall thickness, and structural integrity. The methodology demonstrates that neural decoders can learn complex mapping functions from abstract representations to valid 3D geometries, producing parts with significantly improved manufacturability compared to naive generation approaches. We validate the approach on diverse object categories and demonstrate practical 3D printing of decoder-generated structures.", "date": "2026-01-07", "categories": []}
{"id": "2601.07660", "title": "StdGEN++: A Comprehensive System for Semantic-Decomposed 3D Character Generation", "abstract": "We present StdGEN++, a novel and comprehensive system for generating high-fidelity, semantically decomposed 3D characters from diverse inputs. Existing 3D generative methods often produce monolithic meshes that lack the structural flexibility required by industrial pipelines in gaming and animation. Addressing this gap, StdGEN++ is built upon a Dual-branch Semantic-aware Large Reconstruction Model (Dual-Branch S-LRM), which jointly reconstructs geometry, color, and per-component semantics in a feed-forward manner. To achieve production-level fidelity, we introduce a novel semantic surface extraction formalism compatible with hybrid implicit fields. This mechanism is accelerated by a coarse-to-fine proposal scheme, which significantly reduces memory footprint and enables high-resolution mesh generation. Furthermore, we propose a video-diffusion-based texture decomposition module that disentangles appearance into editable layers (e.g., separated iris and skin), resolving semantic confusion in facial regions. Experiments demonstrate that StdGEN++ achieves state-of-the-art performance, significantly outperforming existing methods in geometric accuracy and semantic disentanglement. Crucially, the resulting structural independence unlocks advanced downstream capabilities, including non-destructive editing, physics-compliant animation, and gaze tracking, making it a robust solution for automated character asset production.", "date": "2026-01-12", "categories": []}
{"id": "2411.19037", "title": "3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes", "abstract": "Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike diffusion models, AR models enable more efficient and controllable generation with faster inference times, making them especially suitable for data-intensive domains. Traditional 3D generative models using AR approaches often rely on ``next-token\" predictions at the voxel or point level. While effective for certain applications, these methods can be restrictive and computationally expensive when dealing with large-scale 3D data. To tackle these challenges, we introduce 3D-WAG, an AR model for 3D implicit distance fields that can perform unconditional shape generation, class-conditioned and also text-conditioned shape generation. Our key idea is to encode shapes as multi-scale wavelet token maps and use a Transformer to predict the ``next higher-resolution token map\" in an autoregressive manner. By redefining 3D AR generation task as ``next-scale\" prediction, we reduce the computational cost of generation compared to traditional ``next-token\" prediction models, while preserving essential geometric details of 3D shapes in a more structured and hierarchical manner. We evaluate 3D-WAG to showcase its benefit by quantitative and qualitative comparisons with state-of-the-art methods on widely used benchmarks. Our results show 3D-WAG achieves superior performance in key metrics like Coverage and MMD, generating high-fidelity 3D shapes that closely match the real data distribution.", "date": "2024-11-28", "categories": []}
{"id": "2601.05722", "title": "Rotate Your Character: Revisiting Video Diffusion Models for High-Quality 3D Character Generation", "abstract": "Generating high-quality 3D characters from single images remains a significant challenge in digital content creation, particularly due to complex body poses and self-occlusion. In this paper, we present RCM (Rotate your Character Model), an advanced image-to-video diffusion framework tailored for high-quality novel view synthesis (NVS) and 3D character generation. Compared to existing diffusion-based approaches, RCM offers several key advantages: (1) transferring characters with any complex poses into a canonical pose, enabling consistent novel view synthesis across the entire viewing orbit, (2) high-resolution orbital video generation at 1024x1024 resolution, (3) controllable observation positions given different initial camera poses, and (4) multi-view conditioning supporting up to 4 input images, accommodating diverse user scenarios. Extensive experiments demonstrate that RCM outperforms state-of-the-art methods in both novel view synthesis and 3D generation quality.", "date": "2026-01-09", "categories": []}
{"id": "2410.18987", "title": "Point Cloud Synthesis Using Inner Product Transforms", "abstract": "Point cloud synthesis, i.e. the generation of novel point clouds from an input distribution, remains a challenging task, for which numerous complex machine learning models have been devised. We develop a novel method that encodes geometrical-topological characteristics of point clouds using inner products, leading to a highly-efficient point cloud representation with provable expressivity properties. Integrated into deep learning models, our encoding exhibits high quality in typical tasks like reconstruction, generation, and interpolation, with inference times orders of magnitude faster than existing methods.", "date": "2024-10-09", "categories": []}
{"id": "2511.22425", "title": "Wukong's 72 Transformations: High-fidelity Textured 3D Morphing via Flow Models", "abstract": "We present WUKONG, a novel training-free framework for high-fidelity textured 3D morphing that takes a pair of source and target prompts (image or text) as input. Unlike conventional methods -- which rely on manual correspondence matching and deformation trajectory estimation (limiting generalization and requiring costly preprocessing) -- WUKONG leverages the generative prior of flow-based transformers to produce high-fidelity 3D transitions with rich texture details. To ensure smooth shape transitions, we exploit the inherent continuity of flow-based generative processes and formulate morphing as an optimal transport barycenter problem. We further introduce a sequential initialization strategy to prevent abrupt geometric distortions and preserve identity coherence. For faithful texture preservation, we propose a similarity-guided semantic consistency mechanism that selectively retains high-frequency details and enables precise control over blending dynamics. This empowers WUKONG to support both global texture transitions and identity-preserving texture morphing, catering to diverse generation needs. Extensive quantitative and qualitative evaluations demonstrate that WUKONG significantly outperforms state-of-the-art methods, achieving superior results across diverse geometry and texture variations.", "date": "2025-11-27", "categories": []}
{"id": "2412.19413", "title": "Multi-scale Latent Point Consistency Models for 3D Shape Generation", "abstract": "Consistency Models (CMs) have significantly accelerated the sampling process in diffusion models, yielding impressive results in synthesizing high-resolution images. To explore and extend these advancements to point-cloud-based 3D shape generation, we propose a novel Multi-scale Latent Point Consistency Model (MLPCM). Our MLPCM follows a latent diffusion framework and introduces hierarchical levels of latent representations, ranging from point-level to super-point levels, each corresponding to a different spatial resolution. We design a multi-scale latent integration module along with 3D spatial attention to effectively denoise the point-level latent representations conditioned on those from multiple super-point levels. Additionally, we propose a latent consistency model, learned through consistency distillation, that compresses the prior into a one-step generator. This significantly improves sampling efficiency while preserving the performance of the original teacher model. Extensive experiments on standard benchmarks ShapeNet and ShapeNet-Vol demonstrate that MLPCM achieves a 100x speedup in the generation process, while surpassing state-of-the-art diffusion models in terms of both shape quality and diversity.", "date": "2024-12-27", "categories": []}
{"id": "2512.23628", "title": "Memorization in 3D Shape Generation: An Empirical Study", "abstract": "Generative models are increasingly used in 3D vision to synthesize novel shapes, yet it remains unclear whether their generation relies on memorizing training shapes. Understanding their memorization could help prevent training data leakage and improve the diversity of generated results. In this paper, we design an evaluation framework to quantify memorization in 3D generative models and study the influence of different data and modeling designs on memorization. We first apply our framework to quantify memorization in existing methods. Next, through controlled experiments with a latent vector-set (Vecset) diffusion model, we find that, on the data side, memorization depends on data modality, and increases with data diversity and finer-grained conditioning; on the modeling side, it peaks at a moderate guidance scale and can be mitigated by longer Vecsets and simple rotation augmentation. Together, our framework and analysis provide an empirical understanding of memorization in 3D generative models and suggest simple yet effective strategies to reduce it without degrading generation quality. Our code is available at https://github.com/zlab-princeton/3d_mem.", "date": "2025-12-29", "categories": []}
{"id": "2512.21185", "title": "UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement", "abstract": "In this report, we introduce UltraShape 1.0, a scalable 3D diffusion framework for high-fidelity 3D geometry generation. The proposed approach adopts a two-stage generation pipeline: a coarse global structure is first synthesized and then refined to produce detailed, high-quality geometry. To support reliable 3D generation, we develop a comprehensive data processing pipeline that includes a novel watertight processing method and high-quality data filtering. This pipeline improves the geometric quality of publicly available 3D datasets by removing low-quality samples, filling holes, and thickening thin structures, while preserving fine-grained geometric details. To enable fine-grained geometry refinement, we decouple spatial localization from geometric detail synthesis in the diffusion process. We achieve this by performing voxel-based refinement at fixed spatial locations, where voxel queries derived from coarse geometry provide explicit positional anchors encoded via RoPE, allowing the diffusion model to focus on synthesizing local geometric details within a reduced, structured solution space. Our model is trained exclusively on publicly available 3D datasets, achieving strong geometric quality despite limited training resources. Extensive evaluations demonstrate that UltraShape 1.0 performs competitively with existing open-source methods in both data processing quality and geometry generation. All code and trained models will be released to support future research.", "date": "2025-12-24", "categories": []}
{"id": "2504.11734", "title": "Recent Advances in 3D Object and Scene Generation: A Survey", "abstract": "In recent years, the demand for 3D content has grown exponentially with the intelligent upgrade of interactive media, extended reality (XR), and Metaverse industries. In order to overcome the limitations of traditional manual modeling approaches, such as labor-intensive workflows and prolonged production cycles, revolutionary advances have been achieved through the convergence of novel 3D representation paradigms and artificial intelligence generative technologies. In this survey, we conduct a systematic review of the cutting-edge achievements in static 3D object and scene generation, as well as establish a comprehensive technical framework through systematic categorization. We start our analysis with mainstream 3D object representations. Subsequently, we delve into the technical pathways of 3D object generation based on four mainstream deep generative models: Variational Autoencoders, Generative Adversarial Networks, Autoregressive Models, and Diffusion Models. Regarding scene generation, we focus on three dominant paradigms: layout-guided generation, lifting based on 2D priors, and rule-driven modeling. Finally, we critically examine persistent challenges in 3D generation and propose potential research directions for future investigation. This survey aims to provide readers with a structured understanding of state-of-the-art 3D generation technologies while inspiring researchers to undertake more exploration in this domain.", "date": "2025-04-16", "categories": []}
{"id": "2505.04262", "title": "Bridging Geometry-Coherent Text-to-3D Generation with Multi-View Diffusion Priors and Gaussian Splatting", "abstract": "Score Distillation Sampling (SDS) leverages pretrained 2D diffusion models to advance text-to-3D generation but neglects multi-view correlations, being prone to geometric inconsistencies and multi-face artifacts in the generated 3D content. In this work, we propose Coupled Score Distillation (CSD), a framework that couples multi-view joint distribution priors to ensure geometrically consistent 3D generation while enabling the stable and direct optimization of 3D Gaussian Splatting. Specifically, by reformulating the optimization as a multi-view joint optimization problem, we derive an effective optimization rule that effectively couples multi-view priors to guide optimization across different viewpoints while preserving the diversity of generated 3D assets. Additionally, we propose a framework that directly optimizes 3D Gaussian Splatting (3D-GS) with random initialization to generate geometrically consistent 3D content. We further employ a deformable tetrahedral grid, initialized from 3D-GS and refined through CSD, to produce high-quality, refined meshes. Quantitative and qualitative experimental results demonstrate the efficiency and competitive quality of our approach.", "date": "2025-05-07", "categories": []}
{"id": "2506.22678", "title": "3D Shape Generation: A Survey", "abstract": "Recent advances in deep learning have significantly transformed the field of 3D shape generation, enabling the synthesis of complex, diverse, and semantically meaningful 3D objects. This survey provides a comprehensive overview of the current state-of-the-art in 3D shape generation, organizing the discussion around three core components: shape representations, generative modeling approaches, and evaluation protocols. We begin by categorizing 3D representations into explicit, implicit, and hybrid setups, highlighting their structural properties, advantages, and limitations. Next, we review a wide range of generation methods, focusing on feedforward architectures. We further summarize commonly used datasets and evaluation metrics that assess fidelity, diversity, and realism of generated shapes. Finally, we identify open challenges and outline future research directions that could drive progress in controllable, efficient, and high-quality 3D shape generation. This survey aims to serve as a valuable reference for researchers and practitioners seeking a structured and in-depth understanding of this rapidly evolving field.", "date": "2025-06-27", "categories": []}
{"id": "2403.12019", "title": "LN3DIFF++: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation", "abstract": "The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework called LN3Diff++ to address this gap and enable fast, high-quality, and generic conditional 3D generation. Our approach harnesses a 3D-aware architecture and variational autoencoder (VAE) to encode the input image into a structured, compact, and 3D latent space. The latent is decoded by a transformer-based decoder into a high-capacity 3D neural field. Through training a diffusion model on this 3D-aware latent space, our method achieves state-of-the-art performance on ShapeNet for 3D generation and demonstrates superior performance in monocular 3D reconstruction and conditional 3D generation across various datasets. Moreover, it surpasses existing 3D diffusion methods in terms of inference speed, requiring no per-instance optimization. Our proposed LN3Diff presents a significant advancement in 3D generative modeling and holds promise for various applications in 3D vision and graphics tasks.", "date": "2024-03-18", "categories": []}
{"id": "2512.13991", "title": "Repurposing 2D Diffusion Models for 3D Shape Completion", "abstract": "We present a framework that adapts 2D diffusion models for 3D shape completion from incomplete point clouds. While text-to-image diffusion models have achieved remarkable success with abundant 2D data, 3D diffusion models lag due to the scarcity of high-quality 3D datasets and a persistent modality gap between 3D inputs and 2D latent spaces. To overcome these limitations, we introduce the Shape Atlas, a compact 2D representation of 3D geometry that (1) enables full utilization of the generative power of pretrained 2D diffusion models, and (2) aligns the modalities between the conditional input and output spaces, allowing more effective conditioning. This unified 2D formulation facilitates learning from limited 3D data and produces high-quality, detail-preserving shape completions. We validate the effectiveness of our results on the PCN and ShapeNet-55 datasets. Additionally, we show the downstream application of creating artist-created meshes from our completed point clouds, further demonstrating the practicality of our method.", "date": "2025-12-16", "categories": []}
{"id": "2512.14692", "title": "Native and Compact Structured Latents for 3D Generation", "abstract": "Recent advancements in 3D generative modeling have significantly improved the generation realism, yet the field is still hampered by existing representations, which struggle to capture assets with complex topologies and detailed appearance. This paper present an approach for learning a structured latent representation from native 3D data to address this challenge. At its core is a new sparse voxel structure called O-Voxel, an omni-voxel representation that encodes both geometry and appearance. O-Voxel can robustly model arbitrary topology, including open, non-manifold, and fully-enclosed surfaces, while capturing comprehensive surface attributes beyond texture color, such as physically-based rendering parameters. Based on O-Voxel, we design a Sparse Compression VAE which provides a high spatial compression rate and a compact latent space. We train large-scale flow-matching models comprising 4B parameters for 3D generation using diverse public 3D asset datasets. Despite their scale, inference remains highly efficient. Meanwhile, the geometry and material quality of our generated assets far exceed those of existing models. We believe our approach offers a significant advancement in 3D generative modeling.", "date": "2025-12-16", "categories": []}
{"id": "2512.12395", "title": "ArtGen: Conditional Generative Modeling of Articulated Objects in Arbitrary Part-Level States", "abstract": "Generating articulated assets is crucial for robotics, digital twins, and embodied intelligence. Existing generative models often rely on single-view inputs representing closed states, resulting in ambiguous or unrealistic kinematic structures due to the entanglement between geometric shape and joint dynamics. To address these challenges, we introduce ArtGen, a conditional diffusion-based framework capable of generating articulated 3D objects with accurate geometry and coherent kinematics from single-view images or text descriptions at arbitrary part-level states. Specifically, ArtGen employs cross-state Monte Carlo sampling to explicitly enforce global kinematic consistency, reducing structural-motion entanglement. Additionally, we integrate a Chain-of-Thought reasoning module to infer robust structural priors, such as part semantics, joint types, and connectivity, guiding a sparse-expert Diffusion Transformer to specialize in diverse kinematic interactions. Furthermore, a compositional 3D-VAE latent prior enhanced with local-global attention effectively captures fine-grained geometry and global part-level relationships. Extensive experiments on the PartNet-Mobility benchmark demonstrate that ArtGen significantly outperforms state-of-the-art methods.", "date": "2025-12-13", "categories": []}
{"id": "2312.08591", "title": "Joint2Human: High-quality 3D Human Generation via Compact Spherical Embedding of 3D Joints", "abstract": "3D human generation is increasingly significant in various applications. However, the direct use of 2D generative methods in 3D generation often results in losing local details, while methods that reconstruct geometry from generated images struggle with global view consistency. In this work, we introduce Joint2Human, a novel method that leverages 2D diffusion models to generate detailed 3D human geometry directly, ensuring both global structure and local details. To achieve this, we employ the Fourier occupancy field (FOF) representation, enabling the direct generation of 3D shapes as preliminary results with 2D generative models. With the proposed high-frequency enhancer and the multi-view recarving strategy, our method can seamlessly integrate the details from different views into a uniform global shape. To better utilize the 3D human prior and enhance control over the generated geometry, we introduce a compact spherical embedding of 3D joints. This allows for an effective guidance of pose during the generation process. Additionally, our method can generate 3D humans guided by textual inputs. Our experimental results demonstrate the capability of our method to ensure global structure, local details, high resolution, and low computational cost simultaneously. More results and the code can be found on our project page at http://cic.tju.edu.cn/faculty/likun/projects/Joint2Human.", "date": "2023-12-14", "categories": []}
{"id": "2512.10949", "title": "Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation", "abstract": "Reinforcement learning (RL), earlier proven to be effective in large language and multi-modal models, has been successfully extended to enhance 2D image generation recently. However, applying RL to 3D generation remains largely unexplored due to the higher spatial complexity of 3D objects, which require globally consistent geometry and fine-grained local textures. This makes 3D generation significantly sensitive to reward designs and RL algorithms. To address these challenges, we conduct the first systematic study of RL for text-to-3D autoregressive generation across several dimensions. (1) Reward designs: We evaluate reward dimensions and model choices, showing that alignment with human preference is crucial, and that general multi-modal models provide robust signal for 3D attributes. (2) RL algorithms: We study GRPO variants, highlighting the effectiveness of token-level optimization, and further investigate the scaling of training data and iterations. (3) Text-to-3D Benchmarks: Since existing benchmarks fail to measure implicit reasoning abilities in 3D generation models, we introduce MME-3DR. (4) Advanced RL paradigms: Motivated by the natural hierarchy of 3D generation, we propose Hi-GRPO, which optimizes the global-to-local hierarchical 3D generation through dedicated reward ensembles. Based on these insights, we develop AR3D-R1, the first RL-enhanced text-to-3D model, expert from coarse shape to texture refinement. We hope this study provides insights into RL-driven reasoning for 3D generation. Code is released at https://github.com/Ivan-Tang-3D/3DGen-R1.", "date": "2025-12-11", "categories": []}
{"id": "2506.18331", "title": "End-to-End Fine-Tuning of 3D Texture Generation using Differentiable Rewards", "abstract": "While recent 3D generative models can produce high-quality texture images, they often fail to capture human preferences or meet task-specific requirements. Moreover, a core challenge in the 3D texture generation domain is that most existing approaches rely on repeated calls to 2D text-to-image generative models, which lack an inherent understanding of the 3D structure of the input 3D mesh object. To alleviate these issues, we propose an end-to-end differentiable, reinforcement-learning-free framework that embeds human feedback, expressed as differentiable reward functions, directly into the 3D texture synthesis pipeline. By back-propagating preference signals through both geometric and appearance modules of the proposed framework, our method generates textures that respect the 3D geometry structure and align with desired criteria. To demonstrate its versatility, we introduce three novel geometry-aware reward functions, which offer a more controllable and interpretable pathway for creating high-quality 3D content from natural language. By conducting qualitative, quantitative, and user-preference evaluations against state-of-the-art methods, we demonstrate that our proposed strategy consistently outperforms existing approaches. Our implementation code is publicly available at: https://github.com/AHHHZ975/Differentiable-Texture-Learning", "date": "2025-06-23", "categories": []}
{"id": "2512.07514", "title": "MeshRipple: Structured Autoregressive Generation of Artist-Meshes", "abstract": "Meshes serve as a primary representation for 3D assets. Autoregressive mesh generators serialize faces into sequences and train on truncated segments with sliding-window inference to cope with memory limits. However, this mismatch breaks long-range geometric dependencies, producing holes and fragmented components. To address this critical limitation, we introduce MeshRipple, which expands a mesh outward from an active generation frontier, akin to a ripple on a surface. MeshRipple rests on three key innovations: a frontier-aware BFS tokenization that aligns the generation order with surface topology; an expansive prediction strategy that maintains coherent, connected surface growth; and a sparse-attention global memory that provides an effectively unbounded receptive field to resolve long-range topological dependencies. This integrated design enables MeshRipple to generate meshes with high surface fidelity and topological completeness, outperforming strong recent baselines.", "date": "2025-12-08", "categories": []}
{"id": "2512.07628", "title": "MoCA: Mixture-of-Components Attention for Scalable Compositional 3D Generation", "abstract": "Compositionality is critical for 3D object and scene generation, but existing part-aware 3D generation methods suffer from poor scalability due to quadratic global attention costs when increasing the number of components. In this work, we present MoCA, a compositional 3D generative model with two key designs: (1) importance-based component routing that selects top-k relevant components for sparse global attention, and (2) unimportant components compression that preserve contextual priors of unselected components while reducing computational complexity of global attention. With these designs, MoCA enables efficient, fine-grained compositional 3D asset creation with scalable number of components. Extensive experiments show MoCA outperforms baselines on both compositional object and scene generation tasks. Project page: https://lizhiqi49.github.io/MoCA", "date": "2025-12-08", "categories": []}
{"id": "2512.03052", "title": "LATTICE: Democratize High-Fidelity 3D Generation at Scale", "abstract": "We present LATTICE, a new framework for high-fidelity 3D asset generation that bridges the quality and scalability gap between 3D and 2D generative models. While 2D image synthesis benefits from fixed spatial grids and well-established transformer architectures, 3D generation remains fundamentally more challenging due to the need to predict both spatial structure and detailed geometric surfaces from scratch. These challenges are exacerbated by the computational complexity of existing 3D representations and the lack of structured and scalable 3D asset encoding schemes. To address this, we propose VoxSet, a semi-structured representation that compresses 3D assets into a compact set of latent vectors anchored to a coarse voxel grid, enabling efficient and position-aware generation. VoxSet retains the simplicity and compression advantages of prior VecSet methods while introducing explicit structure into the latent space, allowing positional embeddings to guide generation and enabling strong token-level test-time scaling. Built upon this representation, LATTICE adopts a two-stage pipeline: first generating a sparse voxelized geometry anchor, then producing detailed geometry using a rectified flow transformer. Our method is simple at its core, but supports arbitrary resolution decoding, low-cost training, and flexible inference schemes, achieving state-of-the-art performance on various aspects, and offering a significant step toward scalable, high-quality 3D asset creation.", "date": "2025-11-24", "categories": []}
{"id": "2512.03450", "title": "KeyPointDiffuser: Unsupervised 3D Keypoint Learning via Latent Diffusion Models", "abstract": "Understanding and representing the structure of 3D objects in an unsupervised manner remains a core challenge in computer vision and graphics. Most existing unsupervised keypoint methods are not designed for unconditional generative settings, restricting their use in modern 3D generative pipelines; our formulation explicitly bridges this gap. We present an unsupervised framework for learning spatially structured 3D keypoints from point cloud data. These keypoints serve as a compact and interpretable representation that conditions an Elucidated Diffusion Model (EDM) to reconstruct the full shape. The learned keypoints exhibit repeatable spatial structure across object instances and support smooth interpolation in keypoint space, indicating that they capture geometric variation. Our method achieves strong performance across diverse object categories, yielding a 6 percentage-point improvement in keypoint consistency compared to prior approaches.", "date": "2025-12-03", "categories": []}
{"id": "2512.03566", "title": "GAOT: Generating Articulated Objects Through Text-Guided Diffusion Models", "abstract": "Articulated object generation has seen increasing advancements, yet existing models often lack the ability to be conditioned on text prompts. To address the significant gap between textual descriptions and 3D articulated object representations, we propose GAOT, a three-phase framework that generates articulated objects from text prompts, leveraging diffusion models and hypergraph learning in a three-step process. First, we fine-tune a point cloud generation model to produce a coarse representation of objects from text prompts. Given the inherent connection between articulated objects and graph structures, we design a hypergraph-based learning method to refine these coarse representations, representing object parts as graph vertices. Finally, leveraging a diffusion model, the joints of articulated objects-represented as graph edges-are generated based on the object parts. Extensive qualitative and quantitative experiments on the PartNet-Mobility dataset demonstrate the effectiveness of our approach, achieving superior performance over previous methods.", "date": "2025-12-03", "categories": []}
{"id": "2502.01846", "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "abstract": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation. We further find that these heterogeneous features can be compressed into a lower-dimensional (e.g., 3-channel) shared feature space using a carefully designed multi-branch network. The compressed UVGS can be treated as typical RGB images. Remarkably, we discover that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. Our novel representation makes it effortless to leverage foundational 2D models, such as diffusion models, to directly model 3DGS. Additionally, one can simply increase the 2D UV resolution to accommodate more Gaussians, making UVGS a scalable solution compared to typical 3D backbones. This approach immediately unlocks various novel generation applications of 3DGS by inherently utilizing the already developed superior 2D generation capabilities. In our experiments, we demonstrate various unconditional, conditional generation, and inpainting applications of 3DGS based on diffusion models, which were previously non-trivial.", "date": "2025-02-03", "categories": []}
{"id": "2512.03018", "title": "AutoBrep: Autoregressive B-Rep Generation with Unified Topology and Geometry", "abstract": "The boundary representation (B-Rep) is the standard data structure used in Computer-Aided Design (CAD) for defining solid models. Despite recent progress, directly generating B-Reps end-to-end with precise geometry and watertight topology remains a challenge. This paper presents AutoBrep, a novel Transformer model that autoregressively generates B-Reps with high quality and validity. AutoBrep employs a unified tokenization scheme that encodes both geometric and topological characteristics of a B-Rep model as a sequence of discrete tokens. Geometric primitives (i.e., surfaces and curves) are encoded as latent geometry tokens, and their structural relationships are defined as special topological reference tokens. Sequence order in AutoBrep naturally follows a breadth first traversal of the B-Rep face adjacency graph. At inference time, neighboring faces and edges along with their topological structure are progressively generated. Extensive experiments demonstrate the advantages of our unified representation when coupled with next-token prediction for B-Rep generation. AutoBrep outperforms baselines with better quality and watertightness. It is also highly scalable to complex solids with good fidelity and inference speed. We further show that autocompleting B-Reps is natively supported through our unified tokenization, enabling user-controllable CAD generation with minimal changes. Code is available at https://github.com/AutodeskAILab/AutoBrep.", "date": "2025-12-02", "categories": []}
{"id": "2503.08594", "title": "PointNSP: Autoregressive 3D Point Cloud Generation with Next-Scale Level-of-Detail Prediction", "abstract": "Autoregressive point cloud generation has long lagged behind diffusion-based approaches in quality. The performance gap stems from the fact that autoregressive models impose an artificial ordering on inherently unordered point sets, forcing shape generation to proceed as a sequence of local predictions. This sequential bias emphasizes short-range continuity but undermines the model's capacity to capture long-range dependencies, hindering its ability to enforce global structural properties such as symmetry, consistent topology, and large-scale geometric regularities. Inspired by the level-of-detail (LOD) principle in shape modeling, we propose PointNSP, a coarse-to-fine generative framework that preserves global shape structure at low resolutions and progressively refines fine-grained geometry at higher scales through a next-scale prediction paradigm. This multi-scale factorization aligns the autoregressive objective with the permutation-invariant nature of point sets, enabling rich intra-scale interactions while avoiding brittle fixed orderings. Experiments on ShapeNet show that PointNSP establishes state-of-the-art (SOTA) generation quality for the first time within the autoregressive paradigm. In addition, it surpasses strong diffusion-based baselines in parameter, training, and inference efficiency. Finally, in dense generation with 8,192 points, PointNSP's advantages become even more pronounced, underscoring its scalability potential.", "date": "2025-03-11", "categories": []}
{"id": "2512.00413", "title": "SplatFont3D: Structure-Aware Text-to-3D Artistic Font Generation with Part-Level Style Control", "abstract": "Artistic font generation (AFG) can assist human designers in creating innovative artistic fonts. However, most previous studies primarily focus on 2D artistic fonts in flat design, leaving personalized 3D-AFG largely underexplored. 3D-AFG not only enables applications in immersive 3D environments such as video games and animations, but also may enhance 2D-AFG by rendering 2D fonts of novel views. Moreover, unlike general 3D objects, 3D fonts exhibit precise semantics with strong structural constraints and also demand fine-grained part-level style control. To address these challenges, we propose SplatFont3D, a novel structure-aware text-to-3D AFG framework with 3D Gaussian splatting, which enables the creation of 3D artistic fonts from diverse style text prompts with precise part-level style control. Specifically, we first introduce a Glyph2Cloud module, which progressively enhances both the shapes and styles of 2D glyphs (or components) and produces their corresponding 3D point clouds for Gaussian initialization. The initialized 3D Gaussians are further optimized through interaction with a pretrained 2D diffusion model using score distillation sampling. To enable part-level control, we present a dynamic component assignment strategy that exploits the geometric priors of 3D Gaussians to partition components, while alleviating drift-induced entanglement during 3D Gaussian optimization. Our SplatFont3D provides more explicit and effective part-level style control than NeRF, attaining faster rendering efficiency. Experiments show that our SplatFont3D outperforms existing 3D models for 3D-AFG in style-text consistency, visual quality, and rendering efficiency.", "date": "2025-11-29", "categories": []}
{"id": "2507.12465", "title": "PhysX-3D: Physical-Grounded 3D Asset Generation", "abstract": "3D modeling is moving from virtual to physical. Existing 3D generation primarily emphasizes geometries and textures while neglecting physical-grounded modeling. Consequently, despite the rapid development of 3D generative models, the synthesized 3D assets often overlook rich and important physical properties, hampering their real-world application in physical domains like simulation and embodied AI. As an initial attempt to address this challenge, we propose \\textbf{PhysX-3D}, an end-to-end paradigm for physical-grounded 3D asset generation. 1) To bridge the critical gap in physics-annotated 3D datasets, we present PhysXNet - the first physics-grounded 3D dataset systematically annotated across five foundational dimensions: absolute scale, material, affordance, kinematics, and function description. In particular, we devise a scalable human-in-the-loop annotation pipeline based on vision-language models, which enables efficient creation of physics-first assets from raw 3D assets.2) Furthermore, we propose \\textbf{PhysXGen}, a feed-forward framework for physics-grounded image-to-3D asset generation, injecting physical knowledge into the pre-trained 3D structural space. Specifically, PhysXGen employs a dual-branch architecture to explicitly model the latent correlations between 3D structures and physical properties, thereby producing 3D assets with plausible physical predictions while preserving the native geometry quality. Extensive experiments validate the superior performance and promising generalization capability of our framework. All the code, data, and models will be released to facilitate future research in generative physical AI.", "date": "2025-07-16", "categories": []}
{"id": "2511.21887", "title": "UniArt: Unified 3D Representation for Generating 3D Articulated Objects with Open-Set Articulation", "abstract": "Articulated 3D objects play a vital role in realistic simulation and embodied robotics, yet manually constructing such assets remains costly and difficult to scale. In this paper, we present UniArt, a diffusion-based framework that directly synthesizes fully articulated 3D objects from a single image in an end-to-end manner. Unlike prior multi-stage techniques, UniArt establishes a unified latent representation that jointly encodes geometry, texture, part segmentation, and kinematic parameters. We introduce a reversible joint-to-voxel embedding, which spatially aligns articulation features with volumetric geometry, enabling the model to learn coherent motion behaviors alongside structural formation. Furthermore, we formulate articulation type prediction as an open-set problem, removing the need for fixed joint semantics and allowing generalization to novel joint categories and unseen object types. Experiments on the PartNet-Mobility benchmark demonstrate that UniArt achieves state-of-the-art mesh quality and articulation accuracy.", "date": "2025-11-26", "categories": []}
{"id": "2511.22171", "title": "BrepGPT: Autoregressive B-rep Generation with Voronoi Half-Patch", "abstract": "Boundary representation (B-rep) is the de facto standard for CAD model representation in modern industrial design. The intricate coupling between geometric and topological elements in B-rep structures has forced existing generative methods to rely on cascaded multi-stage networks, resulting in error accumulation and computational inefficiency. We present BrepGPT, a single-stage autoregressive framework for B-rep generation. Our key innovation lies in the Voronoi Half-Patch (VHP) representation, which decomposes B-reps into unified local units by assigning geometry to nearest half-edges and sampling their next pointers. Unlike hierarchical representations that require multiple distinct encodings for different structural levels, our VHP representation facilitates unifying geometric attributes and topological relations in a single, coherent format. We further leverage dual VQ-VAEs to encode both vertex topology and Voronoi Half-Patches into vertex-based tokens, achieving a more compact sequential encoding. A decoder-only Transformer is then trained to autoregressively predict these tokens, which are subsequently mapped to vertex-based features and decoded into complete B-rep models. Experiments demonstrate that BrepGPT achieves state-of-the-art performance in unconditional B-rep generation. The framework also exhibits versatility in various applications, including conditional generation from category labels, point clouds, text descriptions, and images, as well as B-rep autocompletion and interpolation.", "date": "2025-11-27", "categories": []}
{"id": "2511.22194", "title": "Controllable 3D Object Generation with Single Image Prompt", "abstract": "Recently, the impressive generative capabilities of diffusion models have been demonstrated, producing images with remarkable fidelity. Particularly, existing methods for the 3D object generation tasks, which is one of the fastest-growing segments in computer vision, pre-dominantly use text-to-image diffusion models with textual inversion which train a pseudo text prompt to describe the given image. In practice, various text-to-image generative models employ textual inversion to learn concepts or styles of target object in the pseudo text prompt embedding space, thereby generating sophisticated outputs. However, textual inversion requires additional training time and lacks control ability. To tackle this issues, we propose two innovative methods: (1) using an off-the-shelf image adapter that generates 3D objects without textual inversion, offering enhanced control over conditions such as depth, pose, and text. (2) a depth conditioned warmup strategy to enhance 3D consistency. In experimental results, ours show qualitatively and quantitatively comparable performance and improved 3D consistency to the existing text-inversion-based alternatives. Furthermore, we conduct a user study to assess (i) how well results match the input image and (ii) whether 3D consistency is maintained. User study results show that our model outperforms the alternatives, validating the effectiveness of our approaches. Our code is available at GitHub repository:https://github.com/Seooooooogi/Control3D_IP/", "date": "2025-11-27", "categories": []}
{"id": "2511.22456", "title": "ITS3D: Inference-Time Scaling for Text-Guided 3D Diffusion Models", "abstract": "We explore inference-time scaling in text-guided 3D diffusion models to enhance generative quality without additional training. To this end, we introduce ITS3D, a framework that formulates the task as an optimization problem to identify the most effective Gaussian noise input. The framework is driven by a verifier-guided search algorithm, where the search algorithm iteratively refines noise candidates based on verifier feedback. To address the inherent challenges of 3D generation, we introduce three techniques for improved stability, efficiency, and exploration capability. 1) Gaussian normalization is applied to stabilize the search process. It corrects distribution shifts when noise candidates deviate from a standard Gaussian distribution during iterative updates. 2) The high-dimensional nature of the 3D search space increases computational complexity. To mitigate this, a singular value decomposition-based compression technique is employed to reduce dimensionality while preserving effective search directions. 3) To further prevent convergence to suboptimal local minima, a singular space reset mechanism dynamically updates the search space based on diversity measures. Extensive experiments demonstrate that ITS3D enhances text-to-3D generation quality, which shows the potential of computationally efficient search methods in generative processes. The source code is available at https://github.com/ZhenglinZhou/ITS3D.", "date": "2025-11-27", "categories": []}
{"id": "2412.06698", "title": "Gen-3Diffusion: Realistic Image-to-3D Generation via 2D & 3D Diffusion Synergy", "abstract": "Creating realistic 3D objects and clothed avatars from a single RGB image is an attractive yet challenging problem. Due to its ill-posed nature, recent works leverage powerful prior from 2D diffusion models pretrained on large datasets. Although 2D diffusion models demonstrate strong generalization capability, they cannot guarantee the generated multi-view images are 3D consistent. In this paper, we propose Gen-3Diffusion: Realistic Image-to-3D Generation via 2D & 3D Diffusion Synergy. We leverage a pre-trained 2D diffusion model and a 3D diffusion model via our elegantly designed process that synchronizes two diffusion models at both training and sampling time. The synergy between the 2D and 3D diffusion models brings two major advantages: 1) 2D helps 3D in generalization: the pretrained 2D model has strong generalization ability to unseen images, providing strong shape priors for the 3D diffusion model; 2) 3D helps 2D in multi-view consistency: the 3D diffusion model enhances the 3D consistency of 2D multi-view sampling process, resulting in more accurate multi-view generation. We validate our idea through extensive experiments in image-based objects and clothed avatar generation tasks. Results show that our method generates realistic 3D objects and avatars with high-fidelity geometry and texture. Extensive ablations also validate our design choices and demonstrate the strong generalization ability to diverse clothing and compositional shapes. Our code and pretrained models will be publicly released on https://yuxuan-xue.com/gen-3diffusion.", "date": "2024-12-09", "categories": []}
{"id": "2510.05613", "title": "PointNSP: Autoregressive 3D Point Cloud Generation with Next-Scale Level-of-Detail Prediction", "abstract": "Autoregressive point cloud generation has long lagged behind diffusion-based approaches in quality. The performance gap stems from the fact that autoregressive models impose an artificial ordering on inherently unordered point sets, forcing shape generation to proceed as a sequence of local predictions. This sequential bias emphasizes short-range continuity but undermines the model's capacity to capture long-range dependencies, hindering its ability to enforce global structural properties such as symmetry, consistent topology, and large-scale geometric regularities. Inspired by the level-of-detail (LOD) principle in shape modeling, we propose PointNSP, a coarse-to-fine generative framework that preserves global shape structure at low resolutions and progressively refines fine-grained geometry at higher scales through a next-scale prediction paradigm. This multi-scale factorization aligns the autoregressive objective with the permutation-invariant nature of point sets, enabling rich intra-scale interactions while avoiding brittle fixed orderings. Experiments on ShapeNet show that PointNSP establishes state-of-the-art (SOTA) generation quality for the first time within the autoregressive paradigm. In addition, it surpasses strong diffusion-based baselines in parameter, training, and inference efficiency. Finally, in dense generation with 8,192 points, PointNSP's advantages become even more pronounced, underscoring its scalability potential.", "date": "2025-10-07", "categories": []}
{"id": "2506.08640", "title": "Orientation Matters: Making 3D Generative Models Orientation-Aligned", "abstract": "Humans intuitively perceive object shape and orientation from a single image, guided by strong priors about canonical poses. However, existing 3D generative models often produce misaligned results due to inconsistent training data, limiting their usability in downstream tasks. To address this gap, we introduce the task of orientation-aligned 3D object generation: producing 3D objects from single images with consistent orientations across categories. To facilitate this, we construct Objaverse-OA, a dataset of 14,832 orientation-aligned 3D models spanning 1,008 categories. Leveraging Objaverse-OA, we fine-tune two representative 3D generative models based on multi-view diffusion and 3D variational autoencoder frameworks to produce aligned objects that generalize well to unseen objects across various categories. Experimental results demonstrate the superiority of our method over post-hoc alignment approaches. Furthermore, we showcase downstream applications enabled by our aligned object generation, including zero-shot object orientation estimation via analysis-by-synthesis and efficient arrow-based object rotation manipulation.", "date": "2025-06-10", "categories": []}
{"id": "2511.19512", "title": "Single Image to High-Quality 3D Object via Latent Features", "abstract": "3D assets are essential in the digital age. While automatic 3D generation, such as image-to-3d, has made significant strides in recent years, it often struggles to achieve fast, detailed, and high-fidelity generation simultaneously. In this work, we introduce LatentDreamer, a novel framework for generating 3D objects from single images. The key to our approach is a pre-trained variational autoencoder that maps 3D geometries to latent features, which greatly reducing the difficulty of 3D generation. Starting from latent features, the pipeline of LatentDreamer generates coarse geometries, refined geometries, and realistic textures sequentially. The 3D objects generated by LatentDreamer exhibit high fidelity to the input images, and the entire generation process can be completed within a short time (typically in 70 seconds). Extensive experiments show that with only a small amount of training, LatentDreamer demonstrates competitive performance compared to contemporary approachs.", "date": "2025-11-24", "categories": []}
{"id": "2511.20041", "title": "MFM-point: Multi-scale Flow Matching for Point Cloud Generation", "abstract": "In recent years, point cloud generation has gained significant attention in 3D generative modeling. Among existing approaches, point-based methods directly generate point clouds without relying on other representations such as latent features, meshes, or voxels. These methods offer low training cost and algorithmic simplicity, but often underperform compared to representation-based approaches. In this paper, we propose MFM-Point, a multi-scale Flow Matching framework for point cloud generation that substantially improves the scalability and performance of point-based methods while preserving their simplicity and efficiency. Our multi-scale generation algorithm adopts a coarse-to-fine generation paradigm, enhancing generation quality and scalability without incurring additional training or inference overhead. A key challenge in developing such a multi-scale framework lies in preserving the geometric structure of unordered point clouds while ensuring smooth and consistent distributional transitions across resolutions. To address this, we introduce a structured downsampling and upsampling strategy that preserves geometry and maintains alignment between coarse and fine resolutions. Our experimental results demonstrate that MFM-Point achieves best-in-class performance among point-based methods and challenges the best representation-based methods. In particular, MFM-point demonstrates strong results in multi-category and high-resolution generation tasks.", "date": "2025-11-25", "categories": []}
{"id": "2511.20624", "title": "ShapeGen: Towards High-Quality 3D Shape Synthesis", "abstract": "Inspired by generative paradigms in image and video, 3D shape generation has made notable progress, enabling the rapid synthesis of high-fidelity 3D assets from a single image. However, current methods still face challenges, including the lack of intricate details, overly smoothed surfaces, and fragmented thin-shell structures. These limitations leave the generated 3D assets still one step short of meeting the standards favored by artists. In this paper, we present ShapeGen, which achieves high-quality image-to-3D shape generation through 3D representation and supervision improvements, resolution scaling up, and the advantages of linear transformers. These advancements allow the generated assets to be seamlessly integrated into 3D pipelines, facilitating their widespread adoption across various applications. Through extensive experiments, we validate the impact of these improvements on overall performance. Ultimately, thanks to the synergistic effects of these enhancements, ShapeGen achieves a significant leap in image-to-3D generation, establishing a new state-of-the-art performance.", "date": "2025-11-25", "categories": []}
{"id": "2509.00062", "title": "Scaffold Diffusion: Sparse Multi-Category Voxel Structure Generation with Discrete Diffusion", "abstract": "Generating realistic sparse multi-category 3D voxel structures is difficult due to the cubic memory scaling of voxel structures and moreover the significant class imbalance caused by sparsity. We introduce Scaffold Diffusion, a generative model designed for sparse multi-category 3D voxel structures. By treating voxels as tokens, Scaffold Diffusion uses a discrete diffusion language model to generate 3D voxel structures. We show that discrete diffusion language models can be extended beyond inherently sequential domains such as text to generate spatially coherent 3D structures. We evaluate on Minecraft house structures from the 3D-Craft dataset and demonstrate that, unlike prior baselines and an auto-regressive formulation, Scaffold Diffusion produces realistic and coherent structures even when trained on data with over 98% sparsity. We provide an interactive viewer where readers can visualize generated samples and the generation process: https://scaffold.deepexploration.org/", "date": "2025-08-26", "categories": []}
{"id": "2511.17883", "title": "ArticFlow: Generative Simulation of Articulated Mechanisms", "abstract": "Recent advances in generative models have produced strong results for static 3D shapes, whereas articulated 3D generation remains challenging due to action-dependent deformations and limited datasets. We introduce ArticFlow, a two-stage flow matching framework that learns a controllable velocity field from noise to target point sets under explicit action control. ArticFlow couples (i) a latent flow that transports noise to a shape-prior code and (ii) a point flow that transports points conditioned on the action and the shape prior, enabling a single model to represent diverse articulated categories and generalize across actions. On MuJoCo Menagerie, ArticFlow functions both as a generative model and as a neural simulator: it predicts action-conditioned kinematics from a compact prior and synthesizes novel morphologies via latent interpolation. Compared with object-specific simulators and an action-conditioned variant of static point-cloud generators, ArticFlow achieves higher kinematic accuracy and better shape quality. Results show that action-conditioned flow matching is a practical route to controllable and high-quality articulated mechanism generation.", "date": "2025-11-22", "categories": []}
{"id": "2511.16807", "title": "Mesh RAG: Retrieval Augmentation for Autoregressive Mesh Generation", "abstract": "3D meshes are a critical building block for applications ranging from industrial design and gaming to simulation and robotics. Traditionally, meshes are crafted manually by artists, a process that is time-intensive and difficult to scale. To automate and accelerate this asset creation, autoregressive models have emerged as a powerful paradigm for artistic mesh generation. However, current methods to enhance quality typically rely on larger models or longer sequences that result in longer generation time, and their inherent sequential nature imposes a severe quality-speed trade-off. This sequential dependency also significantly complicates incremental editing. To overcome these limitations, we propose Mesh RAG, a novel, training-free, plug-and-play framework for autoregressive mesh generation models. Inspired by RAG for language models, our approach augments the generation process by leveraging point cloud segmentation, spatial transformation, and point cloud registration to retrieve, generate, and integrate mesh components. This retrieval-based approach decouples generation from its strict sequential dependency, facilitating efficient and parallelizable inference. We demonstrate the wide applicability of Mesh RAG across various foundational autoregressive mesh generation models, showing it significantly enhances mesh quality, accelerates generation speed compared to sequential part prediction, and enables incremental editing, all without model retraining.", "date": "2025-11-20", "categories": []}
{"id": "2412.17957", "title": "ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based Upsampling", "abstract": "Recent advances in 3D generative models have shown promising results but often fall short in capturing the complexity of architectural geometries and topologies and fine geometric details at high resolutions. To tackle this, we present ArchComplete, a two-stage voxel-based 3D generative pipeline consisting of a vector-quantised model, whose composition is modelled with an autoregressive transformer for generating coarse shapes, followed by a hierarchical upsampling strategy for further enrichment with fine structures and details. Key to our pipeline is (i) learning a contextually rich codebook of local patch embeddings, optimised alongside a 2.5D perceptual loss that captures global spatial correspondence of projections onto three axis-aligned orthogonal planes, and (ii) redefining upsampling as a set of conditional diffusion models learning from a hierarchy of randomly cropped coarse-to-fine local volumetric patches. Trained on our introduced dataset of 3D house models with fully modelled exterior and interior, ArchComplete autoregressively generates models at the resolution of $64^{3}$ and progressively refines them up to $512^{3}$, with voxel sizes as small as $ \\approx 9\\text{cm}$. ArchComplete solves a variety of tasks, including genetic interpolation and variation, unconditional synthesis, shape and plan-drawing completion, as well as geometric detailisation, while achieving state-of-the-art performance in quality, diversity, and computational efficiency.", "date": "2024-12-23", "categories": []}
{"id": "2511.01767", "title": "Wonder3D++: Cross-domain Diffusion for High-fidelity 3D Generation from a Single Image", "abstract": "In this work, we introduce \\textbf{Wonder3D++}, a novel method for efficiently generating high-fidelity textured meshes from single-view images. Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry. In contrast, certain works directly produce 3D information via fast network inferences, but their results are often of low quality and lack geometric details. To holistically improve the quality, consistency, and efficiency of single-view reconstruction tasks, we propose a cross-domain diffusion model that generates multi-view normal maps and the corresponding color images. To ensure the consistency of generation, we employ a multi-view cross-domain attention mechanism that facilitates information exchange across views and modalities. Lastly, we introduce a cascaded 3D mesh extraction algorithm that drives high-quality surfaces from the multi-view 2D representations in only about $3$ minute in a coarse-to-fine manner. Our extensive evaluations demonstrate that our method achieves high-quality reconstruction results, robust generalization, and good efficiency compared to prior works. Code available at https://github.com/xxlong0/Wonder3D/tree/Wonder3D_Plus.", "date": "2025-11-03", "categories": []}
{"id": "2511.14870", "title": "B-Rep Distance Functions (BR-DF): How to Represent a B-Rep Model by Volumetric Distance Functions?", "abstract": "This paper presents a novel geometric representation for CAD Boundary Representation (B-Rep) based on volumetric distance functions, dubbed B-Rep Distance Functions (BR-DF). BR-DF encodes the surface mesh geometry of a CAD model as signed distance function (SDF). B-Rep vertices, edges, faces and their topology information are encoded as per-face unsigned distance functions (UDFs). An extension of the Marching Cubes algorithm converts BR-DF directly into watertight CAD B-Rep model (strictly speaking a faceted B-Rep model). A surprising characteristic of BR-DF is that this conversion process never fails. Leveraging the volumetric nature of BR-DF, we propose a multi-branch latent diffusion with 3D U-Net backbone for jointly generating the SDF and per-face UDFs of a BR-DF model. Our approach achieves comparable CAD generation performance against SOTA methods while reaching the unprecedented 100% success rate in producing (faceted) B-Rep models.", "date": "2025-11-18", "categories": []}
{"id": "2511.15618", "title": "FlashMesh: Faster and Better Autoregressive Mesh Synthesis via Structured Speculation", "abstract": "Autoregressive models can generate high-quality 3D meshes by sequentially producing vertices and faces, but their token-by-token decoding results in slow inference, limiting practical use in interactive and large-scale applications. We present FlashMesh, a fast and high-fidelity mesh generation framework that rethinks autoregressive decoding through a predict-correct-verify paradigm. The key insight is that mesh tokens exhibit strong structural and geometric correlations that enable confident multi-token speculation. FlashMesh leverages this by introducing a speculative decoding scheme tailored to the commonly used hourglass transformer architecture, enabling parallel prediction across face, point, and coordinate levels. Extensive experiments show that FlashMesh achieves up to a 2 x speedup over standard autoregressive models while also improving generation fidelity. Our results demonstrate that structural priors in mesh data can be systematically harnessed to accelerate and enhance autoregressive generation.", "date": "2025-11-19", "categories": []}
{"id": "2511.10040", "title": "LoG3D: Ultra-High-Resolution 3D Shape Modeling via Local-to-Global Partitioning", "abstract": "Generating high-fidelity 3D contents remains a fundamental challenge due to the complexity of representing arbitrary topologies-such as open surfaces and intricate internal structures-while preserving geometric details. Prevailing methods based on signed distance fields (SDFs) are hampered by costly watertight preprocessing and struggle with non-manifold geometries, while point-cloud representations often suffer from sampling artifacts and surface discontinuities. To overcome these limitations, we propose a novel 3D variational autoencoder (VAE) framework built upon unsigned distance fields (UDFs)-a more robust and computationally efficient representation that naturally handles complex and incomplete shapes. Our core innovation is a local-to-global (LoG) architecture that processes the UDF by partitioning it into uniform subvolumes, termed UBlocks. This architecture couples 3D convolutions for capturing local detail with sparse transformers for enforcing global coherence. A Pad-Average strategy further ensures smooth transitions at subvolume boundaries during reconstruction. This modular design enables seamless scaling to ultra-high resolutions up to $2048^3$-a regime previously unattainable for 3D VAEs. Experiments demonstrate state-of-the-art performance in both reconstruction accuracy and generative quality, yielding superior surface smoothness and geometric flexibility.", "date": "2025-11-13", "categories": []}
{"id": "2511.14271", "title": "Let Language Constrain Geometry: Vision-Language Models as Semantic and Spatial Critics for 3D Generation", "abstract": "Text-to-3D generation has advanced rapidly, yet state-of-the-art models, encompassing both optimization-based and feed-forward architectures, still face two fundamental limitations. First, they struggle with coarse semantic alignment, often failing to capture fine-grained prompt details. Second, they lack robust 3D spatial understanding, leading to geometric inconsistencies and catastrophic failures in part assembly and spatial relationships. To address these challenges, we propose VLM3D, a general framework that repurposes large vision-language models (VLMs) as powerful, differentiable semantic and spatial critics. Our core contribution is a dual-query critic signal derived from the VLM's Yes or No log-odds, which assesses both semantic fidelity and geometric coherence. We demonstrate the generality of this guidance signal across two distinct paradigms: (1) As a reward objective for optimization-based pipelines, VLM3D significantly outperforms existing methods on standard benchmarks. (2) As a test-time guidance module for feed-forward pipelines, it actively steers the iterative sampling process of SOTA native 3D models to correct severe spatial errors. VLM3D establishes a principled and generalizable path to inject the VLM's rich, language-grounded understanding of both semantics and space into diverse 3D generative pipelines.", "date": "2025-11-18", "categories": []}
{"id": "2511.06194", "title": "NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS Modeling", "abstract": "Generating editable 3D CAD models from natural language remains challenging, as existing text-to-CAD systems either produce meshes or rely on scarce design-history data. We present NURBGen, the first framework to generate high-fidelity 3D CAD models directly from text using Non-Uniform Rational B-Splines (NURBS). To achieve this, we fine-tune a large language model (LLM) to translate free-form texts into JSON representations containing NURBS surface parameters (\\textit{i.e}, control points, knot vectors, degrees, and rational weights) which can be directly converted into BRep format using Python. We further propose a hybrid representation that combines untrimmed NURBS with analytic primitives to handle trimmed surfaces and degenerate regions more robustly, while reducing token complexity. Additionally, we introduce partABC, a curated subset of the ABC dataset consisting of individual CAD components, annotated with detailed captions using an automated annotation pipeline. NURBGen demonstrates strong performance on diverse prompts, surpassing prior methods in geometric fidelity and dimensional accuracy, as confirmed by expert evaluations. Code and dataset will be released publicly.", "date": "2025-11-09", "categories": []}
{"id": "2511.11692", "title": "AnchorDS: Anchoring Dynamic Sources for Semantically Consistent Text-to-3D Generation", "abstract": "Optimization-based text-to-3D methods distill guidance from 2D generative models via Score Distillation Sampling (SDS), but implicitly treat this guidance as static. This work shows that ignoring source dynamics yields inconsistent trajectories that suppress or merge semantic cues, leading to \"semantic over-smoothing\" artifacts. As such, we reformulate text-to-3D optimization as mapping a dynamically evolving source distribution to a fixed target distribution. We cast the problem into a dual-conditioned latent space, conditioned on both the text prompt and the intermediately rendered image. Given this joint setup, we observe that the image condition naturally anchors the current source distribution. Building on this insight, we introduce AnchorDS, an improved score distillation mechanism that provides state-anchored guidance with image conditions and stabilizes generation. We further penalize erroneous source estimates and design a lightweight filter strategy and fine-tuning strategy that refines the anchor with negligible overhead. AnchorDS produces finer-grained detail, more natural colours, and stronger semantic consistency, particularly for complex prompts, while maintaining efficiency. Extensive experiments show that our method surpasses previous methods in both quality and efficiency.", "date": "2025-11-12", "categories": []}
{"id": "2511.11710", "title": "Target-Balanced Score Distillation", "abstract": "Score Distillation Sampling (SDS) enables 3D asset generation by distilling priors from pretrained 2D text-to-image diffusion models, but vanilla SDS suffers from over-saturation and over-smoothing. To mitigate this issue, recent variants have incorporated negative prompts. However, these methods face a critical trade-off: limited texture optimization, or significant texture gains with shape distortion. In this work, we first conduct a systematic analysis and reveal that this trade-off is fundamentally governed by the utilization of the negative prompts, where Target Negative Prompts (TNP) that embed target information in the negative prompts dramatically enhancing texture realism and fidelity but inducing shape distortions. Informed by this key insight, we introduce the Target-Balanced Score Distillation (TBSD). It formulates generation as a multi-objective optimization problem and introduces an adaptive strategy that effectively resolves the aforementioned trade-off. Extensive experiments demonstrate that TBSD significantly outperforms existing state-of-the-art methods, yielding 3D assets with high-fidelity textures and geometrically accurate shape.", "date": "2025-11-12", "categories": []}
{"id": "2511.12202", "title": "LSS3D: Learnable Spatial Shifting for Consistent and High-Quality 3D Generation from Single-Image", "abstract": "Recently, multi-view diffusion-based 3D generation methods have gained significant attention. However, these methods often suffer from shape and texture misalignment across generated multi-view images, leading to low-quality 3D generation results, such as incomplete geometric details and textural ghosting. Some methods are mainly optimized for the frontal perspective and exhibit poor robustness to oblique perspective inputs. In this paper, to tackle the above challenges, we propose a high-quality image-to-3D approach, named LSS3D, with learnable spatial shifting to explicitly and effectively handle the multiview inconsistencies and non-frontal input view. Specifically, we assign learnable spatial shifting parameters to each view, and adjust each view towards a spatially consistent target, guided by the reconstructed mesh, resulting in high-quality 3D generation with more complete geometric details and clean textures. Besides, we include the input view as an extra constraint for the optimization, further enhancing robustness to non-frontal input angles, especially for elevated viewpoint inputs. We also provide a comprehensive quantitative evaluation pipeline that can contribute to the community in performance comparisons. Extensive experiments demonstrate that our method consistently achieves leading results in both geometric and texture evaluation metrics across more flexible input viewpoints.", "date": "2025-11-15", "categories": []}
{"id": "2511.13648", "title": "PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image", "abstract": "3D modeling is shifting from static visual representations toward physical, articulated assets that can be directly used in simulation and interaction. However, most existing 3D generation methods overlook key physical and articulation properties, thereby limiting their utility in embodied AI. To bridge this gap, we introduce PhysX-Anything, the first simulation-ready physical 3D generative framework that, given a single in-the-wild image, produces high-quality sim-ready 3D assets with explicit geometry, articulation, and physical attributes. Specifically, we propose the first VLM-based physical 3D generative model, along with a new 3D representation that efficiently tokenizes geometry. It reduces the number of tokens by 193x, enabling explicit geometry learning within standard VLM token budgets without introducing any special tokens during fine-tuning and significantly improving generative quality. In addition, to overcome the limited diversity of existing physical 3D datasets, we construct a new dataset, PhysX-Mobility, which expands the object categories in prior physical 3D datasets by over 2x and includes more than 2K common real-world objects with rich physical annotations. Extensive experiments on PhysX-Mobility and in-the-wild images demonstrate that PhysX-Anything delivers strong generative performance and robust generalization. Furthermore, simulation-based experiments in a MuJoCo-style environment validate that our sim-ready assets can be directly used for contact-rich robotic policy learning. We believe PhysX-Anything can substantially empower a broad range of downstream applications, especially in embodied AI and physics-based simulation.", "date": "2025-11-17", "categories": []}
{"id": "2509.19995", "title": "MeshMosaic: Scaling Artist Mesh Generation via Local-to-Global Assembly", "abstract": "Scaling artist-designed meshes to high triangle numbers remains challenging for autoregressive generative models. Existing transformer-based methods suffer from long-sequence bottlenecks and limited quantization resolution, primarily due to the large number of tokens required and constrained quantization granularity. These issues prevent faithful reproduction of fine geometric details and structured density patterns. We introduce MeshMosaic, a novel local-to-global framework for artist mesh generation that scales to over 100K triangles--substantially surpassing prior methods, which typically handle only around 8K faces. MeshMosaic first segments shapes into patches, generating each patch autoregressively and leveraging shared boundary conditions to promote coherence, symmetry, and seamless connectivity between neighboring regions. This strategy enhances scalability to high-resolution meshes by quantizing patches individually, resulting in more symmetrical and organized mesh density and structure. Extensive experiments across multiple public datasets demonstrate that MeshMosaic significantly outperforms state-of-the-art methods in both geometric fidelity and user preference, supporting superior detail representation and practical mesh generation for real-world applications.", "date": "2025-09-24", "categories": []}
{"id": "2511.08203", "title": "Twist and Compute: The Cost of Pose in 3D Generative Diffusion", "abstract": "Despite their impressive results, large-scale image-to-3D generative models remain opaque in their inductive biases. We identify a significant limitation in image-conditioned 3D generative models: a strong canonical view bias. Through controlled experiments using simple 2D rotations, we show that the state-of-the-art Hunyuan3D 2.0 model can struggle to generalize across viewpoints, with performance degrading under rotated inputs. We show that this failure can be mitigated by a lightweight CNN that detects and corrects input orientation, restoring model performance without modifying the generative backbone. Our findings raise an important open question: Is scale enough, or should we pursue modular, symmetry-aware designs?", "date": "2025-11-11", "categories": []}
{"id": "2511.05308", "title": "Rethinking Metrics and Diffusion Architecture for 3D Point Cloud Generation", "abstract": "As 3D point clouds become a cornerstone of modern technology, the need for sophisticated generative models and reliable evaluation metrics has grown exponentially. In this work, we first expose that some commonly used metrics for evaluating generated point clouds, particularly those based on Chamfer Distance (CD), lack robustness against defects and fail to capture geometric fidelity and local shape consistency when used as quality indicators. We further show that introducing samples alignment prior to distance calculation and replacing CD with Density-Aware Chamfer Distance (DCD) are simple yet essential steps to ensure the consistency and robustness of point cloud generative model evaluation metrics. While existing metrics primarily focus on directly comparing 3D Euclidean coordinates, we present a novel metric, named Surface Normal Concordance (SNC), which approximates surface similarity by comparing estimated point normals. This new metric, when combined with traditional ones, provides a more comprehensive evaluation of the quality of generated samples. Finally, leveraging recent advancements in transformer-based models for point cloud analysis, such as serialized patch attention , we propose a new architecture for generating high-fidelity 3D structures, the Diffusion Point Transformer. We perform extensive experiments and comparisons on the ShapeNet dataset, showing that our model outperforms previous solutions, particularly in terms of quality of generated point clouds, achieving new state-of-the-art. Code available at https://github.com/matteo-bastico/DiffusionPointTransformer.", "date": "2025-11-07", "categories": []}
{"id": "2511.05609", "title": "Walking the Schr\\\"odinger Bridge: A Direct Trajectory for Text-to-3D Generation", "abstract": "Recent advancements in optimization-based text-to-3D generation heavily rely on distilling knowledge from pre-trained text-to-image diffusion models using techniques like Score Distillation Sampling (SDS), which often introduce artifacts such as over-saturation and over-smoothing into the generated 3D assets. In this paper, we address this essential problem by formulating the generation process as learning an optimal, direct transport trajectory between the distribution of the current rendering and the desired target distribution, thereby enabling high-quality generation with smaller Classifier-free Guidance (CFG) values. At first, we theoretically establish SDS as a simplified instance of the Schr\\\"odinger Bridge framework. We prove that SDS employs the reverse process of an Schr\\\"odinger Bridge, which, under specific conditions (e.g., a Gaussian noise as one end), collapses to SDS's score function of the pre-trained diffusion model. Based upon this, we introduce Trajectory-Centric Distillation (TraCe), a novel text-to-3D generation framework, which reformulates the mathematically trackable framework of Schr\\\"odinger Bridge to explicitly construct a diffusion bridge from the current rendering to its text-conditioned, denoised target, and trains a LoRA-adapted model on this trajectory's score dynamics for robust 3D optimization. Comprehensive experiments demonstrate that TraCe consistently achieves superior quality and fidelity to state-of-the-art techniques.", "date": "2025-11-06", "categories": []}
{"id": "2511.07142", "title": "ProcGen3D: Learning Neural Procedural Graph Representations for Image-to-3D Reconstruction", "abstract": "We introduce ProcGen3D, a new approach for 3D content creation by generating procedural graph abstractions of 3D objects, which can then be decoded into rich, complex 3D assets. Inspired by the prevalent use of procedural generators in production 3D applications, we propose a sequentialized, graph-based procedural graph representation for 3D assets. We use this to learn to approximate the landscape of a procedural generator for image-based 3D reconstruction. We employ edge-based tokenization to encode the procedural graphs, and train a transformer prior to predict the next token conditioned on an input RGB image. Crucially, to enable better alignment of our generated outputs to an input image, we incorporate Monte Carlo Tree Search (MCTS) guided sampling into our generation process, steering output procedural graphs towards more image-faithful reconstructions. Our approach is applicable across a variety of objects that can be synthesized with procedural generators. Extensive experiments on cacti, trees, and bridges show that our neural procedural graph generation outperforms both state-of-the-art generative 3D methods and domain-specific modeling techniques. Furthermore, this enables improved generalization on real-world input images, despite training only on synthetic data.", "date": "2025-11-10", "categories": []}
{"id": "2504.01619", "title": "3DBonsai: Structure-Aware Bonsai Modeling Using Conditioned 3D Gaussian Splatting", "abstract": "Recent advancements in text-to-3D generation have shown remarkable results by leveraging 3D priors in combination with 2D diffusion. However, previous methods utilize 3D priors that lack detailed and complex structural information, limiting them to generating simple objects and presenting challenges for creating intricate structures such as bonsai. In this paper, we propose 3DBonsai, a novel text-to-3D framework for generating 3D bonsai with complex structures. Technically, we first design a trainable 3D space colonization algorithm to produce bonsai structures, which are then enhanced through random sampling and point cloud augmentation to serve as the 3D Gaussian priors. We introduce two bonsai generation pipelines with distinct structural levels: fine structure conditioned generation, which initializes 3D Gaussians using a 3D structure prior to produce detailed and complex bonsai, and coarse structure conditioned generation, which employs a multi-view structure consistency module to align 2D and 3D structures. Moreover, we have compiled a unified 2D and 3D Chinese-style bonsai dataset. Our experimental results demonstrate that 3DBonsai significantly outperforms existing methods, providing a new benchmark for structure-aware 3D bonsai generation.", "date": "2025-04-02", "categories": []}
{"id": "2510.25765", "title": "FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusion", "abstract": "Articulated 3D objects are central to many applications in robotics, AR/VR, and animation. Recent approaches to modeling such objects either rely on optimization-based reconstruction pipelines that require dense-view supervision or on feed-forward generative models that produce coarse geometric approximations and often overlook surface texture. In contrast, open-world 3D generation of static objects has achieved remarkable success, especially with the advent of native 3D diffusion models such as Trellis. However, extending these methods to articulated objects by training native 3D diffusion models poses significant challenges. In this work, we present FreeArt3D, a training-free framework for articulated 3D object generation. Instead of training a new model on limited articulated data, FreeArt3D repurposes a pre-trained static 3D diffusion model (e.g., Trellis) as a powerful shape prior. It extends Score Distillation Sampling (SDS) into the 3D-to-4D domain by treating articulation as an additional generative dimension. Given a few images captured in different articulation states, FreeArt3D jointly optimizes the object's geometry, texture, and articulation parameters without requiring task-specific training or access to large-scale articulated datasets. Our method generates high-fidelity geometry and textures, accurately predicts underlying kinematic structures, and generalizes well across diverse object categories. Despite following a per-instance optimization paradigm, FreeArt3D completes in minutes and significantly outperforms prior state-of-the-art approaches in both quality and versatility. Please check our website for more details: https://czzzzh.github.io/FreeArt3D", "date": "2025-10-29", "categories": []}
{"id": "2507.08772", "title": "From One to More: Contextual Part Latents for 3D Generation", "abstract": "Recent advances in 3D generation have transitioned from multi-view 2D rendering approaches to 3D-native latent diffusion frameworks that exploit geometric priors in ground truth data. Despite progress, three key limitations persist: (1) Single-latent representations fail to capture complex multi-part geometries, causing detail degradation; (2) Holistic latent coding neglects part independence and interrelationships critical for compositional design; (3) Global conditioning mechanisms lack fine-grained controllability. Inspired by human 3D design workflows, we propose CoPart - a part-aware diffusion framework that decomposes 3D objects into contextual part latents for coherent multi-part generation. This paradigm offers three advantages: i) Reduces encoding complexity through part decomposition; ii) Enables explicit part relationship modeling; iii) Supports part-level conditioning. We further develop a mutual guidance strategy to fine-tune pre-trained diffusion models for joint part latent denoising, ensuring both geometric coherence and foundation model priors. To enable large-scale training, we construct Partverse - a novel 3D part dataset derived from Objaverse through automated mesh segmentation and human-verified annotations. Extensive experiments demonstrate CoPart's superior capabilities in part-level editing, articulated object generation, and scene composition with unprecedented controllability.", "date": "2025-07-11", "categories": []}
{"id": "2510.26140", "title": "FullPart: Generating each 3D Part at Full Resolution", "abstract": "Part-based 3D generation holds great potential for various applications. Previous part generators that represent parts using implicit vector-set tokens often suffer from insufficient geometric details. Another line of work adopts an explicit voxel representation but shares a global voxel grid among all parts; this often causes small parts to occupy too few voxels, leading to degraded quality. In this paper, we propose FullPart, a novel framework that combines both implicit and explicit paradigms. It first derives the bounding box layout through an implicit box vector-set diffusion process, a task that implicit diffusion handles effectively since box tokens contain little geometric detail. Then, it generates detailed parts, each within its own fixed full-resolution voxel grid. Instead of sharing a global low-resolution space, each part in our method - even small ones - is generated at full resolution, enabling the synthesis of intricate details. We further introduce a center-point encoding strategy to address the misalignment issue when exchanging information between parts of different actual sizes, thereby maintaining global coherence. Moreover, to tackle the scarcity of reliable part data, we present PartVerse-XL, the largest human-annotated 3D part dataset to date with 40K objects and 320K parts. Extensive experiments demonstrate that FullPart achieves state-of-the-art results in 3D part generation. We will release all code, data, and model to benefit future research in 3D part generation.", "date": "2025-10-30", "categories": []}
{"id": "2510.21264", "title": "Topology Sculptor, Shape Refiner: Discrete Diffusion Model for High-Fidelity 3D Meshes Generation", "abstract": "In this paper, we introduce Topology Sculptor, Shape Refiner (TSSR), a novel method for generating high-quality, artist-style 3D meshes based on Discrete Diffusion Models (DDMs). Our primary motivation for TSSR is to achieve highly accurate token prediction while enabling parallel generation, a significant advantage over sequential autoregressive methods. By allowing TSSR to \"see\" all mesh tokens concurrently, we unlock a new level of efficiency and control. We leverage this parallel generation capability through three key innovations: 1) Decoupled Training and Hybrid Inference, which distinctly separates the DDM-based generation into a topology sculpting stage and a subsequent shape refinement stage. This strategic decoupling enables TSSR to effectively capture both intricate local topology and overarching global shape. 2) An Improved Hourglass Architecture, featuring bidirectional attention enriched by face-vertex-sequence level Rotational Positional Embeddings (RoPE), thereby capturing richer contextual information across the mesh structure. 3) A novel Connection Loss, which acts as a topological constraint to further enhance the realism and fidelity of the generated meshes. Extensive experiments on complex datasets demonstrate that TSSR generates high-quality 3D artist-style meshes, capable of achieving up to 10,000 faces at a remarkable spatial resolution of $1024^3$. The code will be released at: https://github.com/psky1111/Tencent-TSSR.", "date": "2025-10-24", "categories": []}
{"id": "2510.22337", "title": "GeoDiffusion: A Training-Free Framework for Accurate 3D Geometric Conditioning in Image Generation", "abstract": "Precise geometric control in image generation is essential for engineering \\& product design and creative industries to control 3D object features accurately in image space. Traditional 3D editing approaches are time-consuming and demand specialized skills, while current image-based generative methods lack accuracy in geometric conditioning. To address these challenges, we propose GeoDiffusion, a training-free framework for accurate and efficient geometric conditioning of 3D features in image generation. GeoDiffusion employs a class-specific 3D object as a geometric prior to define keypoints and parametric correlations in 3D space. We ensure viewpoint consistency through a rendered image of a reference 3D object, followed by style transfer to meet user-defined appearance specifications. At the core of our framework is GeoDrag, improving accuracy and speed of drag-based image editing on geometry guidance tasks and general instructions on DragBench. Our results demonstrate that GeoDiffusion enables precise geometric modifications across various iterative design workflows.", "date": "2025-10-25", "categories": []}
{"id": "2510.19944", "title": "Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets", "abstract": "Developing embodied AI agents requires scalable training environments that balance content diversity with physics accuracy. World simulators provide such environments but face distinct limitations: video-based methods generate diverse content but lack real-time physics feedback for interactive learning, while physics-based engines provide accurate dynamics but face scalability limitations from costly manual asset creation. We present Seed3D 1.0, a foundation model that generates simulation-ready 3D assets from single images, addressing the scalability challenge while maintaining physics rigor. Unlike existing 3D generation models, our system produces assets with accurate geometry, well-aligned textures, and realistic physically-based materials. These assets can be directly integrated into physics engines with minimal configuration, enabling deployment in robotic manipulation and simulation training. Beyond individual objects, the system scales to complete scene generation through assembling objects into coherent environments. By enabling scalable simulation-ready content creation, Seed3D 1.0 provides a foundation for advancing physics-based world simulators. Seed3D 1.0 is now available on https://console.volcengine.com/ark/region:ark+cn-beijing/experience/vision?modelId=doubao-seed3d-1-0-250928&tab=Gen3D", "date": "2025-10-22", "categories": []}
{"id": "2510.21432", "title": "ArtiLatent: Realistic Articulated 3D Object Generation via Structured Latents", "abstract": "We propose ArtiLatent, a generative framework that synthesizes human-made 3D objects with fine-grained geometry, accurate articulation, and realistic appearance. Our approach jointly models part geometry and articulation dynamics by embedding sparse voxel representations and associated articulation properties, including joint type, axis, origin, range, and part category, into a unified latent space via a variational autoencoder. A latent diffusion model is then trained over this space to enable diverse yet physically plausible sampling. To reconstruct photorealistic 3D shapes, we introduce an articulation-aware Gaussian decoder that accounts for articulation-dependent visibility changes (e.g., revealing the interior of a drawer when opened). By conditioning appearance decoding on articulation state, our method assigns plausible texture features to regions that are typically occluded in static poses, significantly improving visual realism across articulation configurations. Extensive experiments on furniture-like objects from PartNet-Mobility and ACD datasets demonstrate that ArtiLatent outperforms existing approaches in geometric consistency and appearance fidelity. Our framework provides a scalable solution for articulated 3D object synthesis and manipulation.", "date": "2025-10-24", "categories": []}
{"id": "2505.16761", "title": "Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning", "abstract": "Existing pretrained models for 3D mesh generation often suffer from data biases and produce low-quality results, while global reinforcement learning (RL) methods rely on object-level rewards that struggle to capture local structure details. To address these challenges, we present Mesh-RFT, a novel fine-grained reinforcement fine-tuning framework that employs Masked Direct Preference Optimization (M-DPO) to enable localized refinement via quality-aware face masking. To facilitate efficient quality evaluation, we introduce an objective topology-aware scoring system to evaluate geometric integrity and topological regularity at both object and face levels through two metrics: Boundary Edge Ratio (BER) and Topology Score (TS). By integrating these metrics into a fine-grained RL strategy, Mesh-RFT becomes the first method to optimize mesh quality at the granularity of individual faces, resolving localized errors while preserving global coherence. Experiment results show that our M-DPO approach reduces Hausdorff Distance (HD) by 24.6% and improves Topology Score (TS) by 3.8% over pre-trained models, while outperforming global DPO methods with a 17.4% HD reduction and 4.9% TS gain. These results demonstrate Mesh-RFT's ability to improve geometric integrity and topological regularity, achieving new state-of-the-art performance in production-ready mesh generation. Project Page: https://hitcslj.github.io/mesh-rft/.", "date": "2025-05-22", "categories": []}
{"id": "2506.21152", "title": "Geometry and Perception Guided Gaussians for Multiview-consistent 3D Generation from a Single Image", "abstract": "Generating realistic 3D objects from single-view images requires natural appearance, 3D consistency, and the ability to capture multiple plausible interpretations of unseen regions. Existing approaches often rely on fine-tuning pretrained 2D diffusion models or directly generating 3D information through fast network inference or 3D Gaussian Splatting, but their results generally suffer from poor multiview consistency and lack geometric detail. To tackle these issues, we present a novel method that seamlessly integrates geometry and perception information without requiring additional model training to reconstruct detailed 3D objects from a single image. Specifically, we incorporate geometry and perception priors to initialize the Gaussian branches and guide their parameter optimization. The geometry prior captures the rough 3D shapes, while the perception prior utilizes the 2D pretrained diffusion model to enhance multiview information. Subsequently, we introduce a stable Score Distillation Sampling for fine-grained prior distillation to ensure effective knowledge transfer. The model is further enhanced by a reprojection-based strategy that enforces depth consistency. Experimental results show that we outperform existing methods on novel view synthesis and 3D reconstruction, demonstrating robust and consistent 3D object generation.", "date": "2025-06-26", "categories": []}
{"id": "2510.17603", "title": "ShapeCraft: LLM Agents for Structured, Textured and Interactive 3D Modeling", "abstract": "3D generation from natural language offers significant potential to reduce expert manual modeling efforts and enhance accessibility to 3D assets. However, existing methods often yield unstructured meshes and exhibit poor interactivity, making them impractical for artistic workflows. To address these limitations, we represent 3D assets as shape programs and introduce ShapeCraft, a novel multi-agent framework for text-to-3D generation. At its core, we propose a Graph-based Procedural Shape (GPS) representation that decomposes complex natural language into a structured graph of sub-tasks, thereby facilitating accurate LLM comprehension and interpretation of spatial relationships and semantic shape details. Specifically, LLM agents hierarchically parse user input to initialize GPS, then iteratively refine procedural modeling and painting to produce structured, textured, and interactive 3D assets. Qualitative and quantitative experiments demonstrate ShapeCraft's superior performance in generating geometrically accurate and semantically rich 3D assets compared to existing LLM-based agents. We further show the versatility of ShapeCraft through examples of animated and user-customized editing, highlighting its potential for broader interactive applications.", "date": "2025-10-20", "categories": []}
{"id": "2411.14384", "title": "Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation and Reconstruction", "abstract": "Existing feedforward image-to-3D methods mainly rely on 2D multi-view diffusion models that cannot guarantee 3D consistency. These methods easily collapse when changing the prompt view direction and mainly handle object-centric cases. In this paper, we propose a novel single-stage 3D diffusion model, DiffusionGS, for object generation and scene reconstruction from a single view. DiffusionGS directly outputs 3D Gaussian point clouds at each timestep to enforce view consistency and allow the model to generate robustly given prompt views of any directions, beyond object-centric inputs. Plus, to improve the capability and generality of DiffusionGS, we scale up 3D training data by developing a scene-object mixed training strategy. Experiments show that DiffusionGS yields improvements of 2.20 dB/23.25 and 1.34 dB/19.16 in PSNR/FID for objects and scenes than the state-of-the-art methods, without depth estimator. Plus, our method enjoys over 5$\\times$ faster speed ($\\sim$6s on an A100 GPU). Our Project page at https://caiyuanhao1998.github.io/project/DiffusionGS/ shows the video and interactive results. The code and models are publicly available at https://github.com/caiyuanhao1998/Open-DiffusionGS", "date": "2024-11-21", "categories": []}
{"id": "2409.02851", "title": "Human-VDM: Learning Single-Image 3D Human Gaussian Splatting from Video Diffusion Models", "abstract": "Generating lifelike 3D humans from a single RGB image remains a challenging task in computer vision, as it requires accurate modeling of geometry, high-quality texture, and plausible unseen parts. Existing methods typically use multi-view diffusion models for 3D generation, but they often face inconsistent view issues, which hinder high-quality 3D human generation. To address this, we propose Human-VDM, a novel method for generating 3D human from a single RGB image using Video Diffusion Models. Human-VDM provides temporally consistent views for 3D human generation using Gaussian Splatting. It consists of three modules: a view-consistent human video diffusion module, a video augmentation module, and a Gaussian Splatting module. First, a single image is fed into a human video diffusion module to generate a coherent human video. Next, the video augmentation module applies super-resolution and video interpolation to enhance the textures and geometric smoothness of the generated video. Finally, the 3D Human Gaussian Splatting module learns lifelike humans under the guidance of these high-resolution and view-consistent images. Experiments demonstrate that Human-VDM achieves high-quality 3D human from a single image, outperforming state-of-the-art methods in both generation quality and quantity. Project page: https://human-vdm.github.io/Human-VDM/", "date": "2024-09-04", "categories": []}
{"id": "2510.08656", "title": "A 3D Generation Framework from Cross Modality to Parameterized Primitive", "abstract": "Recent advancements in AI-driven 3D model generation have leveraged cross modality, yet generating models with smooth surfaces and minimizing storage overhead remain challenges. This paper introduces a novel multi-stage framework for generating 3D models composed of parameterized primitives, guided by textual and image inputs. In the framework, A model generation algorithm based on parameterized primitives, is proposed, which can identifies the shape features of the model constituent elements, and replace the elements with parameterized primitives with high quality surface. In addition, a corresponding model storage method is proposed, it can ensure the original surface quality of the model, while retaining only the parameters of parameterized primitives. Experiments on virtual scene dataset and real scene dataset demonstrate the effectiveness of our method, achieving a Chamfer Distance of 0.003092, a VIoU of 0.545, a F1-Score of 0.9139 and a NC of 0.8369, with primitive parameter files approximately 6KB in size. Our approach is particularly suitable for rapid prototyping of simple models.", "date": "2025-10-09", "categories": []}
{"id": "2503.16278", "title": "Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modeling", "abstract": "3D structure modeling is essential across scales, enabling applications from fluid simulation and 3D reconstruction to protein folding and molecular docking. Yet, despite shared 3D spatial patterns, current approaches remain fragmented, with models narrowly specialized for specific domains and unable to generalize across tasks or scales. We propose Uni-3DAR, a unified autoregressive framework for cross-scale 3D generation and understanding. At its core is a coarse-to-fine tokenizer based on octree data structures, which compresses diverse 3D structures into compact 1D token sequences. We further propose a two-level subtree compression strategy, which reduces the octree token sequence by up to 8x. To address the challenge of dynamically varying token positions introduced by compression, we introduce a masked next-token prediction strategy that ensures accurate positional modeling, significantly boosting model performance. Extensive experiments across multiple 3D generation and understanding tasks, including small molecules, proteins, polymers, crystals, and macroscopic 3D objects, validate its effectiveness and versatility. Notably, Uni-3DAR surpasses previous state-of-the-art diffusion models by a substantial margin, achieving up to 256\\% relative improvement while delivering inference speeds up to 21.8x faster.", "date": "2025-03-20", "categories": []}
{"id": "2510.06967", "title": "Generating Surface for Text-to-3D using 2D Gaussian Splatting", "abstract": "Recent advancements in Text-to-3D modeling have shown significant potential for the creation of 3D content. However, due to the complex geometric shapes of objects in the natural world, generating 3D content remains a challenging task. Current methods either leverage 2D diffusion priors to recover 3D geometry, or train the model directly based on specific 3D representations. In this paper, we propose a novel method named DirectGaussian, which focuses on generating the surfaces of 3D objects represented by surfels. In DirectGaussian, we utilize conditional text generation models and the surface of a 3D object is rendered by 2D Gaussian splatting with multi-view normal and texture priors. For multi-view geometric consistency problems, DirectGaussian incorporates curvature constraints on the generated surface during optimization process. Through extensive experiments, we demonstrate that our framework is capable of achieving diverse and high-fidelity 3D content creation.", "date": "2025-10-08", "categories": []}
{"id": "2510.06208", "title": "ShapeGen4D: Towards High Quality 4D Shape Generation from Videos", "abstract": "Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. In this work, we introduce a native video-to-4D shape generation framework that synthesizes a single dynamic 3D representation end-to-end from the video. Our framework introduces three key components based on large-scale pre-trained 3D models: (i) a temporal attention that conditions generation on all frames while producing a time-indexed dynamic representation; (ii) a time-aware point sampling and 4D latent anchoring that promote temporally consistent geometry and texture; and (iii) noise sharing across frames to enhance temporal stability. Our method accurately captures non-rigid motion, volume changes, and even topological transitions without per-frame optimization. Across diverse in-the-wild videos, our method improves robustness and perceptual fidelity and reduces failure modes compared with the baselines.", "date": "2025-10-07", "categories": []}
{"id": "2503.11981", "title": "DecompDreamer: A Composition-Aware Curriculum for Structured 3D Asset Generation", "abstract": "Current text-to-3D methods excel at generating single objects but falter on compositional prompts. We argue this failure is fundamental to their optimization schedules, as simultaneous or iterative heuristics predictably collapse under a combinatorial explosion of conflicting gradients, leading to entangled geometry or catastrophic divergence. In this paper, we reframe the core challenge of compositional generation as one of optimization scheduling. We introduce DecompDreamer, a framework built on a novel staged optimization strategy that functions as an implicit curriculum. Our method first establishes a coherent structural scaffold by prioritizing inter-object relationships before shifting to the high-fidelity refinement of individual components. This temporal decoupling of competing objectives provides a robust solution to gradient conflict. Qualitative and quantitative evaluations on diverse compositional prompts demonstrate that DecompDreamer outperforms state-of-the-art methods in fidelity, disentanglement, and spatial coherence.", "date": "2025-03-15", "categories": []}
{"id": "2505.20431", "title": "ART-DECO: Arbitrary Text Guidance for 3D Detailizer Construction", "abstract": "We introduce a 3D detailizer, a neural model which can instantaneously (in <1s) transform a coarse 3D shape proxy into a high-quality asset with detailed geometry and texture as guided by an input text prompt. Our model is trained using the text prompt, which defines the shape class and characterizes the appearance and fine-grained style of the generated details. The coarse 3D proxy, which can be easily varied and adjusted (e.g., via user editing), provides structure control over the final shape. Importantly, our detailizer is not optimized for a single shape; it is the result of distilling a generative model, so that it can be reused, without retraining, to generate any number of shapes, with varied structures, whose local details all share a consistent style and appearance. Our detailizer training utilizes a pretrained multi-view image diffusion model, with text conditioning, to distill the foundational knowledge therein into our detailizer via Score Distillation Sampling (SDS). To improve SDS and enable our detailizer architecture to learn generalizable features over complex structures, we train our model in two training stages to generate shapes with increasing structural complexity. Through extensive experiments, we show that our method generates shapes of superior quality and details compared to existing text-to-3D models under varied structure control. Our detailizer can refine a coarse shape in less than a second, making it possible to interactively author and adjust 3D shapes. Furthermore, the user-imposed structure control can lead to creative, and hence out-of-distribution, 3D asset generations that are beyond the current capabilities of leading text-to-3D generative models. We demonstrate an interactive 3D modeling workflow our method enables, and its strong generalizability over styles, structures, and object categories.", "date": "2025-05-26", "categories": []}
{"id": "2509.23709", "title": "StrucADT: Generating Structure-controlled 3D Point Clouds with Adjacency Diffusion Transformer", "abstract": "In the field of 3D point cloud generation, numerous 3D generative models have demonstrated the ability to generate diverse and realistic 3D shapes. However, the majority of these approaches struggle to generate controllable 3D point cloud shapes that meet user-specific requirements, hindering the large-scale application of 3D point cloud generation. To address the challenge of lacking control in 3D point cloud generation, we are the first to propose controlling the generation of point clouds by shape structures that comprise part existences and part adjacency relationships. We manually annotate the adjacency relationships between the segmented parts of point cloud shapes, thereby constructing a StructureGraph representation. Based on this StructureGraph representation, we introduce StrucADT, a novel structure-controllable point cloud generation model, which consists of StructureGraphNet module to extract structure-aware latent features, cCNF Prior module to learn the distribution of the latent features controlled by the part adjacency, and Diffusion Transformer module conditioned on the latent features and part adjacency to generate structure-consistent point cloud shapes. Experimental results demonstrate that our structure-controllable 3D point cloud generation method produces high-quality and diverse point cloud shapes, enabling the generation of controllable point clouds based on user-specified shape structures and achieving state-of-the-art performance in controllable point cloud generation on the ShapeNet dataset.", "date": "2025-09-28", "categories": []}
{"id": "2509.25079", "title": "UniLat3D: Geometry-Appearance Unified Latents for Single-Stage 3D Generation", "abstract": "High-fidelity 3D asset generation is crucial for various industries. While recent 3D pretrained models show strong capability in producing realistic content, most are built upon diffusion models and follow a two-stage pipeline that first generates geometry and then synthesizes appearance. Such a decoupled design tends to produce geometry-texture misalignment and non-negligible cost. In this paper, we propose UniLat3D, a unified framework that encodes geometry and appearance in a single latent space, enabling direct single-stage generation. Our key contribution is a geometry-appearance Unified VAE, which compresses high-resolution sparse features into a compact latent representation -- UniLat. UniLat integrates structural and visual information into a dense low-resolution latent, which can be efficiently decoded into diverse 3D formats, e.g., 3D Gaussians and meshes. Based on this unified representation, we train a single flow-matching model to map Gaussian noise directly into UniLat, eliminating redundant stages. Trained solely on public datasets, UniLat3D produces high-quality 3D assets in seconds from a single image, achieving superior appearance fidelity and geometric quality. More demos \\& code are available at https://unilat3d.github.io/", "date": "2025-09-29", "categories": []}
{"id": "2509.20824", "title": "ARMesh: Autoregressive Mesh Generation via Next-Level-of-Detail Prediction", "abstract": "Directly generating 3D meshes, the default representation for 3D shapes in the graphics industry, using auto-regressive (AR) models has become popular these days, thanks to their sharpness, compactness in the generated results, and ability to represent various types of surfaces. However, AR mesh generative models typically construct meshes face by face in lexicographic order, which does not effectively capture the underlying geometry in a manner consistent with human perception. Inspired by 2D models that progressively refine images, such as the prevailing next-scale prediction AR models, we propose generating meshes auto-regressively in a progressive coarse-to-fine manner. Specifically, we view mesh simplification algorithms, which gradually merge mesh faces to build simpler meshes, as a natural fine-to-coarse process. Therefore, we generalize meshes to simplicial complexes and develop a transformer-based AR model to approximate the reverse process of simplification in the order of level of detail, constructing meshes initially from a single point and gradually adding geometric details through local remeshing, where the topology is not predefined and is alterable. Our experiments show that this novel progressive mesh generation approach not only provides intuitive control over generation quality and time consumption by early stopping the auto-regressive process but also enables applications such as mesh refinement and editing.", "date": "2025-09-25", "categories": []}
{"id": "2509.21114", "title": "CHARM: Control-point-based 3D Anime Hairstyle Auto-Regressive Modeling", "abstract": "We present CHARM, a novel parametric representation and generative framework for anime hairstyle modeling. While traditional hair modeling methods focus on realistic hair using strand-based or volumetric representations, anime hairstyle exhibits highly stylized, piecewise-structured geometry that challenges existing techniques. Existing works often rely on dense mesh modeling or hand-crafted spline curves, making them inefficient for editing and unsuitable for scalable learning. CHARM introduces a compact, invertible control-point-based parameterization, where a sequence of control points represents each hair card, and each point is encoded with only five geometric parameters. This efficient and accurate representation supports both artist-friendly design and learning-based generation. Built upon this representation, CHARM introduces an autoregressive generative framework that effectively generates anime hairstyles from input images or point clouds. By interpreting anime hairstyles as a sequential \"hair language\", our autoregressive transformer captures both local geometry and global hairstyle topology, resulting in high-fidelity anime hairstyle creation. To facilitate both training and evaluation of anime hairstyle generation, we construct AnimeHair, a large-scale dataset of 37K high-quality anime hairstyles with separated hair cards and processed mesh data. Extensive experiments demonstrate state-of-the-art performance of CHARM in both reconstruction accuracy and generation quality, offering an expressive and scalable solution for anime hairstyle modeling. Project page: https://hyzcluster.github.io/charm/", "date": "2025-09-25", "categories": []}
{"id": "2509.21245", "title": "Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets", "abstract": "Recent advances in 3D-native generative models have accelerated asset creation for games, film, and design. However, most methods still rely primarily on image or text conditioning and lack fine-grained, cross-modal controls, which limits controllability and practical adoption. To address this gap, we present Hunyuan3D-Omni, a unified framework for fine-grained, controllable 3D asset generation built on Hunyuan3D 2.1. In addition to images, Hunyuan3D-Omni accepts point clouds, voxels, bounding boxes, and skeletal pose priors as conditioning signals, enabling precise control over geometry, topology, and pose. Instead of separate heads for each modality, our model unifies all signals in a single cross-modal architecture. We train with a progressive, difficulty-aware sampling strategy that selects one control modality per example and biases sampling toward harder signals (e.g., skeletal pose) while downweighting easier ones (e.g., point clouds), encouraging robust multi-modal fusion and graceful handling of missing inputs. Experiments show that these additional controls improve generation accuracy, enable geometry-aware transformations, and increase robustness for production workflows.", "date": "2025-09-25", "categories": []}
{"id": "2509.08643", "title": "X-Part: high fidelity and structure coherent shape decomposition", "abstract": "Generating 3D shapes at part level is pivotal for downstream applications such as mesh retopology, UV mapping, and 3D printing. However, existing part-based generation methods often lack sufficient controllability and suffer from poor semantically meaningful decomposition. To this end, we introduce X-Part, a controllable generative model designed to decompose a holistic 3D object into semantically meaningful and structurally coherent parts with high geometric fidelity. X-Part exploits the bounding box as prompts for the part generation and injects point-wise semantic features for meaningful decomposition. Furthermore, we design an editable pipeline for interactive part generation. Extensive experimental results show that X-Part achieves state-of-the-art performance in part-level shape generation. This work establishes a new paradigm for creating production-ready, editable, and structurally sound 3D assets. Codes will be released for public research.", "date": "2025-09-10", "categories": []}
{"id": "2509.16768", "title": "MMPart: Harnessing Multi-Modal Large Language Models for Part-Aware 3D Generation", "abstract": "Generative 3D modeling has advanced rapidly, driven by applications in VR/AR, metaverse, and robotics. However, most methods represent the target object as a closed mesh devoid of any structural information, limiting editing, animation, and semantic understanding. Part-aware 3D generation addresses this problem by decomposing objects into meaningful components, but existing pipelines face challenges: in existing methods, the user has no control over which objects are separated and how model imagine the occluded parts in isolation phase. In this paper, we introduce MMPart, an innovative framework for generating part-aware 3D models from a single image. We first use a VLM to generate a set of prompts based on the input image and user descriptions. In the next step, a generative model generates isolated images of each object based on the initial image and the previous step's prompts as supervisor (which control the pose and guide model how imagine previously occluded areas). Each of those images then enters the multi-view generation stage, where a number of consistent images from different views are generated. Finally, a reconstruction model converts each of these multi-view images into a 3D model.", "date": "2025-09-20", "categories": []}
{"id": "2509.17206", "title": "Guided and Unguided Conditional Diffusion Mechanisms for Structured and Semantically-Aware 3D Point Cloud Generation", "abstract": "Generating realistic 3D point clouds is a fundamental problem in computer vision with applications in remote sensing, robotics, and digital object modeling. Existing generative approaches primarily capture geometry, and when semantics are considered, they are typically imposed post hoc through external segmentation or clustering rather than integrated into the generative process itself. We propose a diffusion-based framework that embeds per-point semantic conditioning directly within generation. Each point is associated with a conditional variable corresponding to its semantic label, which guides the diffusion dynamics and enables the joint synthesis of geometry and semantics. This design produces point clouds that are both structurally coherent and segmentation-aware, with object parts explicitly represented during synthesis. Through a comparative analysis of guided and unguided diffusion processes, we demonstrate the significant impact of conditional variables on diffusion dynamics and generation quality. Extensive experiments validate the efficacy of our approach, producing detailed and accurate 3D point clouds tailored to specific parts and features.", "date": "2025-09-21", "categories": []}
{"id": "2509.15772", "title": "Vision-Language Models as Differentiable Semantic and Spatial Rewards for Text-to-3D Generation", "abstract": "Score Distillation Sampling (SDS) enables high-quality text-to-3D generation by supervising 3D models through the denoising of multi-view 2D renderings, using a pretrained text-to-image diffusion model to align with the input prompt and ensure 3D consistency. However, existing SDS-based methods face two fundamental limitations: (1) their reliance on CLIP-style text encoders leads to coarse semantic alignment and struggles with fine-grained prompts; and (2) 2D diffusion priors lack explicit 3D spatial constraints, resulting in geometric inconsistencies and inaccurate object relationships in multi-object scenes. To address these challenges, we propose VLM3D, a novel text-to-3D generation framework that integrates large vision-language models (VLMs) into the SDS pipeline as differentiable semantic and spatial priors. Unlike standard text-to-image diffusion priors, VLMs leverage rich language-grounded supervision that enables fine-grained prompt alignment. Moreover, their inherent vision language modeling provides strong spatial understanding, which significantly enhances 3D consistency for single-object generation and improves relational reasoning in multi-object scenes. We instantiate VLM3D based on the open-source Qwen2.5-VL model and evaluate it on the GPTeval3D benchmark. Experiments across diverse objects and complex scenes show that VLM3D significantly outperforms prior SDS-based methods in semantic fidelity, geometric coherence, and spatial correctness.", "date": "2025-09-19", "categories": []}
{"id": "2509.13301", "title": "StyleSculptor: Zero-Shot Style-Controllable 3D Asset Generation with Texture-Geometry Dual Guidance", "abstract": "Creating 3D assets that follow the texture and geometry style of existing ones is often desirable or even inevitable in practical applications like video gaming and virtual reality. While impressive progress has been made in generating 3D objects from text or images, creating style-controllable 3D assets remains a complex and challenging problem. In this work, we propose StyleSculptor, a novel training-free approach for generating style-guided 3D assets from a content image and one or more style images. Unlike previous works, StyleSculptor achieves style-guided 3D generation in a zero-shot manner, enabling fine-grained 3D style control that captures the texture, geometry, or both styles of user-provided style images. At the core of StyleSculptor is a novel Style Disentangled Attention (SD-Attn) module, which establishes a dynamic interaction between the input content image and style image for style-guided 3D asset generation via a cross-3D attention mechanism, enabling stable feature fusion and effective style-guided generation. To alleviate semantic content leakage, we also introduce a style-disentangled feature selection strategy within the SD-Attn module, which leverages the variance of 3D feature patches to disentangle style- and content-significant channels, allowing selective feature injection within the attention framework. With SD-Attn, the network can dynamically compute texture-, geometry-, or both-guided features to steer the 3D generation process. Built upon this, we further propose the Style Guided Control (SGC) mechanism, which enables exclusive geometry- or texture-only stylization, as well as adjustable style intensity control. Extensive experiments demonstrate that StyleSculptor outperforms existing baseline methods in producing high-fidelity 3D assets.", "date": "2025-09-16", "categories": []}
{"id": "2509.12501", "title": "Artist-Created Mesh Generation from Raw Observation", "abstract": "We present an end-to-end framework for generating artist-style meshes from noisy or incomplete point clouds, such as those captured by real-world sensors like LiDAR or mobile RGB-D cameras. Artist-created meshes are crucial for commercial graphics pipelines due to their compatibility with animation and texturing tools and their efficiency in rendering. However, existing approaches often assume clean, complete inputs or rely on complex multi-stage pipelines, limiting their applicability in real-world scenarios. To address this, we propose an end-to-end method that refines the input point cloud and directly produces high-quality, artist-style meshes. At the core of our approach is a novel reformulation of 3D point cloud refinement as a 2D inpainting task, enabling the use of powerful generative models. Preliminary results on the ShapeNet dataset demonstrate the promise of our framework in producing clean, complete meshes.", "date": "2025-09-15", "categories": []}
{"id": "2509.12721", "title": "SPGen: Spherical Projection as Consistent and Flexible Representation for Single Image 3D Shape Generation", "abstract": "Existing single-view 3D generative models typically adopt multiview diffusion priors to reconstruct object surfaces, yet they remain prone to inter-view inconsistencies and are unable to faithfully represent complex internal structure or nontrivial topologies. In particular, we encode geometry information by projecting it onto a bounding sphere and unwrapping it into a compact and structural multi-layer 2D Spherical Projection (SP) representation. Operating solely in the image domain, SPGen offers three key advantages simultaneously: (1) Consistency. The injective SP mapping encodes surface geometry with a single viewpoint which naturally eliminates view inconsistency and ambiguity; (2) Flexibility. Multi-layer SP maps represent nested internal structures and support direct lifting to watertight or open 3D surfaces; (3) Efficiency. The image-domain formulation allows the direct inheritance of powerful 2D diffusion priors and enables efficient finetuning with limited computational resources. Extensive experiments demonstrate that SPGen significantly outperforms existing baselines in geometric quality and computational efficiency.", "date": "2025-09-16", "categories": []}
{"id": "2509.12815", "title": "Hunyuan3D Studio: End-to-End AI Pipeline for Game-Ready 3D Asset Generation", "abstract": "The creation of high-quality 3D assets, a cornerstone of modern game development, has long been characterized by labor-intensive and specialized workflows. This paper presents Hunyuan3D Studio, an end-to-end AI-powered content creation platform designed to revolutionize the game production pipeline by automating and streamlining the generation of game-ready 3D assets. At its core, Hunyuan3D Studio integrates a suite of advanced neural modules (such as Part-level 3D Generation, Polygon Generation, Semantic UV, etc.) into a cohesive and user-friendly system. This unified framework allows for the rapid transformation of a single concept image or textual description into a fully-realized, production-quality 3D model complete with optimized geometry and high-fidelity PBR textures. We demonstrate that assets generated by Hunyuan3D Studio are not only visually compelling but also adhere to the stringent technical requirements of contemporary game engines, significantly reducing iteration time and lowering the barrier to entry for 3D content creation. By providing a seamless bridge from creative intent to technical asset, Hunyuan3D Studio represents a significant leap forward for AI-assisted workflows in game development and interactive media.", "date": "2025-09-16", "categories": []}
{"id": "2412.08641", "title": "3D Mesh Editing using Masked LRMs", "abstract": "We present a novel approach to shape editing, building on recent progress in 3D reconstruction from multi-view images. We formulate shape editing as a conditional reconstruction problem, where the model must reconstruct the input shape with the exception of a specified 3D region, in which the geometry should be generated from the conditional signal. To this end, we train a conditional Large Reconstruction Model (LRM) for masked reconstruction, using multi-view consistent masks rendered from a randomly generated 3D occlusion, and using one clean viewpoint as the conditional signal. During inference, we manually define a 3D region to edit and provide an edited image from a canonical viewpoint to fill that region. We demonstrate that, in just a single forward pass, our method not only preserves the input geometry in the unmasked region through reconstruction capabilities on par with SoTA, but is also expressive enough to perform a variety of mesh edits from a single image guidance that past works struggle with, while being 2-10x faster than the top-performing prior work.", "date": "2024-12-11", "categories": []}
{"id": "2509.07010", "title": "Human-in-the-Loop: Quantitative Evaluation of 3D Models Generation by Large Language Models", "abstract": "Large Language Models are increasingly capable of interpreting multimodal inputs to generate complex 3D shapes, yet robust methods to evaluate geometric and structural fidelity remain underdeveloped. This paper introduces a human in the loop framework for the quantitative evaluation of LLM generated 3D models, supporting applications such as democratization of CAD design, reverse engineering of legacy designs, and rapid prototyping. We propose a comprehensive suite of similarity and complexity metrics, including volumetric accuracy, surface alignment, dimensional fidelity, and topological intricacy, to benchmark generated models against ground truth CAD references. Using an L bracket component as a case study, we systematically compare LLM performance across four input modalities: 2D orthographic views, isometric sketches, geometric structure trees, and code based correction prompts. Our findings demonstrate improved generation fidelity with increased semantic richness, with code level prompts achieving perfect reconstruction across all metrics. A key contribution of this work is demonstrating that our proposed quantitative evaluation approach enables significantly faster convergence toward the ground truth, especially compared to traditional qualitative methods based solely on visual inspection and human intuition. This work not only advances the understanding of AI assisted shape synthesis but also provides a scalable methodology to validate and refine generative models for diverse CAD applications.", "date": "2025-09-06", "categories": []}
{"id": "2509.04406", "title": "Few-step Flow for 3D Generation via Marginal-Data Transport Distillation", "abstract": "Flow-based 3D generation models typically require dozens of sampling steps during inference. Though few-step distillation methods, particularly Consistency Models (CMs), have achieved substantial advancements in accelerating 2D diffusion models, they remain under-explored for more complex 3D generation tasks. In this study, we propose a novel framework, MDT-dist, for few-step 3D flow distillation. Our approach is built upon a primary objective: distilling the pretrained model to learn the Marginal-Data Transport. Directly learning this objective needs to integrate the velocity fields, while this integral is intractable to be implemented. Therefore, we propose two optimizable objectives, Velocity Matching (VM) and Velocity Distillation (VD), to equivalently convert the optimization target from the transport level to the velocity and the distribution level respectively. Velocity Matching (VM) learns to stably match the velocity fields between the student and the teacher, but inevitably provides biased gradient estimates. Velocity Distillation (VD) further enhances the optimization process by leveraging the learned velocity fields to perform probability density distillation. When evaluated on the pioneer 3D generation framework TRELLIS, our method reduces sampling steps of each flow transformer from 25 to 1 or 2, achieving 0.68s (1 step x 2) and 0.94s (2 steps x 2) latency with 9.0x and 6.5x speedup on A800, while preserving high visual and geometric fidelity. Extensive experiments demonstrate that our method significantly outperforms existing CM distillation methods, and enables TRELLIS to achieve superior performance in few-step 3D generation.", "date": "2025-09-04", "categories": []}
{"id": "2509.02466", "title": "TeRA: Rethinking Text-guided Realistic 3D Avatar Generation", "abstract": "In this paper, we rethink text-to-avatar generative models by proposing TeRA, a more efficient and effective framework than the previous SDS-based models and general large 3D generative models. Our approach employs a two-stage training strategy for learning a native 3D avatar generative model. Initially, we distill a decoder to derive a structured latent space from a large human reconstruction model. Subsequently, a text-controlled latent diffusion model is trained to generate photorealistic 3D human avatars within this latent space. TeRA enhances the model performance by eliminating slow iterative optimization and enables text-based partial customization through a structured 3D human representation. Experiments have proven our approach's superiority over previous text-to-avatar generative models in subjective and objective evaluation.", "date": "2025-09-02", "categories": []}
{"id": "2403.12470", "title": "SC-Diff: 3D Shape Completion with Latent Diffusion Models", "abstract": "We present a novel 3D shape completion framework that unifies multimodal conditioning, leveraging both 2D images and 3D partial scans through a latent diffusion model. Shapes are represented as Truncated Signed Distance Functions (TSDFs) and encoded into a discrete latent space jointly supervised by 2D and 3D cues, enabling efficient high-resolution processing while reducing GPU memory usage by 30\\% compared to state-of-the-art methods. Our approach guides the generation process with flexible multimodal conditioning, ensuring consistent integration of 2D and 3D information from encoding to reconstruction. Our training strategy simulates realistic partial observations, avoiding assumptions about input structure and improving robustness in real-world scenarios. Leveraging our efficient latent space and multimodal conditioning, our model generalizes across object categories, outperforming class-specific models by 12\\% and class-agnostic models by 47\\% in $l_1$ reconstruction error, while producing more diverse, realistic, and high-fidelity completions than prior approaches.", "date": "2024-03-19", "categories": []}
{"id": "2407.01074", "title": "Multimodal Conditional 3D Face Geometry Generation", "abstract": "We present a new method for multimodal conditional 3D face geometry generation that allows user-friendly control over the output identity and expression via a number of different conditioning signals. Within a single model, we demonstrate 3D faces generated from artistic sketches, portrait photos, Canny edges, FLAME face model parameters, 2D face landmarks, or text prompts. Our approach is based on a diffusion process that generates 3D geometry in a 2D parameterized UV domain. Geometry generation passes each conditioning signal through a set of cross-attention layers (IP-Adapter), one set for each user-defined conditioning signal. The result is an easy-to-use 3D face generation tool that produces topology-consistent, high-quality geometry with fine-grain user control.", "date": "2024-07-01", "categories": []}
{"id": "2509.01492", "title": "A Continuous-Time Consistency Model for 3D Point Cloud Generation", "abstract": "Fast and accurate 3D shape generation from point clouds is essential for applications in robotics, AR/VR, and digital content creation. We introduce ConTiCoM-3D, a continuous-time consistency model that synthesizes 3D shapes directly in point space, without discretized diffusion steps, pre-trained teacher models, or latent-space encodings. The method integrates a TrigFlow-inspired continuous noise schedule with a Chamfer Distance-based geometric loss, enabling stable training on high-dimensional point sets while avoiding expensive Jacobian-vector products. This design supports efficient one- to two-step inference with high geometric fidelity. In contrast to previous approaches that rely on iterative denoising or latent decoders, ConTiCoM-3D employs a time-conditioned neural network operating entirely in continuous time, thereby achieving fast generation. Experiments on the ShapeNet benchmark show that ConTiCoM-3D matches or outperforms state-of-the-art diffusion and latent consistency models in both quality and efficiency, establishing it as a practical framework for scalable 3D shape generation.", "date": "2025-09-01", "categories": []}
{"id": "2509.02357", "title": "Category-Aware 3D Object Composition with Disentangled Texture and Shape Multi-view Diffusion", "abstract": "In this paper, we tackle a new task of 3D object synthesis, where a 3D model is composited with another object category to create a novel 3D model. However, most existing text/image/3D-to-3D methods struggle to effectively integrate multiple content sources, often resulting in inconsistent textures and inaccurate shapes. To overcome these challenges, we propose a straightforward yet powerful approach, category+3D-to-3D (C33D), for generating novel and structurally coherent 3D models. Our method begins by rendering multi-view images and normal maps from the input 3D model, then generating a novel 2D object using adaptive text-image harmony (ATIH) with the front-view image and a text description from another object category as inputs. To ensure texture consistency, we introduce texture multi-view diffusion, which refines the textures of the remaining multi-view RGB images based on the novel 2D object. For enhanced shape accuracy, we propose shape multi-view diffusion to improve the 2D shapes of both the multi-view RGB images and the normal maps, also conditioned on the novel 2D object. Finally, these outputs are used to reconstruct a complete and novel 3D model. Extensive experiments demonstrate the effectiveness of our method, yielding impressive 3D creations, such as shark(3D)-crocodile(text) in the first row of Fig. 1. A project page is available at: https://xzr52.github.io/C33D/", "date": "2025-09-02", "categories": []}
{"id": "2509.02474", "title": "Unifi3D: A Study on 3D Representations for Generation and Reconstruction in a Common Framework", "abstract": "Following rapid advancements in text and image generation, research has increasingly shifted towards 3D generation. Unlike the well-established pixel-based representation in images, 3D representations remain diverse and fragmented, encompassing a wide variety of approaches such as voxel grids, neural radiance fields, signed distance functions, point clouds, or octrees, each offering distinct advantages and limitations. In this work, we present a unified evaluation framework designed to assess the performance of 3D representations in reconstruction and generation. We compare these representations based on multiple criteria: quality, computational efficiency, and generalization performance. Beyond standard model benchmarking, our experiments aim to derive best practices over all steps involved in the 3D generation pipeline, including preprocessing, mesh reconstruction, compression with autoencoders, and generation. Our findings highlight that reconstruction errors significantly impact overall performance, underscoring the need to evaluate generation and reconstruction jointly. We provide insights that can inform the selection of suitable 3D models for various applications, facilitating the development of more robust and application-specific solutions in 3D generation. The code for our framework is available at https://github.com/isl-org/unifi3d.", "date": "2025-09-02", "categories": []}
{"id": "2503.22677", "title": "DSO: Aligning 3D Generators with Simulation Feedback for Physical Soundness", "abstract": "Most 3D object generators prioritize aesthetic quality, often neglecting the physical constraints necessary for practical applications. One such constraint is that a 3D object should be self-supporting, i.e., remain balanced under gravity. Previous approaches to generating stable 3D objects relied on differentiable physics simulators to optimize geometry at test time, which is slow, unstable, and prone to local optima. Inspired by the literature on aligning generative models with external feedback, we propose Direct Simulation Optimization (DSO). This framework leverages feedback from a (non-differentiable) simulator to increase the likelihood that the 3D generator directly outputs stable 3D objects. We construct a dataset of 3D objects labeled with stability scores obtained from the physics simulator. This dataset enables fine-tuning of the 3D generator using the stability score as an alignment metric, via direct preference optimization (DPO) or direct reward optimization (DRO) - a novel objective we introduce to align diffusion models without requiring pairwise preferences. Our experiments demonstrate that the fine-tuned feed-forward generator, using either the DPO or DRO objective, is significantly faster and more likely to produce stable objects than test-time optimization. Notably, the DSO framework functions even without any ground-truth 3D objects for training, allowing the 3D generator to self-improve by automatically collecting simulation feedback on its own outputs.", "date": "2025-03-28", "categories": []}
{"id": "2504.08419", "title": "GeoTexBuild: 3D Building Model Generation from Map Footprints", "abstract": "We introduce GeoTexBuild, a modular generative framework for creating 3D building models from footprints derived from site planning or map designs. The system is designed for architects and city planners, offering a seamless solution that directly converts map features into 3D buildings. The proposed framework employs a three-stage process comprising height map generation, geometry reconstruction, and appearance stylization, culminating in building models with detailed geometry and appearance attributes. By integrating customized ControlNet, Neural style field (NSF), and Multi-view diffusion model, we explore effective methods for controlling both geometric and visual attributes during the generation process. Our approach eliminates the problem of structural variations in a single facade image in existing 3D generation techniques for buildings. Experimental results at each stage validate the capability of GeoTexBuild to generate detailed and accurate building models from footprints.", "date": "2025-04-11", "categories": []}
{"id": "2508.20470", "title": "Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation", "abstract": "Scaling laws have validated the success and promise of large-data-trained models in creative generation across text, image, and video domains. However, this paradigm faces data scarcity in the 3D domain, as there is far less of it available on the internet compared to the aforementioned modalities. Fortunately, there exist adequate videos that inherently contain commonsense priors, offering an alternative supervisory signal to mitigate the generalization bottleneck caused by limited native 3D data. On the one hand, videos capturing multiple views of an object or scene provide a spatial consistency prior for 3D generation. On the other hand, the rich semantic information contained within the videos enables the generated content to be more faithful to the text prompts and semantically plausible. This paper explores how to apply the video modality in 3D asset generation, spanning datasets to models. We introduce Droplet3D-4M, the first large-scale video dataset with multi-view level annotations, and train Droplet3D, a generative model supporting both image and dense text input. Extensive experiments validate the effectiveness of our approach, demonstrating its ability to produce spatially consistent and semantically plausible content. Moreover, in contrast to the prevailing 3D solutions, our approach exhibits the potential for extension to scene-level applications. This indicates that the commonsense priors from the videos significantly facilitate 3D creation. We have open-sourced all resources including the dataset, code, technical framework, and model weights: https://dropletx.github.io/.", "date": "2025-08-28", "categories": []}
{"id": "2508.18531", "title": "SAT-SKYLINES: 3D Building Generation from Satellite Imagery and Coarse Geometric Priors", "abstract": "We present SatSkylines, a 3D building generation approach that takes satellite imagery and coarse geometric priors. Without proper geometric guidance, existing image-based 3D generation methods struggle to recover accurate building structures from the top-down views of satellite images alone. On the other hand, 3D detailization methods tend to rely heavily on highly detailed voxel inputs and fail to produce satisfying results from simple priors such as cuboids. To address these issues, our key idea is to model the transformation from interpolated noisy coarse priors to detailed geometries, enabling flexible geometric control without additional computational cost. We have further developed Skylines-50K, a large-scale dataset of over 50,000 unique and stylized 3D building assets in order to support the generations of detailed building models. Extensive evaluations indicate the effectiveness of our model and strong generalization ability.", "date": "2025-08-25", "categories": []}
{"id": "2503.10660", "title": "Text-to-3D Generation using Jensen-Shannon Score Distillation", "abstract": "Score distillation sampling is an effective technique to generate 3D models from text prompts, utilizing pre-trained large-scale text-to-image diffusion models as guidance. However, the produced 3D assets tend to be over-saturating, over-smoothing, with limited diversity. These issues are results from a reverse Kullback-Leibler (KL) divergence objective, which makes the optimization unstable and results in mode-seeking behavior. In this paper, we derive a bounded score distillation objective based on Jensen-Shannon divergence (JSD), which stabilizes the optimization process and produces high-quality 3D generation. JSD can match well generated and target distribution, therefore mitigating mode seeking. We provide a practical implementation of JSD by utilizing the theory of generative adversarial networks to define an approximate objective function for the generator, assuming the discriminator is well trained. By assuming the discriminator following a log-odds classifier, we propose a minority sampling algorithm to estimate the gradients of our proposed objective, providing a practical implementation for JSD. We conduct both theoretical and empirical studies to validate our method. Experimental results on T3Bench demonstrate that our method can produce high-quality and diversified 3D assets.", "date": "2025-03-08", "categories": []}
{"id": "2502.00360", "title": "Shape from Semantics: 3D Shape Generation from Multi-View Semantics", "abstract": "Existing 3D reconstruction methods utilize guidances such as 2D images, 3D point clouds, shape contours and single semantics to recover the 3D surface, which limits the creative exploration of 3D modeling. In this paper, we propose a novel 3D modeling task called ``Shape from Semantics'', which aims to create 3D models whose geometry and appearance are consistent with the given text semantics when viewed from different views. The reconstructed 3D models incorporate more than one semantic elements and are easy for observers to distinguish. We adopt generative models as priors and disentangle the connection between geometry and appearance to solve this challenging problem. Specifically, we propose Local Geometry-Aware Distillation (LGAD), a strategy that employs multi-view normal-depth diffusion priors to complete partial geometries, ensuring realistic shape generation. We also integrate view-adaptive guidance scales to enable smooth semantic transitions across views. For appearance modeling, we adopt physically based rendering to generate high-quality material properties, which are subsequently baked into fabricable meshes. Extensive experimental results demonstrate that our method can generate meshes with well-structured, intricately detailed geometries, coherent textures, and smooth transitions, resulting in visually appealing 3D shape designs. Project page: https://shapefromsemantics.github.io", "date": "2025-02-01", "categories": []}
{"id": "2508.11106", "title": "HierOctFusion: Multi-scale Octree-based 3D Shape Generation via Part-Whole-Hierarchy Message Passing", "abstract": "3D content generation remains a fundamental yet challenging task due to the inherent structural complexity of 3D data. While recent octree-based diffusion models offer a promising balance between efficiency and quality through hierarchical generation, they often overlook two key insights: 1) existing methods typically model 3D objects as holistic entities, ignoring their semantic part hierarchies and limiting generalization; and 2) holistic high-resolution modeling is computationally expensive, whereas real-world objects are inherently sparse and hierarchical, making them well-suited for layered generation. Motivated by these observations, we propose HierOctFusion, a part-aware multi-scale octree diffusion model that enhances hierarchical feature interaction for generating fine-grained and sparse object structures. Furthermore, we introduce a cross-attention conditioning mechanism that injects part-level information into the generation process, enabling semantic features to propagate effectively across hierarchical levels from parts to the whole. Additionally, we construct a 3D dataset with part category annotations using a pre-trained segmentation model to facilitate training and evaluation. Experiments demonstrate that HierOctFusion achieves superior shape quality and efficiency compared to prior methods.", "date": "2025-08-14", "categories": []}
{"id": "2412.02287", "title": "Improving Viewpoint Consistency in 3D Generation via Structure Feature and CLIP Guidance", "abstract": "Despite recent advances in text-to-3D generation techniques, current methods often suffer from geometric inconsistencies, commonly referred to as the Janus Problem. This paper identifies the root cause of the Janus Problem: viewpoint generation bias in diffusion models, which creates a significant gap between the actual generated viewpoint and the expected one required for optimizing the 3D model. To address this issue, we propose a tuning-free approach called the Attention and CLIP Guidance (ACG) mechanism. ACG enhances desired viewpoints by adaptively controlling cross-attention maps, employs CLIP-based view-text similarities to filter out erroneous viewpoints, and uses a coarse-to-fine optimization strategy with staged prompts to progressively refine 3D generation. Extensive experiments demonstrate that our method significantly reduces the Janus Problem without compromising generation speed, establishing ACG as an efficient, plug-and-play component for existing text-to-3D frameworks.", "date": "2024-12-03", "categories": []}
{"id": "2501.14317", "title": "Nautilus: Locality-aware Autoencoder for Scalable Mesh Generation", "abstract": "Triangle meshes are fundamental to 3D applications, enabling efficient modification and rasterization while maintaining compatibility with standard rendering pipelines. However, current automatic mesh generation methods typically rely on intermediate representations that lack the continuous surface quality inherent to meshes. Converting these representations into meshes produces dense, suboptimal outputs. Although recent autoregressive approaches demonstrate promise in directly modeling mesh vertices and faces, they are constrained by the limitation in face count, scalability, and structural fidelity. To address these challenges, we propose Nautilus, a locality-aware autoencoder for artist-like mesh generation that leverages the local properties of manifold meshes to achieve structural fidelity and efficient representation. Our approach introduces a novel tokenization algorithm that preserves face proximity relationships and compresses sequence length through locally shared vertices and edges, enabling the generation of meshes with an unprecedented scale of up to 5,000 faces. Furthermore, we develop a Dual-stream Point Conditioner that provides multi-scale geometric guidance, ensuring global consistency and local structural fidelity by capturing fine-grained geometric features. Extensive experiments demonstrate that Nautilus significantly outperforms state-of-the-art methods in both fidelity and scalability. The project page is at https://nautilusmeshgen.github.io.", "date": "2025-01-24", "categories": []}
{"id": "2504.14975", "title": "Cyc3D: Fine-grained Controllable 3D Generation via Cycle Consistency Regularization", "abstract": "Despite the remarkable progress of 3D generation, achieving controllability, i.e., ensuring consistency between generated 3D content and input conditions like edge and depth, remains a significant challenge. Existing methods often struggle to maintain accurate alignment, leading to noticeable discrepancies. To address this issue, we propose \\name{}, a new framework that enhances controllable 3D generation by explicitly encouraging cyclic consistency between the second-order 3D content, generated based on extracted signals from the first-order generation, and its original input controls. Specifically, we employ an efficient feed-forward backbone that can generate a 3D object from an input condition and a text prompt. Given an initial viewpoint and a control signal, a novel view is rendered from the generated 3D content, from which the extracted condition is used to regenerate the 3D content. This re-generated output is then rendered back to the initial viewpoint, followed by another round of control signal extraction, forming a cyclic process with two consistency constraints. \\emph{View consistency} ensures coherence between the two generated 3D objects, measured by semantic similarity to accommodate generative diversity. \\emph{Condition consistency} aligns the final extracted signal with the original input control, preserving structural or geometric details throughout the process. Extensive experiments on popular benchmarks demonstrate that \\name{} significantly improves controllability, especially for fine-grained details, outperforming existing methods across various conditions (e.g., +14.17\\% PSNR for edge, +6.26\\% PSNR for sketch).", "date": "2025-04-21", "categories": []}
{"id": "2411.16185", "title": "Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play Deformation", "abstract": "Generating 3D meshes from a single image is an important but ill-posed task. Existing methods mainly adopt 2D multiview diffusion models to generate intermediate multiview images, and use the Large Reconstruction Model (LRM) to create the final meshes. However, the multiview images exhibit local inconsistencies, and the meshes often lack fidelity to the input image or look blurry. We propose Fancy123, featuring two enhancement modules and an unprojection operation to address the above three issues, respectively. The appearance enhancement module deforms the 2D multiview images to realign misaligned pixels for better multiview consistency. The fidelity enhancement module deforms the 3D mesh to match the input image. The unprojection of the input image and deformed multiview images onto LRM's generated mesh ensures high clarity, discarding LRM's predicted blurry-looking mesh colors. Extensive qualitative and quantitative experiments verify Fancy123's SoTA performance with significant improvement. Also, the two enhancement modules are plug-and-play and work at inference time, allowing seamless integration into various existing single-image-to-3D methods. Code at: https://github.com/YuQiao0303/Fancy123", "date": "2024-11-25", "categories": []}
{"id": "2508.08821", "title": "3DFroMLLM: 3D Prototype Generation only from Pretrained Multimodal LLMs", "abstract": "Recent Multi-Modal Large Language Models (MLLMs) have demonstrated strong capabilities in learning joint representations from text and images. However, their spatial reasoning remains limited. We introduce 3DFroMLLM, a novel framework that enables the generation of 3D object prototypes directly from MLLMs, including geometry and part labels. Our pipeline is agentic, comprising a designer, coder, and visual inspector operating in a refinement loop. Notably, our approach requires no additional training data or detailed user instructions. Building on prior work in 2D generation, we demonstrate that rendered images produced by our framework can be effectively used for image classification pretraining tasks and outperforms previous methods by 15%. As a compelling real-world use case, we show that the generated prototypes can be leveraged to improve fine-grained vision-language models by using the rendered, part-labeled prototypes to fine-tune CLIP for part segmentation and achieving a 55% accuracy improvement without relying on any additional human-labeled data.", "date": "2025-08-12", "categories": []}
{"id": "2508.09062", "title": "VertexRegen: Mesh Generation with Continuous Level of Detail", "abstract": "We introduce VertexRegen, a novel mesh generation framework that enables generation at a continuous level of detail. Existing autoregressive methods generate meshes in a partial-to-complete manner and thus intermediate steps of generation represent incomplete structures. VertexRegen takes inspiration from progressive meshes and reformulates the process as the reversal of edge collapse, i.e. vertex split, learned through a generative model. Experimental results demonstrate that VertexRegen produces meshes of comparable quality to state-of-the-art methods while uniquely offering anytime generation with the flexibility to halt at any step to yield valid meshes with varying levels of detail.", "date": "2025-08-12", "categories": []}
{"id": "2412.16919", "title": "TAR3D: Creating High-Quality 3D Assets via Next-Part Prediction", "abstract": "We present TAR3D, a novel framework that consists of a 3D-aware Vector Quantized-Variational AutoEncoder (VQ-VAE) and a Generative Pre-trained Transformer (GPT) to generate high-quality 3D assets. The core insight of this work is to migrate the multimodal unification and promising learning capabilities of the next-token prediction paradigm to conditional 3D object generation. To achieve this, the 3D VQ-VAE first encodes a wide range of 3D shapes into a compact triplane latent space and utilizes a set of discrete representations from a trainable codebook to reconstruct fine-grained geometries under the supervision of query point occupancy. Then, the 3D GPT, equipped with a custom triplane position embedding called TriPE, predicts the codebook index sequence with prefilling prompt tokens in an autoregressive manner so that the composition of 3D geometries can be modeled part by part. Extensive experiments on ShapeNet and Objaverse demonstrate that TAR3D can achieve superior generation quality over existing methods in text-to-3D and image-to-3D tasks", "date": "2024-12-22", "categories": []}
{"id": "2503.12929", "title": "AR-1-to-3: Single Image to Consistent 3D Object Generation via Next-View Prediction", "abstract": "Novel view synthesis (NVS) is a cornerstone for image-to-3d creation. However, existing works still struggle to maintain consistency between the generated views and the input views, especially when there is a significant camera pose difference, leading to poor-quality 3D geometries and textures. We attribute this issue to their treatment of all target views with equal priority according to our empirical observation that the target views closer to the input views exhibit higher fidelity. With this inspiration, we propose AR-1-to-3, a novel next-view prediction paradigm based on diffusion models that first generates views close to the input views, which are then utilized as contextual information to progressively synthesize farther views. To encode the generated view subsequences as local and global conditions for the next-view prediction, we accordingly develop a stacked local feature encoding strategy (Stacked-LE) and an LSTM-based global feature encoding strategy (LSTM-GE). Extensive experiments demonstrate that our method significantly improves the consistency between the generated views and the input views, producing high-fidelity 3D assets.", "date": "2025-03-17", "categories": []}
{"id": "2504.12684", "title": "SOPHY: Learning to Generate Simulation-Ready Objects with Physical Materials", "abstract": "We present SOPHY, a generative model for 3D physics-aware shape synthesis. Unlike existing 3D generative models that focus solely on static geometry or 4D models that produce physics-agnostic animations, our method jointly synthesizes shape, texture, and material properties related to physics-grounded dynamics, making the generated objects ready for simulations and interactive, dynamic environments. To train our model, we introduce a dataset of 3D objects annotated with detailed physical material attributes, along with an efficient pipeline for material annotation. Our method enables applications such as text-driven generation of interactive, physics-aware 3D objects and single-image reconstruction of physically plausible shapes. Furthermore, our experiments show that jointly modeling shape and material properties enhances the realism and fidelity of the generated shapes, improving performance on both generative geometry and physical plausibility.", "date": "2025-04-17", "categories": []}
{"id": "2508.08228", "title": "LL3M: Large Language 3D Modelers", "abstract": "We present LL3M, a multi-agent system that leverages pretrained large language models (LLMs) to generate 3D assets by writing interpretable Python code in Blender. We break away from the typical generative approach that learns from a collection of 3D data. Instead, we reformulate shape generation as a code-writing task, enabling greater modularity, editability, and integration with artist workflows. Given a text prompt, LL3M coordinates a team of specialized LLM agents to plan, retrieve, write, debug, and refine Blender scripts that generate and edit geometry and appearance. The generated code works as a high-level, interpretable, human-readable, well-documented representation of scenes and objects, making full use of sophisticated Blender constructs (e.g. B-meshes, geometry modifiers, shader nodes) for diverse, unconstrained shapes, materials, and scenes. This code presents many avenues for further agent and human editing and experimentation via code tweaks or procedural parameters. This medium naturally enables a co-creative loop in our system: agents can automatically self-critique using code and visuals, while iterative user instructions provide an intuitive way to refine assets. A shared code context across agents enables awareness of previous attempts, and a retrieval-augmented generation knowledge base built from Blender API documentation, BlenderRAG, equips agents with examples, types, and functions empowering advanced modeling operations and code correctness. We demonstrate the effectiveness of LL3M across diverse shape categories, style and material edits, and user-driven refinements. Our experiments showcase the power of code as a generative and interpretable medium for 3D asset creation. Our project page is at https://threedle.github.io/ll3m.", "date": "2025-08-11", "categories": []}
{"id": "2503.15877", "title": "Repurposing 2D Diffusion Models with Gaussian Atlas for 3D Generation", "abstract": "Recent advances in text-to-image diffusion models have been driven by the increasing availability of paired 2D data. However, the development of 3D diffusion models has been hindered by the scarcity of high-quality 3D data, resulting in less competitive performance compared to their 2D counterparts. To address this challenge, we propose repurposing pre-trained 2D diffusion models for 3D object generation. We introduce Gaussian Atlas, a novel representation that utilizes dense 2D grids, enabling the fine-tuning of 2D diffusion models to generate 3D Gaussians. Our approach demonstrates successful transfer learning from a pre-trained 2D diffusion model to a 2D manifold flattened from 3D structures. To support model training, we compile GaussianVerse, a large-scale dataset comprising 205K high-quality 3D Gaussian fittings of various 3D objects. Our experimental results show that text-to-image diffusion models can be effectively adapted for 3D content generation, bridging the gap between 2D and 3D modeling.", "date": "2025-03-20", "categories": []}
{"id": "2507.23777", "title": "XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding", "abstract": "Current auto-regressive models can generate high-quality, topologically precise meshes; however, they necessitate thousands-or even tens of thousands-of next-token predictions during inference, resulting in substantial latency. We introduce XSpecMesh, a quality-preserving acceleration method for auto-regressive mesh generation models. XSpecMesh employs a lightweight, multi-head speculative decoding scheme to predict multiple tokens in parallel within a single forward pass, thereby accelerating inference. We further propose a verification and resampling strategy: the backbone model verifies each predicted token and resamples any tokens that do not meet the quality criteria. In addition, we propose a distillation strategy that trains the lightweight decoding heads by distilling from the backbone model, encouraging their prediction distributions to align and improving the success rate of speculative predictions. Extensive experiments demonstrate that our method achieves a 1.7x speedup without sacrificing generation quality. Our code will be released.", "date": "2025-07-31", "categories": []}
{"id": "2508.03925", "title": "Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model", "abstract": "We propose a diffusion model designed to generate point-based shape representations with correspondences. Traditional statistical shape models have considered point correspondences extensively, but current deep learning methods do not take them into account, focusing on unordered point clouds instead. Current deep generative models for point clouds do not address generating shapes with point correspondences between generated shapes. This work aims to formulate a diffusion model that is capable of generating realistic point-based shape representations, which preserve point correspondences that are present in the training data. Using shape representation data with correspondences derived from Open Access Series of Imaging Studies 3 (OASIS-3), we demonstrate that our correspondence-preserving model effectively generates point-based hippocampal shape representations that are highly realistic compared to existing methods. We further demonstrate the applications of our generative model by downstream tasks, such as conditional generation of healthy and AD subjects and predicting morphological changes of disease progression by counterfactual generation.", "date": "2025-08-05", "categories": []}
{"id": "2508.01242", "title": "MeshLLM: Empowering Large Language Models to Progressively Understand and Generate 3D Mesh", "abstract": "We present MeshLLM, a novel framework that leverages large language models (LLMs) to understand and generate text-serialized 3D meshes. Our approach addresses key limitations in existing methods, including the limited dataset scale when catering to LLMs' token length and the loss of 3D structural information during mesh serialization. We introduce a Primitive-Mesh decomposition strategy, which divides 3D meshes into structurally meaningful subunits. This enables the creation of a large-scale dataset with 1500k+ samples, almost 50 times larger than previous methods, which aligns better with the LLM scaling law principles. Furthermore, we propose inferring face connectivity from vertices and local mesh assembly training strategies, significantly enhancing the LLMs' ability to capture mesh topology and spatial structures. Experiments show that MeshLLM outperforms the state-of-the-art LLaMA-Mesh in both mesh generation quality and shape understanding, highlighting its great potential in processing text-serialized 3D meshes.", "date": "2025-08-02", "categories": []}
{"id": "2504.02817", "title": "Efficient Autoregressive Shape Generation via Octree-Based Adaptive Tokenization", "abstract": "Many 3D generative models rely on variational autoencoders (VAEs) to learn compact shape representations. However, existing methods encode all shapes into a fixed-size token, disregarding the inherent variations in scale and complexity across 3D data. This leads to inefficient latent representations that can compromise downstream generation. We address this challenge by introducing Octree-based Adaptive Tokenization, a novel framework that adjusts the dimension of latent representations according to shape complexity. Our approach constructs an adaptive octree structure guided by a quadric-error-based subdivision criterion and allocates a shape latent vector to each octree cell using a query-based transformer. Building upon this tokenization, we develop an octree-based autoregressive generative model that effectively leverages these variable-sized representations in shape generation. Extensive experiments demonstrate that our approach reduces token counts by 50% compared to fixed-size methods while maintaining comparable visual quality. When using a similar token length, our method produces significantly higher-quality shapes. When incorporated with our downstream generative model, our method creates more detailed and diverse 3D content than existing approaches.", "date": "2025-04-03", "categories": []}
{"id": "2507.05256", "title": "SegmentDreamer: Towards High-fidelity Text-to-3D Synthesis with Segmented Consistency Trajectory Distillation", "abstract": "Recent advancements in text-to-3D generation improve the visual quality of Score Distillation Sampling (SDS) and its variants by directly connecting Consistency Distillation (CD) to score distillation. However, due to the imbalance between self-consistency and cross-consistency, these CD-based methods inherently suffer from improper conditional guidance, leading to sub-optimal generation results. To address this issue, we present SegmentDreamer, a novel framework designed to fully unleash the potential of consistency models for high-fidelity text-to-3D generation. Specifically, we reformulate SDS through the proposed Segmented Consistency Trajectory Distillation (SCTD), effectively mitigating the imbalance issues by explicitly defining the relationship between self- and cross-consistency. Moreover, SCTD partitions the Probability Flow Ordinary Differential Equation (PF-ODE) trajectory into multiple sub-trajectories and ensures consistency within each segment, which can theoretically provide a significantly tighter upper bound on distillation error. Additionally, we propose a distillation pipeline for a more swift and stable generation. Extensive experiments demonstrate that our SegmentDreamer outperforms state-of-the-art methods in visual quality, enabling high-fidelity 3D asset creation through 3D Gaussian Splatting (3DGS).", "date": "2025-07-07", "categories": []}
{"id": "2508.00558", "title": "Guiding Diffusion-Based Articulated Object Generation by Partial Point Cloud Alignment and Physical Plausibility Constraints", "abstract": "Articulated objects are an important type of interactable objects in everyday environments. In this paper, we propose PhysNAP, a novel diffusion model-based approach for generating articulated objects that aligns them with partial point clouds and improves their physical plausibility. The model represents part shapes by signed distance functions (SDFs). We guide the reverse diffusion process using a point cloud alignment loss computed using the predicted SDFs. Additionally, we impose non-penetration and mobility constraints based on the part SDFs for guiding the model to generate more physically plausible objects. We also make our diffusion approach category-aware to further improve point cloud alignment if category information is available. We evaluate the generative ability and constraint consistency of samples generated with PhysNAP using the PartNet-Mobility dataset. We also compare it with an unguided baseline diffusion model and demonstrate that PhysNAP can improve constraint consistency and provides a tradeoff with generative ability.", "date": "2025-08-01", "categories": []}
{"id": "2507.17745", "title": "Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention", "abstract": "Recent advances in sparse voxel representations have significantly improved the quality of 3D content generation, enabling high-resolution modeling with fine-grained geometry. However, existing frameworks suffer from severe computational inefficiencies due to the quadratic complexity of attention mechanisms in their two-stage diffusion pipelines. In this work, we propose Ultra3D, an efficient 3D generation framework that significantly accelerates sparse voxel modeling without compromising quality. Our method leverages the compact VecSet representation to efficiently generate a coarse object layout in the first stage, reducing token count and accelerating voxel coordinate prediction. To refine per-voxel latent features in the second stage, we introduce Part Attention, a geometry-aware localized attention mechanism that restricts attention computation within semantically consistent part regions. This design preserves structural continuity while avoiding unnecessary global attention, achieving up to 6.7x speed-up in latent generation. To support this mechanism, we construct a scalable part annotation pipeline that converts raw meshes into part-labeled sparse voxels. Extensive experiments demonstrate that Ultra3D supports high-resolution 3D generation at 1024 resolution and achieves state-of-the-art performance in both visual fidelity and user preference.", "date": "2025-07-23", "categories": []}
{"id": "2412.00623", "title": "A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision", "abstract": "We present a novel framework for training 3D image-conditioned diffusion models using only 2D supervision. Recovering 3D structure from 2D images is inherently ill-posed due to the ambiguity of possible reconstructions, making generative models a natural choice. However, most existing 3D generative models rely on full 3D supervision, which is impractical due to the scarcity of large-scale 3D datasets. To address this, we propose leveraging sparse-view supervision as a scalable alternative. While recent reconstruction models use sparse-view supervision with differentiable rendering to lift 2D images to 3D, they are predominantly deterministic, failing to capture the diverse set of plausible solutions and producing blurry predictions in uncertain regions. A key challenge in training 3D diffusion models with 2D supervision is that the standard training paradigm requires both the denoising process and supervision to be in the same modality. We address this by decoupling the noisy samples being denoised from the supervision signal, allowing the former to remain in 3D while the latter is provided in 2D. Our approach leverages suboptimal predictions from a deterministic image-to-3D model-acting as a \"teacher\"-to generate noisy 3D inputs, enabling effective 3D diffusion training without requiring full 3D ground truth. We validate our framework on both object-level and scene-level datasets, using two different 3D Gaussian Splat (3DGS) teachers. Our results show that our approach consistently improves upon these deterministic teachers, demonstrating its effectiveness in scalable and high-fidelity 3D generative modeling. See our project page at https://lesson-in-splats.github.io/", "date": "2024-12-01", "categories": []}
{"id": "2503.08737", "title": "Representing 3D Shapes With 64 Latent Vectors for 3D Diffusion Models", "abstract": "Constructing a compressed latent space through a variational autoencoder (VAE) is the key for efficient 3D diffusion models. This paper introduces COD-VAE that encodes 3D shapes into a COmpact set of 1D latent vectors without sacrificing quality. COD-VAE introduces a two-stage autoencoder scheme to improve compression and decoding efficiency. First, our encoder block progressively compresses point clouds into compact latent vectors via intermediate point patches. Second, our triplane-based decoder reconstructs dense triplanes from latent vectors instead of directly decoding neural fields, significantly reducing computational overhead of neural fields decoding. Finally, we propose uncertainty-guided token pruning, which allocates resources adaptively by skipping computations in simpler regions and improves the decoder efficiency. Experimental results demonstrate that COD-VAE achieves 16x compression compared to the baseline while maintaining quality. This enables 20.8x speedup in generation, highlighting that a large number of latent vectors is not a prerequisite for high-quality reconstruction and generation. The code is available at https://github.com/join16/COD-VAE.", "date": "2025-03-11", "categories": []}
{"id": "2503.15475", "title": "Cube: A Roblox View of 3D Intelligence", "abstract": "Foundation models trained on vast amounts of data have demonstrated remarkable reasoning and generation capabilities in the domains of text, images, audio and video. Our goal at Roblox is to build such a foundation model for 3D intelligence, a model that can support developers in producing all aspects of a Roblox experience, from generating 3D objects and scenes to rigging characters for animation to producing programmatic scripts describing object behaviors. We discuss three key design requirements for such a 3D foundation model and then present our first step towards building such a model. We expect that 3D geometric shapes will be a core data type and describe our solution for 3D shape tokenizer. We show how our tokenization scheme can be used in applications for text-to-shape generation, shape-to-text generation and text-to-scene generation. We demonstrate how these applications can collaborate with existing large language models (LLMs) to perform scene analysis and reasoning. We conclude with a discussion outlining our path to building a fully unified foundation model for 3D intelligence.", "date": "2025-03-19", "categories": []}
{"id": "2507.13346", "title": "AutoPartGen: Autogressive 3D Part Generation and Discovery", "abstract": "We introduce AutoPartGen, a model that generates objects composed of 3D parts in an autoregressive manner. This model can take as input an image of an object, 2D masks of the object's parts, or an existing 3D object, and generate a corresponding compositional 3D reconstruction. Our approach builds upon 3DShape2VecSet, a recent latent 3D representation with powerful geometric expressiveness. We observe that this latent space exhibits strong compositional properties, making it particularly well-suited for part-based generation tasks. Specifically, AutoPartGen generates object parts autoregressively, predicting one part at a time while conditioning on previously generated parts and additional inputs, such as 2D images, masks, or 3D objects. This process continues until the model decides that all parts have been generated, thus determining automatically the type and number of parts. The resulting parts can be seamlessly assembled into coherent objects or scenes without requiring additional optimization. We evaluate both the overall 3D generation capabilities and the part-level generation quality of AutoPartGen, demonstrating that it achieves state-of-the-art performance in 3D part generation.", "date": "2025-07-17", "categories": []}
{"id": "2507.15212", "title": "MeshMamba: State Space Models for Articulated 3D Mesh Generation and Reconstruction", "abstract": "In this paper, we introduce MeshMamba, a neural network model for learning 3D articulated mesh models by employing the recently proposed Mamba State Space Models (Mamba-SSMs). MeshMamba is efficient and scalable in handling a large number of input tokens, enabling the generation and reconstruction of body mesh models with more than 10,000 vertices, capturing clothing and hand geometries. The key to effectively learning MeshMamba is the serialization technique of mesh vertices into orderings that are easily processed by Mamba. This is achieved by sorting the vertices based on body part annotations or the 3D vertex locations of a template mesh, such that the ordering respects the structure of articulated shapes. Based on MeshMamba, we design 1) MambaDiff3D, a denoising diffusion model for generating 3D articulated meshes and 2) Mamba-HMR, a 3D human mesh recovery model that reconstructs a human body shape and pose from a single image. Experimental results showed that MambaDiff3D can generate dense 3D human meshes in clothes, with grasping hands, etc., and outperforms previous approaches in the 3D human shape generation task. Additionally, Mamba-HMR extends the capabilities of previous non-parametric human mesh recovery approaches, which were limited to handling body-only poses using around 500 vertex tokens, to the whole-body setting with face and hands, while achieving competitive performance in (near) real-time.", "date": "2025-07-21", "categories": []}
{"id": "2507.15399", "title": "Blended Point Cloud Diffusion for Localized Text-guided Shape Editing", "abstract": "Natural language offers a highly intuitive interface for enabling localized fine-grained edits of 3D shapes. However, prior works face challenges in preserving global coherence while locally modifying the input 3D shape. In this work, we introduce an inpainting-based framework for editing shapes represented as point clouds. Our approach leverages foundation 3D diffusion models for achieving localized shape edits, adding structural guidance in the form of a partial conditional shape, ensuring that other regions correctly preserve the shape's identity. Furthermore, to encourage identity preservation also within the local edited region, we propose an inference-time coordinate blending algorithm which balances reconstruction of the full shape with inpainting at a progression of noise levels during the inference process. Our coordinate blending algorithm seamlessly blends the original shape with its edited version, enabling a fine-grained editing of 3D shapes, all while circumventing the need for computationally expensive and often inaccurate inversion. Extensive experiments show that our method outperforms alternative techniques across a wide range of metrics that evaluate both fidelity to the original shape and also adherence to the textual description.", "date": "2025-07-21", "categories": []}
{"id": "2507.09748", "title": "Advancing Text-to-3D Generation with Linearized Lookahead Variational Score Distillation", "abstract": "Text-to-3D generation based on score distillation of pre-trained 2D diffusion models has gained increasing interest, with variational score distillation (VSD) as a remarkable example. VSD proves that vanilla score distillation can be improved by introducing an extra score-based model, which characterizes the distribution of images rendered from 3D models, to correct the distillation gradient. Despite the theoretical foundations, VSD, in practice, is likely to suffer from slow and sometimes ill-posed convergence. In this paper, we perform an in-depth investigation of the interplay between the introduced score model and the 3D model, and find that there exists a mismatching problem between LoRA and 3D distributions in practical implementation. We can simply adjust their optimization order to improve the generation quality. By doing so, the score model looks ahead to the current 3D state and hence yields more reasonable corrections. Nevertheless, naive lookahead VSD may suffer from unstable training in practice due to the potential over-fitting. To address this, we propose to use a linearized variant of the model for score distillation, giving rise to the Linearized Lookahead Variational Score Distillation ($L^2$-VSD). $L^2$-VSD can be realized efficiently with forward-mode autodiff functionalities of existing deep learning libraries. Extensive experiments validate the efficacy of $L^2$-VSD, revealing its clear superiority over prior score distillation-based methods. We also show that our method can be seamlessly incorporated into any other VSD-based text-to-3D framework.", "date": "2025-07-13", "categories": []}
{"id": "2507.05763", "title": "DreamArt: Generating Interactable Articulated Objects from a Single Image", "abstract": "Generating articulated objects, such as laptops and microwaves, is a crucial yet challenging task with extensive applications in Embodied AI and AR/VR. Current image-to-3D methods primarily focus on surface geometry and texture, neglecting part decomposition and articulation modeling. Meanwhile, neural reconstruction approaches (e.g., NeRF or Gaussian Splatting) rely on dense multi-view or interaction data, limiting their scalability. In this paper, we introduce DreamArt, a novel framework for generating high-fidelity, interactable articulated assets from single-view images. DreamArt employs a three-stage pipeline: firstly, it reconstructs part-segmented and complete 3D object meshes through a combination of image-to-3D generation, mask-prompted 3D segmentation, and part amodal completion. Second, we fine-tune a video diffusion model to capture part-level articulation priors, leveraging movable part masks as prompt and amodal images to mitigate ambiguities caused by occlusion. Finally, DreamArt optimizes the articulation motion, represented by a dual quaternion, and conducts global texture refinement and repainting to ensure coherent, high-quality textures across all parts. Experimental results demonstrate that DreamArt effectively generates high-quality articulated objects, possessing accurate part shape, high appearance fidelity, and plausible articulation, thereby providing a scalable solution for articulated asset generation. Our project page is available at https://dream-art-0.github.io/DreamArt/.", "date": "2025-07-08", "categories": []}
{"id": "2507.06165", "title": "OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesion", "abstract": "The creation of 3D assets with explicit, editable part structures is crucial for advancing interactive applications, yet most generative methods produce only monolithic shapes, limiting their utility. We introduce OmniPart, a novel framework for part-aware 3D object generation designed to achieve high semantic decoupling among components while maintaining robust structural cohesion. OmniPart uniquely decouples this complex task into two synergistic stages: (1) an autoregressive structure planning module generates a controllable, variable-length sequence of 3D part bounding boxes, critically guided by flexible 2D part masks that allow for intuitive control over part decomposition without requiring direct correspondences or semantic labels; and (2) a spatially-conditioned rectified flow model, efficiently adapted from a pre-trained holistic 3D generator, synthesizes all 3D parts simultaneously and consistently within the planned layout. Our approach supports user-defined part granularity, precise localization, and enables diverse downstream applications. Extensive experiments demonstrate that OmniPart achieves state-of-the-art performance, paving the way for more interpretable, editable, and versatile 3D content.", "date": "2025-07-08", "categories": []}
{"id": "2407.14419", "title": "HOTS3D: Hyper-Spherical Optimal Transport for Semantic Alignment of Text-to-3D Generation", "abstract": "Recent CLIP-guided 3D generation methods have achieved promising results but struggle with generating faithful 3D shapes that conform with input text due to the gap between text and image embeddings. To this end, this paper proposes HOTS3D which makes the first attempt to effectively bridge this gap by aligning text features to the image features with spherical optimal transport(SOT). However, in high-dimensional situations, solving the SOT remains a challenge. To obtain the SOT map for high-dimensional features obtained from CLIP encoding of two modalities, we mathematically formulate and derive the solution based on Villani's theorem, which can directly align two hyper-sphere distributions without manifold exponential maps. Furthermore, we implement it by leveraging input convex neural networks (ICNNs) for the optimal Kantorovich potential. With the optimally mapped features, a diffusion-based generator is utilized to decode them into 3D shapes. Extensive quantitative and qualitative comparisons with state-of-the-art methods demonstrate the superiority of HOTS3D for text-to-3D generation, especially in the consistency with text semantics.", "date": "2024-07-19", "categories": []}
{"id": "2505.07003", "title": "CMD: Controllable Multiview Diffusion for 3D Editing and Progressive Generation", "abstract": "Recently, 3D generation methods have shown their powerful ability to automate 3D model creation. However, most 3D generation methods only rely on an input image or a text prompt to generate a 3D model, which lacks the control of each component of the generated 3D model. Any modifications of the input image lead to an entire regeneration of the 3D models. In this paper, we introduce a new method called CMD that generates a 3D model from an input image while enabling flexible local editing of each component of the 3D model. In CMD, we formulate the 3D generation as a conditional multiview diffusion model, which takes the existing or known parts as conditions and generates the edited or added components. This conditional multiview diffusion model not only allows the generation of 3D models part by part but also enables local editing of 3D models according to the local revision of the input image without changing other 3D parts. Extensive experiments are conducted to demonstrate that CMD decomposes a complex 3D generation task into multiple components, improving the generation quality. Meanwhile, CMD enables efficient and flexible local editing of a 3D model by just editing one rendered image.", "date": "2025-05-11", "categories": []}
{"id": "2505.17721", "title": "SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation", "abstract": "Denoising diffusion probabilistic models have achieved significant success in point cloud generation, enabling numerous downstream applications, such as generative data augmentation and 3D model editing. However, little attention has been given to generating point clouds with point-wise segmentation labels, as well as to developing evaluation metrics for this task. Therefore, in this paper, we present SeaLion, a novel diffusion model designed to generate high-quality and diverse point clouds with fine-grained segmentation labels. Specifically, we introduce the semantic part-aware latent point diffusion technique, which leverages the intermediate features of the generative models to jointly predict the noise for perturbed latent points and associated part segmentation labels during the denoising process, and subsequently decodes the latent points to point clouds conditioned on part segmentation labels. To effectively evaluate the quality of generated point clouds, we introduce a novel point cloud pairwise distance calculation method named part-aware Chamfer distance (p-CD). This method enables existing metrics, such as 1-NNA, to measure both the local structural quality and inter-part coherence of generated point clouds. Experiments on the large-scale synthetic dataset ShapeNet and real-world medical dataset IntrA demonstrate that SeaLion achieves remarkable performance in generation quality and diversity, outperforming the existing state-of-the-art model, DiffFacto, by 13.33% and 6.52% on 1-NNA (p-CD) across the two datasets. Experimental analysis shows that SeaLion can be trained semi-supervised, thereby reducing the demand for labeling efforts. Lastly, we validate the applicability of SeaLion in generative data augmentation for training segmentation models and the capability of SeaLion to serve as a tool for part-aware 3D shape editing.", "date": "2025-05-23", "categories": []}
{"id": "2506.20875", "title": "3DGH: 3D Head Generation with Composable Hair and Face", "abstract": "We present 3DGH, an unconditional generative model for 3D human heads with composable hair and face components. Unlike previous work that entangles the modeling of hair and face, we propose to separate them using a novel data representation with template-based 3D Gaussian Splatting, in which deformable hair geometry is introduced to capture the geometric variations across different hairstyles. Based on this data representation, we design a 3D GAN-based architecture with dual generators and employ a cross-attention mechanism to model the inherent correlation between hair and face. The model is trained on synthetic renderings using carefully designed objectives to stabilize training and facilitate hair-face separation. We conduct extensive experiments to validate the design choice of 3DGH, and evaluate it both qualitatively and quantitatively by comparing with several state-of-the-art 3D GAN methods, demonstrating its effectiveness in unconditional full-head image synthesis and composable 3D hairstyle editing. More details will be available on our project page: https://c-he.github.io/projects/3dgh/.", "date": "2025-06-25", "categories": []}
{"id": "2506.15718", "title": "BuildingBRep-11K: Precise Multi-Storey B-Rep Building Solids with Rich Layout Metadata", "abstract": "With the rise of artificial intelligence, the automatic generation of building-scale 3-D objects has become an active research topic, yet training such models still demands large, clean and richly annotated datasets. We introduce BuildingBRep-11K, a collection of 11 978 multi-storey (2-10 floors) buildings (about 10 GB) produced by a shape-grammar-driven pipeline that encodes established building-design principles. Every sample consists of a geometrically exact B-rep solid-covering floors, walls, slabs and rule-based openings-together with a fast-loading .npy metadata file that records detailed per-floor parameters. The generator incorporates constraints on spatial scale, daylight optimisation and interior layout, and the resulting objects pass multi-stage filters that remove Boolean failures, undersized rooms and extreme aspect ratios, ensuring compliance with architectural standards. To verify the dataset's learnability we trained two lightweight PointNet baselines. (i) Multi-attribute regression. A single encoder predicts storey count, total rooms, per-storey vector and mean room area from a 4 000-point cloud. On 100 unseen buildings it attains 0.37-storey MAE (87 \\% within $\\pm1$), 5.7-room MAE, and 3.2 m$^2$ MAE on mean area. (ii) Defect detection. With the same backbone we classify GOOD versus DEFECT; on a balanced 100-model set the network reaches 54 \\% accuracy, recalling 82 \\% of true defects at 53 \\% precision (41 TP, 9 FN, 37 FP, 13 TN). These pilots show that BuildingBRep-11K is learnable yet non-trivial for both geometric regression and topological quality assessment", "date": "2025-06-03", "categories": []}
{"id": "2506.16504", "title": "Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details", "abstract": "In this report, we present Hunyuan3D 2.5, a robust suite of 3D diffusion models aimed at generating high-fidelity and detailed textured 3D assets. Hunyuan3D 2.5 follows two-stages pipeline of its previous version Hunyuan3D 2.0, while demonstrating substantial advancements in both shape and texture generation. In terms of shape generation, we introduce a new shape foundation model -- LATTICE, which is trained with scaled high-quality datasets, model-size, and compute. Our largest model reaches 10B parameters and generates sharp and detailed 3D shape with precise image-3D following while keeping mesh surface clean and smooth, significantly closing the gap between generated and handcrafted 3D shapes. In terms of texture generation, it is upgraded with phyiscal-based rendering (PBR) via a novel multi-view architecture extended from Hunyuan3D 2.0 Paint model. Our extensive evaluation shows that Hunyuan3D 2.5 significantly outperforms previous methods in both shape and end-to-end texture generation.", "date": "2025-06-19", "categories": []}
{"id": "2506.15442", "title": "Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material", "abstract": "3D AI-generated content (AIGC) is a passionate field that has significantly accelerated the creation of 3D models in gaming, film, and design. Despite the development of several groundbreaking models that have revolutionized 3D generation, the field remains largely accessible only to researchers, developers, and designers due to the complexities involved in collecting, processing, and training 3D models. To address these challenges, we introduce Hunyuan3D 2.1 as a case study in this tutorial. This tutorial offers a comprehensive, step-by-step guide on processing 3D data, training a 3D generative model, and evaluating its performance using Hunyuan3D 2.1, an advanced system for producing high-resolution, textured 3D assets. The system comprises two core components: the Hunyuan3D-DiT for shape generation and the Hunyuan3D-Paint for texture synthesis. We will explore the entire workflow, including data preparation, model architecture, training strategies, evaluation metrics, and deployment. By the conclusion of this tutorial, you will have the knowledge to finetune or develop a robust 3D generative model suitable for applications in gaming, virtual reality, and industrial design.", "date": "2025-06-18", "categories": []}
{"id": "2506.15684", "title": "Nabla-R2D3: Effective and Efficient 3D Diffusion Alignment with 2D Rewards", "abstract": "Generating high-quality and photorealistic 3D assets remains a longstanding challenge in 3D vision and computer graphics. Although state-of-the-art generative models, such as diffusion models, have made significant progress in 3D generation, they often fall short of human-designed content due to limited ability to follow instructions, align with human preferences, or produce realistic textures, geometries, and physical attributes. In this paper, we introduce Nabla-R2D3, a highly effective and sample-efficient reinforcement learning alignment framework for 3D-native diffusion models using 2D rewards. Built upon the recently proposed Nabla-GFlowNet method, which matches the score function to reward gradients in a principled manner for reward finetuning, our Nabla-R2D3 enables effective adaptation of 3D diffusion models using only 2D reward signals. Extensive experiments show that, unlike vanilla finetuning baselines which either struggle to converge or suffer from reward hacking, Nabla-R2D3 consistently achieves higher rewards and reduced prior forgetting within a few finetuning steps.", "date": "2025-06-18", "categories": []}
{"id": "2506.10600", "title": "EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence", "abstract": "Constructing a physically realistic and accurately scaled simulated 3D world is crucial for the training and evaluation of embodied intelligence tasks. The diversity, realism, low cost accessibility and affordability of 3D data assets are critical for achieving generalization and scalability in embodied AI. However, most current embodied intelligence tasks still rely heavily on traditional 3D computer graphics assets manually created and annotated, which suffer from high production costs and limited realism. These limitations significantly hinder the scalability of data driven approaches. We present EmbodiedGen, a foundational platform for interactive 3D world generation. It enables the scalable generation of high-quality, controllable and photorealistic 3D assets with accurate physical properties and real-world scale in the Unified Robotics Description Format (URDF) at low cost. These assets can be directly imported into various physics simulation engines for fine-grained physical control, supporting downstream tasks in training and evaluation. EmbodiedGen is an easy-to-use, full-featured toolkit composed of six key modules: Image-to-3D, Text-to-3D, Texture Generation, Articulated Object Generation, Scene Generation and Layout Generation. EmbodiedGen generates diverse and interactive 3D worlds composed of generative 3D assets, leveraging generative AI to address the challenges of generalization and evaluation to the needs of embodied intelligence related research. Code is available at https://horizonrobotics.github.io/robot_lab/embodied_gen/index.html.", "date": "2025-06-12", "categories": []}
{"id": "2506.13594", "title": "Dive3D: Diverse Distillation-based Text-to-3D Generation via Score Implicit Matching", "abstract": "Distilling pre-trained 2D diffusion models into 3D assets has driven remarkable advances in text-to-3D synthesis. However, existing methods typically rely on Score Distillation Sampling (SDS) loss, which involves asymmetric KL divergence--a formulation that inherently favors mode-seeking behavior and limits generation diversity. In this paper, we introduce Dive3D, a novel text-to-3D generation framework that replaces KL-based objectives with Score Implicit Matching (SIM) loss, a score-based objective that effectively mitigates mode collapse. Furthermore, Dive3D integrates both diffusion distillation and reward-guided optimization under a unified divergence perspective. Such reformulation, together with SIM loss, yields significantly more diverse 3D outputs while improving text alignment, human preference, and overall visual fidelity. We validate Dive3D across various 2D-to-3D prompts and find that it consistently outperforms prior methods in qualitative assessments, including diversity, photorealism, and aesthetic appeal. We further evaluate its performance on the GPTEval3D benchmark, comparing against nine state-of-the-art baselines. Dive3D also achieves strong results on quantitative metrics, including text-asset alignment, 3D plausibility, text-geometry consistency, texture quality, and geometric detail.", "date": "2025-06-16", "categories": []}
{"id": "2506.11148", "title": "LLM-to-Phy3D: Physically Conform Online 3D Object Generation with LLMs", "abstract": "The emergence of generative artificial intelligence (GenAI) and large language models (LLMs) has revolutionized the landscape of digital content creation in different modalities. However, its potential use in Physical AI for engineering design, where the production of physically viable artifacts is paramount, remains vastly underexplored. The absence of physical knowledge in existing LLM-to-3D models often results in outputs detached from real-world physical constraints. To address this gap, we introduce LLM-to-Phy3D, a physically conform online 3D object generation that enables existing LLM-to-3D models to produce physically conforming 3D objects on the fly. LLM-to-Phy3D introduces a novel online black-box refinement loop that empowers large language models (LLMs) through synergistic visual and physics-based evaluations. By delivering directional feedback in an iterative refinement process, LLM-to-Phy3D actively drives the discovery of prompts that yield 3D artifacts with enhanced physical performance and greater geometric novelty relative to reference objects, marking a substantial contribution to AI-driven generative design. Systematic evaluations of LLM-to-Phy3D, supported by ablation studies in vehicle design optimization, reveal various LLM improvements gained by 4.5% to 106.7% in producing physically conform target domain 3D designs over conventional LLM-to-3D models. The encouraging results suggest the potential general use of LLM-to-Phy3D in Physical AI for scientific and engineering applications.", "date": "2025-06-11", "categories": []}
{"id": "2505.14521", "title": "Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modeling", "abstract": "High-fidelity 3D object synthesis remains significantly more challenging than 2D image generation due to the unstructured nature of mesh data and the cubic complexity of dense volumetric grids. Existing two-stage pipelines-compressing meshes with a VAE (using either 2D or 3D supervision), followed by latent diffusion sampling-often suffer from severe detail loss caused by inefficient representations and modality mismatches introduced in VAE. We introduce Sparc3D, a unified framework that combines a sparse deformable marching cubes representation Sparcubes with a novel encoder Sparconv-VAE. Sparcubes converts raw meshes into high-resolution ($1024^3$) surfaces with arbitrary topology by scattering signed distance and deformation fields onto a sparse cube, allowing differentiable optimization. Sparconv-VAE is the first modality-consistent variational autoencoder built entirely upon sparse convolutional networks, enabling efficient and near-lossless 3D reconstruction suitable for high-resolution generative modeling through latent diffusion. Sparc3D achieves state-of-the-art reconstruction fidelity on challenging inputs, including open surfaces, disconnected components, and intricate geometry. It preserves fine-grained shape details, reduces training and inference cost, and integrates naturally with latent diffusion models for scalable, high-resolution 3D generation.", "date": "2025-05-20", "categories": []}
{"id": "2506.09980", "title": "Efficient Part-level 3D Object Generation via Dual Volume Packing", "abstract": "Recent progress in 3D object generation has greatly improved both the quality and efficiency. However, most existing methods generate a single mesh with all parts fused together, which limits the ability to edit or manipulate individual parts. A key challenge is that different objects may have a varying number of parts. To address this, we propose a new end-to-end framework for part-level 3D object generation. Given a single input image, our method generates high-quality 3D objects with an arbitrary number of complete and semantically meaningful parts. We introduce a dual volume packing strategy that organizes all parts into two complementary volumes, allowing for the creation of complete and interleaved parts that assemble into the final object. Experiments show that our model achieves better quality, diversity, and generalization than previous image-based part-level generation methods.", "date": "2025-06-11", "categories": []}
{"id": "2408.14732", "title": "OctFusion: Octree-based Diffusion Models for 3D Shape Generation", "abstract": "Diffusion models have emerged as a popular method for 3D generation. However, it is still challenging for diffusion models to efficiently generate diverse and high-quality 3D shapes. In this paper, we introduce OctFusion, which can generate 3D shapes with arbitrary resolutions in 2.5 seconds on a single Nvidia 4090 GPU, and the extracted meshes are guaranteed to be continuous and manifold. The key components of OctFusion are the octree-based latent representation and the accompanying diffusion models. The representation combines the benefits of both implicit neural representations and explicit spatial octrees and is learned with an octree-based variational autoencoder. The proposed diffusion model is a unified multi-scale U-Net that enables weights and computation sharing across different octree levels and avoids the complexity of widely used cascaded diffusion schemes. We verify the effectiveness of OctFusion on the ShapeNet and Objaverse datasets and achieve state-of-the-art performances on shape generation tasks. We demonstrate that OctFusion is extendable and flexible by generating high-quality color fields for textured mesh generation and high-quality 3D shapes conditioned on text prompts, sketches, or category labels. Our code and pre-trained models are available at https://github.com/octree-nn/octfusion.", "date": "2024-08-27", "categories": []}
{"id": "2412.11596", "title": "MeshArt: Generating Articulated Meshes with Structure-Guided Transformers", "abstract": "Articulated 3D object generation is fundamental for creating realistic, functional, and interactable virtual assets which are not simply static. We introduce MeshArt, a hierarchical transformer-based approach to generate articulated 3D meshes with clean, compact geometry, reminiscent of human-crafted 3D models. We approach articulated mesh generation in a part-by-part fashion across two stages. First, we generate a high-level articulation-aware object structure; then, based on this structural information, we synthesize each part's mesh faces. Key to our approach is modeling both articulation structures and part meshes as sequences of quantized triangle embeddings, leading to a unified hierarchical framework with transformers for autoregressive generation. Object part structures are first generated as their bounding primitives and articulation modes; a second transformer, guided by these articulation structures, then generates each part's mesh triangles. To ensure coherency among generated parts, we introduce structure-guided conditioning that also incorporates local part mesh connectivity. MeshArt shows significant improvements over state of the art, with 57.1% improvement in structure coverage and a 209-point improvement in mesh generation FID.", "date": "2024-12-16", "categories": []}
{"id": "2506.07698", "title": "NOVA3D: Normal Aligned Video Diffusion Model for Single Image to 3D Generation", "abstract": "3D AI-generated content (AIGC) has made it increasingly accessible for anyone to become a 3D content creator. While recent methods leverage Score Distillation Sampling to distill 3D objects from pretrained image diffusion models, they often suffer from inadequate 3D priors, leading to insufficient multi-view consistency. In this work, we introduce NOVA3D, an innovative single-image-to-3D generation framework. Our key insight lies in leveraging strong 3D priors from a pretrained video diffusion model and integrating geometric information during multi-view video fine-tuning. To facilitate information exchange between color and geometric domains, we propose the Geometry-Temporal Alignment (GTA) attention mechanism, thereby improving generalization and multi-view consistency. Moreover, we introduce the de-conflict geometry fusion algorithm, which improves texture fidelity by addressing multi-view inaccuracies and resolving discrepancies in pose alignment. Extensive experiments validate the superiority of NOVA3D over existing baselines.", "date": "2025-06-09", "categories": []}
{"id": "2506.05573", "title": "PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers", "abstract": "We introduce PartCrafter, the first structured 3D generative model that jointly synthesizes multiple semantically meaningful and geometrically distinct 3D meshes from a single RGB image. Unlike existing methods that either produce monolithic 3D shapes or follow two-stage pipelines, i.e., first segmenting an image and then reconstructing each segment, PartCrafter adopts a unified, compositional generation architecture that does not rely on pre-segmented inputs. Conditioned on a single image, it simultaneously denoises multiple 3D parts, enabling end-to-end part-aware generation of both individual objects and complex multi-object scenes. PartCrafter builds upon a pretrained 3D mesh diffusion transformer (DiT) trained on whole objects, inheriting the pretrained weights, encoder, and decoder, and introduces two key innovations: (1) A compositional latent space, where each 3D part is represented by a set of disentangled latent tokens; (2) A hierarchical attention mechanism that enables structured information flow both within individual parts and across all parts, ensuring global coherence while preserving part-level detail during generation. To support part-level supervision, we curate a new dataset by mining part-level annotations from large-scale 3D object datasets. Experiments show that PartCrafter outperforms existing approaches in generating decomposable 3D meshes, including parts that are not directly visible in input images, demonstrating the strength of part-aware generative priors for 3D understanding and synthesis. Code and training data will be released.", "date": "2025-06-05", "categories": []}
{"id": "2506.04351", "title": "HuGeDiff: 3D Human Generation via Diffusion with Gaussian Splatting", "abstract": "3D human generation is an important problem with a wide range of applications in computer vision and graphics. Despite recent progress in generative AI such as diffusion models or rendering methods like Neural Radiance Fields or Gaussian Splatting, controlling the generation of accurate 3D humans from text prompts remains an open challenge. Current methods struggle with fine detail, accurate rendering of hands and faces, human realism, and controlability over appearance. The lack of diversity, realism, and annotation in human image data also remains a challenge, hindering the development of a foundational 3D human model. We present a weakly supervised pipeline that tries to address these challenges. In the first step, we generate a photorealistic human image dataset with controllable attributes such as appearance, race, gender, etc using a state-of-the-art image diffusion model. Next, we propose an efficient mapping approach from image features to 3D point clouds using a transformer-based architecture. Finally, we close the loop by training a point-cloud diffusion model that is conditioned on the same text prompts used to generate the original samples. We demonstrate orders-of-magnitude speed-ups in 3D human generation compared to the state-of-the-art approaches, along with significantly improved text-prompt alignment, realism, and rendering quality. We will make the code and dataset available.", "date": "2025-06-04", "categories": []}
{"id": "2505.04185", "title": "S3D: Sketch-Driven 3D Model Generation", "abstract": "Generating high-quality 3D models from 2D sketches is a challenging task due to the inherent ambiguity and sparsity of sketch data. In this paper, we present S3D, a novel framework that converts simple hand-drawn sketches into detailed 3D models. Our method utilizes a U-Net-based encoder-decoder architecture to convert sketches into face segmentation masks, which are then used to generate a 3D representation that can be rendered from novel views. To ensure robust consistency between the sketch domain and the 3D output, we introduce a novel style-alignment loss that aligns the U-Net bottleneck features with the initial encoder outputs of the 3D generation module, significantly enhancing reconstruction fidelity. To further enhance the network's robustness, we apply augmentation techniques to the sketch dataset. This streamlined framework demonstrates the effectiveness of S3D in generating high-quality 3D models from sketch inputs. The source code for this project is publicly available at https://github.com/hailsong/S3D.", "date": "2025-05-07", "categories": []}
{"id": "2506.02702", "title": "ToothForge: Automatic Dental Shape Generation using Synchronized Spectral Embeddings", "abstract": "We introduce ToothForge, a spectral approach for automatically generating novel 3D teeth, effectively addressing the sparsity of dental shape datasets. By operating in the spectral domain, our method enables compact machine learning modeling, allowing the generation of high-resolution tooth meshes in milliseconds. However, generating shape spectra comes with the instability of the decomposed harmonics. To address this, we propose modeling the latent manifold on synchronized frequential embeddings. Spectra of all data samples are aligned to a common basis prior to the training procedure, effectively eliminating biases introduced by the decomposition instability. Furthermore, synchronized modeling removes the limiting factor imposed by previous methods, which require all shapes to share a common fixed connectivity. Using a private dataset of real dental crowns, we observe a greater reconstruction quality of the synthetized shapes, exceeding those of models trained on unaligned embeddings. We also explore additional applications of spectral analysis in digital dentistry, such as shape compression and interpolation. ToothForge facilitates a range of approaches at the intersection of spectral analysis and machine learning, with fewer restrictions on mesh structure. This makes it applicable for shape analysis not only in dentistry, but also in broader medical applications, where guaranteeing consistent connectivity across shapes from various clinics is unrealistic. The code is available at https://github.com/tiborkubik/toothForge.", "date": "2025-06-03", "categories": []}
{"id": "2410.00890", "title": "Flex3D: Feed-Forward 3D Generation with Flexible Reconstruction Model and Input View Curation", "abstract": "Generating high-quality 3D content from text, single images, or sparse view images remains a challenging task with broad applications. Existing methods typically employ multi-view diffusion models to synthesize multi-view images, followed by a feed-forward process for 3D reconstruction. However, these approaches are often constrained by a small and fixed number of input views, limiting their ability to capture diverse viewpoints and, even worse, leading to suboptimal generation results if the synthesized views are of poor quality. To address these limitations, we propose Flex3D, a novel two-stage framework capable of leveraging an arbitrary number of high-quality input views. The first stage consists of a candidate view generation and curation pipeline. We employ a fine-tuned multi-view image diffusion model and a video diffusion model to generate a pool of candidate views, enabling a rich representation of the target 3D object. Subsequently, a view selection pipeline filters these views based on quality and consistency, ensuring that only the high-quality and reliable views are used for reconstruction. In the second stage, the curated views are fed into a Flexible Reconstruction Model (FlexRM), built upon a transformer architecture that can effectively process an arbitrary number of inputs. FlemRM directly outputs 3D Gaussian points leveraging a tri-plane representation, enabling efficient and detailed 3D generation. Through extensive exploration of design and training strategies, we optimize FlexRM to achieve superior performance in both reconstruction and generation tasks. Our results demonstrate that Flex3D achieves state-of-the-art performance, with a user study winning rate of over 92% in 3D generation tasks when compared to several of the latest feed-forward 3D generative models.", "date": "2024-10-01", "categories": []}
{"id": "2506.01853", "title": "ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding", "abstract": "Recently, the powerful text-to-image capabilities of ChatGPT-4o have led to growing appreciation for native multimodal large language models. However, its multimodal capabilities remain confined to images and text. Yet beyond images, the ability to understand and generate 3D content is equally crucial. To address this gap, we propose ShapeLLM-Omni-a native 3D large language model capable of understanding and generating 3D assets and text in any sequence. First, we train a 3D vector-quantized variational autoencoder (VQVAE), which maps 3D objects into a discrete latent space to achieve efficient and accurate shape representation and reconstruction. Building upon the 3D-aware discrete tokens, we innovatively construct a large-scale continuous training dataset named 3D-Alpaca, encompassing generation, comprehension, and editing, thus providing rich resources for future research and training. Finally, by performing instruction-based training of the Qwen-2.5-vl-7B-Instruct model on the 3D-Alpaca dataset. Our work provides an effective attempt at extending multimodal models with basic 3D capabilities, which contributes to future research in 3D-native AI. Project page: https://github.com/JAMESYJL/ShapeLLM-Omni", "date": "2025-06-02", "categories": []}
{"id": "2405.14979", "title": "CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner", "abstract": "We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in an interactive manner. Despite the significant advancements in 3D generation, existing methods still struggle with lengthy optimization processes, irregular mesh topologies, noisy surfaces, and difficulties in accommodating user edits, consequently impeding their widespread adoption and implementation in 3D modeling software. Our work is inspired by the craftsman, who usually roughs out the holistic figure of the work first and elaborates the surface details subsequently. Specifically, we employ a 3D native diffusion model, which operates on latent space learned from latent set-based 3D representations, to generate coarse geometries with regular mesh topology in seconds. In particular, this process takes as input a text prompt or a reference image and leverages a powerful multi-view (MV) diffusion model to generate multiple views of the coarse geometry, which are fed into our MV-conditioned 3D diffusion model for generating the 3D geometry, significantly improving robustness and generalizability. Following that, a normal-based geometry refiner is used to significantly enhance the surface details. This refinement can be performed automatically, or interactively with user-supplied edits. Extensive experiments demonstrate that our method achieves high efficacy in producing superior-quality 3D assets compared to existing methods. HomePage: https://craftsman3d.github.io/, Code: https://github.com/wyysf-98/CraftsMan", "date": "2024-05-23", "categories": []}
{"id": "2412.01506", "title": "Structured 3D Latents for Scalable and Versatile 3D Generation", "abstract": "We introduce a novel 3D generation method for versatile and high-quality 3D asset creation. The cornerstone is a unified Structured LATent (SLAT) representation which allows decoding to different output formats, such as Radiance Fields, 3D Gaussians, and meshes. This is achieved by integrating a sparsely-populated 3D grid with dense multiview visual features extracted from a powerful vision foundation model, comprehensively capturing both structural (geometry) and textural (appearance) information while maintaining flexibility during decoding. We employ rectified flow transformers tailored for SLAT as our 3D generation models and train models with up to 2 billion parameters on a large 3D asset dataset of 500K diverse objects. Our model generates high-quality results with text or image conditions, significantly surpassing existing methods, including recent ones at similar scales. We showcase flexible output format selection and local 3D editing capabilities which were not offered by previous models. Code, model, and data will be released.", "date": "2024-12-02", "categories": []}
{"id": "2505.24245", "title": "LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework", "abstract": "We present LTM3D, a Latent Token space Modeling framework for conditional 3D shape generation that integrates the strengths of diffusion and auto-regressive (AR) models. While diffusion-based methods effectively model continuous latent spaces and AR models excel at capturing inter-token dependencies, combining these paradigms for 3D shape generation remains a challenge. To address this, LTM3D features a Conditional Distribution Modeling backbone, leveraging a masked autoencoder and a diffusion model to enhance token dependency learning. Additionally, we introduce Prefix Learning, which aligns condition tokens with shape latent tokens during generation, improving flexibility across modalities. We further propose a Latent Token Reconstruction module with Reconstruction-Guided Sampling to reduce uncertainty and enhance structural fidelity in generated shapes. Our approach operates in token space, enabling support for multiple 3D representations, including signed distance fields, point clouds, meshes, and 3D Gaussian Splatting. Extensive experiments on image- and text-conditioned shape generation tasks demonstrate that LTM3D outperforms existing methods in prompt fidelity and structural accuracy while offering a generalizable framework for multi-modal, multi-representation 3D generation.", "date": "2025-05-30", "categories": []}
{"id": "2505.24877", "title": "AdaHuman: Animatable Detailed 3D Human Generation with Compositional Multiview Diffusion", "abstract": "Existing methods for image-to-3D avatar generation struggle to produce highly detailed, animation-ready avatars suitable for real-world applications. We introduce AdaHuman, a novel framework that generates high-fidelity animatable 3D avatars from a single in-the-wild image. AdaHuman incorporates two key innovations: (1) A pose-conditioned 3D joint diffusion model that synthesizes consistent multi-view images in arbitrary poses alongside corresponding 3D Gaussian Splats (3DGS) reconstruction at each diffusion step; (2) A compositional 3DGS refinement module that enhances the details of local body parts through image-to-image refinement and seamlessly integrates them using a novel crop-aware camera ray map, producing a cohesive detailed 3D avatar. These components allow AdaHuman to generate highly realistic standardized A-pose avatars with minimal self-occlusion, enabling rigging and animation with any input motion. Extensive evaluation on public benchmarks and in-the-wild images demonstrates that AdaHuman significantly outperforms state-of-the-art methods in both avatar reconstruction and reposing. Code and models will be publicly available for research purposes.", "date": "2025-05-30", "categories": []}
{"id": "2505.20460", "title": "DIPO: Dual-State Images Controlled Articulated Object Generation Powered by Diverse Data", "abstract": "We present DIPO, a novel framework for the controllable generation of articulated 3D objects from a pair of images: one depicting the object in a resting state and the other in an articulated state. Compared to the single-image approach, our dual-image input imposes only a modest overhead for data collection, but at the same time provides important motion information, which is a reliable guide for predicting kinematic relationships between parts. Specifically, we propose a dual-image diffusion model that captures relationships between the image pair to generate part layouts and joint parameters. In addition, we introduce a Chain-of-Thought (CoT) based graph reasoner that explicitly infers part connectivity relationships. To further improve robustness and generalization on complex articulated objects, we develop a fully automated dataset expansion pipeline, name LEGO-Art, that enriches the diversity and complexity of PartNet-Mobility dataset. We propose PM-X, a large-scale dataset of complex articulated 3D objects, accompanied by rendered images, URDF annotations, and textual descriptions. Extensive experiments demonstrate that DIPO significantly outperforms existing baselines in both the resting state and the articulated state, while the proposed PM-X dataset further enhances generalization to diverse and structurally complex articulated objects. Our code and dataset will be released to the community upon publication.", "date": "2025-05-26", "categories": []}
{"id": "2505.21050", "title": "Advancing high-fidelity 3D and Texture Generation with 2.5D latents", "abstract": "Despite the availability of large-scale 3D datasets and advancements in 3D generative models, the complexity and uneven quality of 3D geometry and texture data continue to hinder the performance of 3D generation techniques. In most existing approaches, 3D geometry and texture are generated in separate stages using different models and non-unified representations, frequently leading to unsatisfactory coherence between geometry and texture. To address these challenges, we propose a novel framework for joint generation of 3D geometry and texture. Specifically, we focus in generate a versatile 2.5D representations that can be seamlessly transformed between 2D and 3D. Our approach begins by integrating multiview RGB, normal, and coordinate images into a unified representation, termed as 2.5D latents. Next, we adapt pre-trained 2D foundation models for high-fidelity 2.5D generation, utilizing both text and image conditions. Finally, we introduce a lightweight 2.5D-to-3D refiner-decoder framework that efficiently generates detailed 3D representations from 2.5D images. Extensive experiments demonstrate that our model not only excels in generating high-quality 3D objects with coherent structure and color from text and image inputs but also significantly outperforms existing methods in geometry-conditioned texture generation.", "date": "2025-05-27", "categories": []}
{"id": "2505.19868", "title": "Harnessing the Power of Training-Free Techniques in Text-to-2D Generation for Text-to-3D Generation via Score Distillation Sampling", "abstract": "Recent studies show that simple training-free techniques can dramatically improve the quality of text-to-2D generation outputs, e.g. Classifier-Free Guidance (CFG) or FreeU. However, these training-free techniques have been underexplored in the lens of Score Distillation Sampling (SDS), which is a popular and effective technique to leverage the power of pretrained text-to-2D diffusion models for various tasks. In this paper, we aim to shed light on the effect such training-free techniques have on SDS, via a particular application of text-to-3D generation via 2D lifting. We present our findings, which show that varying the scales of CFG presents a trade-off between object size and surface smoothness, while varying the scales of FreeU presents a trade-off between texture details and geometric errors. Based on these findings, we provide insights into how we can effectively harness training-free techniques for SDS, via a strategic scaling of such techniques in a dynamic manner with respect to the timestep or optimization iteration step. We show that using our proposed scheme strikes a favorable balance between texture details and surface smoothness in text-to-3D generations, while preserving the size of the output and mitigating the occurrence of geometric defects.", "date": "2025-05-26", "categories": []}
{"id": "2505.17412", "title": "Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention", "abstract": "Generating high-resolution 3D shapes using volumetric representations such as Signed Distance Functions (SDFs) presents substantial computational and memory challenges. We introduce Direct3D-S2, a scalable 3D generation framework based on sparse volumes that achieves superior output quality with dramatically reduced training costs. Our key innovation is the Spatial Sparse Attention (SSA) mechanism, which greatly enhances the efficiency of Diffusion Transformer (DiT) computations on sparse volumetric data. SSA allows the model to effectively process large token sets within sparse volumes, substantially reducing computational overhead and achieving a 3.9x speedup in the forward pass and a 9.6x speedup in the backward pass. Our framework also includes a variational autoencoder (VAE) that maintains a consistent sparse volumetric format across input, latent, and output stages. Compared to previous methods with heterogeneous representations in 3D VAE, this unified design significantly improves training efficiency and stability. Our model is trained on public available datasets, and experiments demonstrate that Direct3D-S2 not only surpasses state-of-the-art methods in generation quality and efficiency, but also enables training at 1024 resolution using only 8 GPUs, a task typically requiring at least 32 GPUs for volumetric representations at 256 resolution, thus making gigascale 3D generation both practical and accessible. Project page: https://www.neural4d.com/research/direct3d-s2.", "date": "2025-05-23", "categories": []}
{"id": "2505.15528", "title": "PlantDreamer: Achieving Realistic 3D Plant Models with Diffusion-Guided Gaussian Splatting", "abstract": "Recent years have seen substantial improvements in the ability to generate synthetic 3D objects using AI. However, generating complex 3D objects, such as plants, remains a considerable challenge. Current generative 3D models struggle with plant generation compared to general objects, limiting their usability in plant analysis tools, which require fine detail and accurate geometry. We introduce PlantDreamer, a novel approach to 3D synthetic plant generation, which can achieve greater levels of realism for complex plant geometry and textures than available text-to-3D models. To achieve this, our new generation pipeline leverages a depth ControlNet, fine-tuned Low-Rank Adaptation and an adaptable Gaussian culling algorithm, which directly improve textural realism and geometric integrity of generated 3D plant models. Additionally, PlantDreamer enables both purely synthetic plant generation, by leveraging L-System-generated meshes, and the enhancement of real-world plant point clouds by converting them into 3D Gaussian Splats. We evaluate our approach by comparing its outputs with state-of-the-art text-to-3D models, demonstrating that PlantDreamer outperforms existing methods in producing high-fidelity synthetic plants. Our results indicate that our approach not only advances synthetic plant generation, but also facilitates the upgrading of legacy point cloud datasets, making it a valuable tool for 3D phenotyping applications.", "date": "2025-05-21", "categories": []}
{"id": "2505.13573", "title": "FreeMesh: Boosting Mesh Generation with Coordinates Merging", "abstract": "The next-coordinate prediction paradigm has emerged as the de facto standard in current auto-regressive mesh generation methods. Despite their effectiveness, there is no efficient measurement for the various tokenizers that serialize meshes into sequences. In this paper, we introduce a new metric Per-Token-Mesh-Entropy (PTME) to evaluate the existing mesh tokenizers theoretically without any training. Building upon PTME, we propose a plug-and-play tokenization technique called coordinate merging. It further improves the compression ratios of existing tokenizers by rearranging and merging the most frequent patterns of coordinates. Through experiments on various tokenization methods like MeshXL, MeshAnything V2, and Edgerunner, we further validate the performance of our method. We hope that the proposed PTME and coordinate merging can enhance the existing mesh tokenizers and guide the further development of native mesh generation.", "date": "2025-05-19", "categories": []}
{"id": "2505.13091", "title": "Touch2Shape: Touch-Conditioned 3D Diffusion for Shape Exploration and Reconstruction", "abstract": "Diffusion models have made breakthroughs in 3D generation tasks. Current 3D diffusion models focus on reconstructing target shape from images or a set of partial observations. While excelling in global context understanding, they struggle to capture the local details of complex shapes and limited to the occlusion and lighting conditions. To overcome these limitations, we utilize tactile images to capture the local 3D information and propose a Touch2Shape model, which leverages a touch-conditioned diffusion model to explore and reconstruct the target shape from touch. For shape reconstruction, we have developed a touch embedding module to condition the diffusion model in creating a compact representation and a touch shape fusion module to refine the reconstructed shape. For shape exploration, we combine the diffusion model with reinforcement learning to train a policy. This involves using the generated latent vector from the diffusion model to guide the touch exploration policy training through a novel reward design. Experiments validate the reconstruction quality thorough both qualitatively and quantitative analysis, and our touch exploration policy further boosts reconstruction performance.", "date": "2025-05-19", "categories": []}
{"id": "2207.14425", "title": "3D Cartoon Face Generation with Controllable Expressions from a Single GAN Image", "abstract": "In this paper, we investigate an open research task of generating 3D cartoon face shapes from single 2D GAN generated human faces and without 3D supervision, where we can also manipulate the facial expressions of the 3D shapes. To this end, we discover the semantic meanings of StyleGAN latent space, such that we are able to produce face images of various expressions, poses, and lighting conditions by controlling the latent codes. Specifically, we first finetune the pretrained StyleGAN face model on the cartoon datasets. By feeding the same latent codes to face and cartoon generation models, we aim to realize the translation from 2D human face images to cartoon styled avatars. We then discover semantic directions of the GAN latent space, in an attempt to change the facial expressions while preserving the original identity. As we do not have any 3D annotations for cartoon faces, we manipulate the latent codes to generate images with different poses and lighting conditions, such that we can reconstruct the 3D cartoon face shapes. We validate the efficacy of our method on three cartoon datasets qualitatively and quantitatively.", "date": "2022-07-29", "categories": []}
{"id": "2505.09140", "title": "TopoDiT-3D: Topology-Aware Diffusion Transformer with Bottleneck Structure for 3D Point Cloud Generation", "abstract": "Recent advancements in Diffusion Transformer (DiT) models have significantly improved 3D point cloud generation. However, existing methods primarily focus on local feature extraction while overlooking global topological information, such as voids, which are crucial for maintaining shape consistency and capturing complex geometries. To address this limitation, we propose TopoDiT-3D, a Topology-Aware Diffusion Transformer with a bottleneck structure for 3D point cloud generation. Specifically, we design the bottleneck structure utilizing Perceiver Resampler, which not only offers a mode to integrate topological information extracted through persistent homology into feature learning, but also adaptively filters out redundant local features to improve training efficiency. Experimental results demonstrate that TopoDiT-3D outperforms state-of-the-art models in visual quality, diversity, and training efficiency. Furthermore, TopoDiT-3D demonstrates the importance of rich topological information for 3D point cloud generation and its synergy with conventional local feature learning. Videos and code are available at https://github.com/Zechao-Guan/TopoDiT-3D.", "date": "2025-05-14", "categories": []}
{"id": "2504.14257", "title": "HoLa: B-Rep Generation using a Holistic Latent Representation", "abstract": "We introduce a novel representation for learning and generating Computer-Aided Design (CAD) models in the form of $\\textit{boundary representations}$ (B-Reps). Our representation unifies the continuous geometric properties of B-Rep primitives in different orders (e.g., surfaces and curves) and their discrete topological relations in a $\\textit{holistic latent}$ (HoLa) space. This is based on the simple observation that the topological connection between two surfaces is intrinsically tied to the geometry of their intersecting curve. Such a prior allows us to reformulate topology learning in B-Reps as a geometric reconstruction problem in Euclidean space. Specifically, we eliminate the presence of curves, vertices, and all the topological connections in the latent space by learning to distinguish and derive curve geometries from a pair of surface primitives via a neural intersection network. To this end, our holistic latent space is only defined on surfaces but encodes a full B-Rep model, including the geometry of surfaces, curves, vertices, and their topological relations. Our compact and holistic latent space facilitates the design of a first diffusion-based generator to take on a large variety of inputs including point clouds, single/multi-view images, 2D sketches, and text prompts. Our method significantly reduces ambiguities, redundancies, and incoherences among the generated B-Rep primitives, as well as training complexities inherent in prior multi-step B-Rep learning pipelines, while achieving greatly improved validity rate over current state of the art: 82% vs. $\\approx$50%.", "date": "2025-04-19", "categories": []}
{"id": "2505.07747", "title": "Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets", "abstract": "While generative artificial intelligence has advanced significantly across text, image, audio, and video domains, 3D generation remains comparatively underdeveloped due to fundamental challenges such as data scarcity, algorithmic limitations, and ecosystem fragmentation. To this end, we present Step1X-3D, an open framework addressing these challenges through: (1) a rigorous data curation pipeline processing >5M assets to create a 2M high-quality dataset with standardized geometric and textural properties; (2) a two-stage 3D-native architecture combining a hybrid VAE-DiT geometry generator with an diffusion-based texture synthesis module; and (3) the full open-source release of models, training code, and adaptation modules. For geometry generation, the hybrid VAE-DiT component produces TSDF representations by employing perceiver-based latent encoding with sharp edge sampling for detail preservation. The diffusion-based texture synthesis module then ensures cross-view consistency through geometric conditioning and latent-space synchronization. Benchmark results demonstrate state-of-the-art performance that exceeds existing open-source methods, while also achieving competitive quality with proprietary solutions. Notably, the framework uniquely bridges the 2D and 3D generation paradigms by supporting direct transfer of 2D control techniques~(e.g., LoRA) to 3D synthesis. By simultaneously advancing data quality, algorithmic fidelity, and reproducibility, Step1X-3D aims to establish new standards for open research in controllable 3D asset generation.", "date": "2025-05-12", "categories": []}
{"id": "2505.05505", "title": "Apply Hierarchical-Chain-of-Generation to Complex Attributes Text-to-3D Generation", "abstract": "Recent text-to-3D models can render high-quality assets, yet they still stumble on objects with complex attributes. The key obstacles are: (1) existing text-to-3D approaches typically lift text-to-image models to extract semantics via text encoders, while the text encoder exhibits limited comprehension ability for long descriptions, leading to deviated cross-attention focus, subsequently wrong attribute binding in generated results. (2) Occluded object parts demand a disciplined generation order and explicit part disentanglement. Though some works introduce manual efforts to alleviate the above issues, their quality is unstable and highly reliant on manual information. To tackle above problems, we propose a automated method Hierarchical-Chain-of-Generation (HCoG). It leverages a large language model to decompose the long description into blocks representing different object parts, and orders them from inside out according to occlusions, forming a hierarchical chain. Within each block we first coarsely create components, then precisely bind attributes via target-region localization and corresponding 3D Gaussian kernel optimization. Between blocks, we introduce Gaussian Extension and Label Elimination to seamlessly generate new parts by extending new Gaussian kernels, re-assigning semantic labels, and eliminating unnecessary kernels, ensuring that only relevant parts are added without disrupting previously optimized parts. Experiments confirm that HCoG yields structurally coherent, attribute-faithful 3D objects with complex attributes. The code is available at https://github.com/Wakals/GASCOL .", "date": "2025-05-07", "categories": []}
{"id": "2505.06166", "title": "DiffLocks: Generating 3D Hair from a Single Image using Diffusion Models", "abstract": "We address the task of generating 3D hair geometry from a single image, which is challenging due to the diversity of hairstyles and the lack of paired image-to-3D hair data. Previous methods are primarily trained on synthetic data and cope with the limited amount of such data by using low-dimensional intermediate representations, such as guide strands and scalp-level embeddings, that require post-processing to decode, upsample, and add realism. These approaches fail to reconstruct detailed hair, struggle with curly hair, or are limited to handling only a few hairstyles. To overcome these limitations, we propose DiffLocks, a novel framework that enables detailed reconstruction of a wide variety of hairstyles directly from a single image. First, we address the lack of 3D hair data by automating the creation of the largest synthetic hair dataset to date, containing 40K hairstyles. Second, we leverage the synthetic hair dataset to learn an image-conditioned diffusion-transfomer model that generates accurate 3D strands from a single frontal image. By using a pretrained image backbone, our method generalizes to in-the-wild images despite being trained only on synthetic data. Our diffusion model predicts a scalp texture map in which any point in the map contains the latent code for an individual hair strand. These codes are directly decoded to 3D strands without post-processing techniques. Representing individual strands, instead of guide strands, enables the transformer to model the detailed spatial structure of complex hairstyles. With this, DiffLocks can recover highly curled hair, like afro hairstyles, from a single image for the first time. Data and code is available at https://radualexandru.github.io/difflocks/", "date": "2025-05-09", "categories": []}
{"id": "2505.05474", "title": "3D Scene Generation: A Survey", "abstract": "  3D scene generation seeks to synthesize spatially structured, semantically\nmeaningful, and photorealistic environments for applications such as immersive\nmedia, robotics, autonomous driving, and embodied AI. Early methods based on\nprocedural rules offered scalability but limited diversity. Recent advances in\ndeep generative models (e.g., GANs, diffusion models) and 3D representations\n(e.g., NeRF, 3D Gaussians) have enabled the learning of real-world scene\ndistributions, improving fidelity, diversity, and view consistency. Recent\nadvances like diffusion models bridge 3D scene synthesis and photorealism by\nreframing generation as image or video synthesis problems. This survey provides\na systematic overview of state-of-the-art approaches, organizing them into four\nparadigms: procedural generation, neural 3D-based generation, image-based\ngeneration, and video-based generation. We analyze their technical foundations,\ntrade-offs, and representative results, and review commonly used datasets,\nevaluation protocols, and downstream applications. We conclude by discussing\nkey challenges in generation capacity, 3D representation, data and annotations,\nand evaluation, and outline promising directions including higher fidelity,\nphysics-aware and interactive generation, and unified perception-generation\nmodels. This review organizes recent advances in 3D scene generation and\nhighlights promising directions at the intersection of generative AI, 3D\nvision, and embodied intelligence. To track ongoing developments, we maintain\nan up-to-date project page:\nhttps://github.com/hzxie/Awesome-3D-Scene-Generation.\n", "date": "2025-05-08", "categories": []}
{"id": "2505.04622", "title": "PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with\n  Auto-Regressive Transformer", "abstract": "  Shape primitive abstraction, which decomposes complex 3D shapes into simple\ngeometric elements, plays a crucial role in human visual cognition and has\nbroad applications in computer vision and graphics. While recent advances in 3D\ncontent generation have shown remarkable progress, existing primitive\nabstraction methods either rely on geometric optimization with limited semantic\nunderstanding or learn from small-scale, category-specific datasets, struggling\nto generalize across diverse shape categories. We present PrimitiveAnything, a\nnovel framework that reformulates shape primitive abstraction as a primitive\nassembly generation task. PrimitiveAnything includes a shape-conditioned\nprimitive transformer for auto-regressive generation and an ambiguity-free\nparameterization scheme to represent multiple types of primitives in a unified\nmanner. The proposed framework directly learns the process of primitive\nassembly from large-scale human-crafted abstractions, enabling it to capture\nhow humans decompose complex shapes into primitive elements. Through extensive\nexperiments, we demonstrate that PrimitiveAnything can generate high-quality\nprimitive assemblies that better align with human perception while maintaining\ngeometric fidelity across diverse shape categories. It benefits various 3D\napplications and shows potential for enabling primitive-based user-generated\ncontent (UGC) in games. Project page: https://primitiveanything.github.io\n", "date": "2025-05-07", "categories": []}
{"id": "2411.15279", "title": "Don't Mesh with Me: Generating Constructive Solid Geometry Instead of\n  Meshes by Fine-Tuning a Code-Generation LLM", "abstract": "  While recent advancements in machine learning, such as LLMs, are\nrevolutionizing software development and creative industries, they have had\nminimal impact on engineers designing mechanical parts, which remains largely a\nmanual process. Existing approaches to generating 3D geometry most commonly use\nmeshes as a 3D representation. While meshes are suitable for assets in video\ngames or animations, they lack sufficient precision and adaptability for\nmechanical engineering purposes. This paper introduces a novel approach for the\ngeneration of 3D geometry that generates surface-based Constructive Solid\nGeometry (CSG) by leveraging a code-generation LLM. First, we create a dataset\nof 3D mechanical parts represented as code scripts by converting Boundary\nRepresentation geometry (BREP) into CSG-based Python scripts. Second, we create\nannotations in natural language using GPT-4. The resulting dataset is used to\nfine-tune a code-generation LLM. The fine-tuned LLM can complete geometries\nbased on positional input and natural language in a plausible way,\ndemonstrating geometric understanding.\n", "date": "2024-11-22", "categories": []}
{"id": "2504.19174", "title": "CLR-Wire: Towards Continuous Latent Representations for 3D Curve\n  Wireframe Generation", "abstract": "  We introduce CLR-Wire, a novel framework for 3D curve-based wireframe\ngeneration that integrates geometry and topology into a unified Continuous\nLatent Representation. Unlike conventional methods that decouple vertices,\nedges, and faces, CLR-Wire encodes curves as Neural Parametric Curves along\nwith their topological connectivity into a continuous and fixed-length latent\nspace using an attention-driven variational autoencoder (VAE). This unified\napproach facilitates joint learning and generation of both geometry and\ntopology. To generate wireframes, we employ a flow matching model to\nprogressively map Gaussian noise to these latents, which are subsequently\ndecoded into complete 3D wireframes. Our method provides fine-grained modeling\nof complex shapes and irregular topologies, and supports both unconditional\ngeneration and generation conditioned on point cloud or image inputs.\nExperimental results demonstrate that, compared with state-of-the-art\ngenerative approaches, our method achieves substantial improvements in\naccuracy, novelty, and diversity, offering an efficient and comprehensive\nsolution for CAD design, geometric reconstruction, and 3D content creation.\n", "date": "2025-04-27", "categories": []}
{"id": "2505.01888", "title": "Rethinking Score Distilling Sampling for 3D Editing and Generation", "abstract": "  Score Distillation Sampling (SDS) has emerged as a prominent method for\ntext-to-3D generation by leveraging the strengths of 2D diffusion models.\nHowever, SDS is limited to generation tasks and lacks the capability to edit\nexisting 3D assets. Conversely, variants of SDS that introduce editing\ncapabilities often can not generate new 3D assets effectively. In this work, we\nobserve that the processes of generation and editing within SDS and its\nvariants have unified underlying gradient terms. Building on this insight, we\npropose Unified Distillation Sampling (UDS), a method that seamlessly\nintegrates both the generation and editing of 3D assets. Essentially, UDS\nrefines the gradient terms used in vanilla SDS methods, unifying them to\nsupport both tasks. Extensive experiments demonstrate that UDS not only\noutperforms baseline methods in generating 3D assets with richer details but\nalso excels in editing tasks, thereby bridging the gap between 3D generation\nand editing. The code is available on: https://github.com/xingy038/UDS.\n", "date": "2025-05-03", "categories": []}
{"id": "2405.00998", "title": "Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel\n  Fields", "abstract": "  This paper presents a novel latent 3D diffusion model for the generation of\nneural voxel fields, aiming to achieve accurate part-aware structures. Compared\nto existing methods, there are two key designs to ensure high-quality and\naccurate part-aware generation. On one hand, we introduce a latent 3D diffusion\nprocess for neural voxel fields, enabling generation at significantly higher\nresolutions that can accurately capture rich textural and geometric details. On\nthe other hand, a part-aware shape decoder is introduced to integrate the part\ncodes into the neural voxel fields, guiding the accurate part decomposition and\nproducing high-quality rendering results. Through extensive experimentation and\ncomparisons with state-of-the-art methods, we evaluate our approach across four\ndifferent classes of data. The results demonstrate the superior generative\ncapabilities of our proposed method in part-aware shape generation,\noutperforming existing state-of-the-art methods.\n", "date": "2024-05-02", "categories": []}
{"id": "2504.09149", "title": "MASH: Masked Anchored SpHerical Distances for 3D Shape Representation\n  and Generation", "abstract": "  We introduce Masked Anchored SpHerical Distances (MASH), a novel multi-view\nand parametrized representation of 3D shapes. Inspired by multi-view geometry\nand motivated by the importance of perceptual shape understanding for learning\n3D shapes, MASH represents a 3D shape as a collection of observable local\nsurface patches, each defined by a spherical distance function emanating from\nan anchor point. We further leverage the compactness of spherical harmonics to\nencode the MASH functions, combined with a generalized view cone with a\nparameterized base that masks the spatial extent of the spherical function to\nattain locality. We develop a differentiable optimization algorithm capable of\nconverting any point cloud into a MASH representation accurately approximating\nground-truth surfaces with arbitrary geometry and topology. Extensive\nexperiments demonstrate that MASH is versatile for multiple applications\nincluding surface reconstruction, shape generation, completion, and blending,\nachieving superior performance thanks to its unique representation encompassing\nboth implicit and explicit features.\n", "date": "2025-04-12", "categories": []}
{"id": "2406.03184", "title": "Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion", "abstract": "  Existing single image-to-3D creation methods typically involve a two-stage\nprocess, first generating multi-view images, and then using these images for 3D\nreconstruction. However, training these two stages separately leads to\nsignificant data bias in the inference phase, thus affecting the quality of\nreconstructed results. We introduce a unified 3D generation framework, named\nOuroboros3D, which integrates diffusion-based multi-view image generation and\n3D reconstruction into a recursive diffusion process. In our framework, these\ntwo modules are jointly trained through a self-conditioning mechanism, allowing\nthem to adapt to each other's characteristics for robust inference. During the\nmulti-view denoising process, the multi-view diffusion model uses the 3D-aware\nmaps rendered by the reconstruction module at the previous timestep as\nadditional conditions. The recursive diffusion framework with 3D-aware feedback\nunites the entire process and improves geometric consistency.Experiments show\nthat our framework outperforms separation of these two stages and existing\nmethods that combine them at the inference phase. Project page:\nhttps://costwen.github.io/Ouroboros3D/\n", "date": "2024-06-05", "categories": []}
{"id": "2409.08215", "title": "LT3SD: Latent Trees for 3D Scene Diffusion", "abstract": "  We present LT3SD, a novel latent diffusion model for large-scale 3D scene\ngeneration. Recent advances in diffusion models have shown impressive results\nin 3D object generation, but are limited in spatial extent and quality when\nextended to 3D scenes. To generate complex and diverse 3D scene structures, we\nintroduce a latent tree representation to effectively encode both\nlower-frequency geometry and higher-frequency detail in a coarse-to-fine\nhierarchy. We can then learn a generative diffusion process in this latent 3D\nscene space, modeling the latent components of a scene at each resolution\nlevel. To synthesize large-scale scenes with varying sizes, we train our\ndiffusion model on scene patches and synthesize arbitrary-sized output 3D\nscenes through shared diffusion generation across multiple scene patches.\nThrough extensive experiments, we demonstrate the efficacy and benefits of\nLT3SD for large-scale, high-quality unconditional 3D scene generation and for\nprobabilistic completion for partial scene observations.\n", "date": "2024-09-12", "categories": []}
{"id": "2403.09326", "title": "HeadEvolver: Text to Head Avatars via Expressive and\n  Attribute-Preserving Mesh Deformation", "abstract": "  Current text-to-avatar methods often rely on implicit representations (e.g.,\nNeRF, SDF, and DMTet), leading to 3D content that artists cannot easily edit\nand animate in graphics software. This paper introduces a novel framework for\ngenerating stylized head avatars from text guidance, which leverages locally\nlearnable mesh deformation and 2D diffusion priors to achieve high-quality\ndigital assets for attribute-preserving manipulation. Given a template mesh,\nour method represents mesh deformation with per-face Jacobians and adaptively\nmodulates local deformation using a learnable vector field. This vector field\nenables anisotropic scaling while preserving the rotation of vertices, which\ncan better express identity and geometric details. We employ landmark- and\ncontour-based regularization terms to balance the expressiveness and\nplausibility of generated avatars from multiple views without relying on any\nspecific shape prior. Our framework can generate realistic shapes and textures\nthat can be further edited via text, while supporting seamless editing using\nthe preserved attributes from the template mesh, such as 3DMM parameters,\nblendshapes, and UV coordinates. Extensive experiments demonstrate that our\nframework can generate diverse and expressive head avatars with high-quality\nmeshes that artists can easily manipulate in graphics software, facilitating\ndownstream applications such as efficient asset creation and animation with\npreserved attributes.\n", "date": "2024-03-14", "categories": []}
{"id": "2504.19545", "title": "Point2Quad: Generating Quad Meshes from Point Clouds via Face Prediction", "abstract": "  Quad meshes are essential in geometric modeling and computational mechanics.\nAlthough learning-based methods for triangle mesh demonstrate considerable\nadvancements, quad mesh generation remains less explored due to the challenge\nof ensuring coplanarity, convexity, and quad-only meshes. In this paper, we\npresent Point2Quad, the first learning-based method for quad-only mesh\ngeneration from point clouds. The key idea is learning to identify quad mesh\nwith fused pointwise and facewise features. Specifically, Point2Quad begins\nwith a k-NN-based candidate generation considering the coplanarity and\nsquareness. Then, two encoders are followed to extract geometric and\ntopological features that address the challenge of quad-related constraints,\nespecially by combining in-depth quadrilaterals-specific characteristics.\nSubsequently, the extracted features are fused to train the classifier with a\ndesigned compound loss. The final results are derived after the refinement by a\nquad-specific post-processing. Extensive experiments on both clear and noise\ndata demonstrate the effectiveness and superiority of Point2Quad, compared to\nbaseline methods under comprehensive metrics.\n", "date": "2025-04-28", "categories": []}
{"id": "2503.20519", "title": "MAR-3D: Progressive Masked Auto-regressor for High-Resolution 3D\n  Generation", "abstract": "  Recent advances in auto-regressive transformers have revolutionized\ngenerative modeling across different domains, from language processing to\nvisual generation, demonstrating remarkable capabilities. However, applying\nthese advances to 3D generation presents three key challenges: the unordered\nnature of 3D data conflicts with sequential next-token prediction paradigm,\nconventional vector quantization approaches incur substantial compression loss\nwhen applied to 3D meshes, and the lack of efficient scaling strategies for\nhigher resolution latent prediction. To address these challenges, we introduce\nMAR-3D, which integrates a pyramid variational autoencoder with a cascaded\nmasked auto-regressive transformer (Cascaded MAR) for progressive latent\nupscaling in the continuous space. Our architecture employs random masking\nduring training and auto-regressive denoising in random order during inference,\nnaturally accommodating the unordered property of 3D latent tokens.\nAdditionally, we propose a cascaded training strategy with condition\naugmentation that enables efficiently up-scale the latent token resolution with\nfast convergence. Extensive experiments demonstrate that MAR-3D not only\nachieves superior performance and generalization capabilities compared to\nexisting methods but also exhibits enhanced scaling capabilities compared to\njoint distribution modeling approaches (e.g., diffusion transformers).\n", "date": "2025-03-26", "categories": []}
{"id": "2311.17510", "title": "StructRe: Rewriting for Structured Shape Modeling", "abstract": "  Man-made 3D shapes are naturally organized in parts and hierarchies; such\nstructures provide important constraints for shape reconstruction and\ngeneration. Modeling shape structures is difficult, because there can be\nmultiple hierarchies for a given shape, causing ambiguity, and across different\ncategories the shape structures are correlated with semantics, limiting\ngeneralization. We present StructRe, a structure rewriting system, as a novel\napproach to structured shape modeling. Given a 3D object represented by points\nand components, StructRe can rewrite it upward into more concise structures, or\ndownward into more detailed structures; by iterating the rewriting process,\nhierarchies are obtained. Such a localized rewriting process enables\nprobabilistic modeling of ambiguous structures and robust generalization across\nobject categories. We train StructRe on PartNet data and show its\ngeneralization to cross-category and multiple object hierarchies, and test its\nextension to ShapeNet. We also demonstrate the benefits of probabilistic and\ngeneralizable structure modeling for shape reconstruction, generation and\nediting tasks.\n", "date": "2023-11-29", "categories": []}
{"id": "2504.09975", "title": "OctGPT: Octree-based Multiscale Autoregressive Models for 3D Shape\n  Generation", "abstract": "  Autoregressive models have achieved remarkable success across various\ndomains, yet their performance in 3D shape generation lags significantly behind\nthat of diffusion models. In this paper, we introduce OctGPT, a novel\nmultiscale autoregressive model for 3D shape generation that dramatically\nimproves the efficiency and performance of prior 3D autoregressive approaches,\nwhile rivaling or surpassing state-of-the-art diffusion models. Our method\nemploys a serialized octree representation to efficiently capture the\nhierarchical and spatial structures of 3D shapes. Coarse geometry is encoded\nvia octree structures, while fine-grained details are represented by binary\ntokens generated using a vector quantized variational autoencoder (VQVAE),\ntransforming 3D shapes into compact multiscale binary sequences suitable for\nautoregressive prediction. To address the computational challenges of handling\nlong sequences, we incorporate octree-based transformers enhanced with 3D\nrotary positional encodings, scale-specific embeddings, and token-parallel\ngeneration schemes. These innovations reduce training time by 13 folds and\ngeneration time by 69 folds, enabling the efficient training of high-resolution\n3D shapes, e.g.,$1024^3$, on just four NVIDIA 4090 GPUs only within days.\nOctGPT showcases exceptional versatility across various tasks, including text-,\nsketch-, and image-conditioned generation, as well as scene-level synthesis\ninvolving multiple objects. Extensive experiments demonstrate that OctGPT\naccelerates convergence and improves generation quality over prior\nautoregressive methods, offering a new paradigm for high-quality, scalable 3D\ncontent creation. Our code and trained models are available at\nhttps://github.com/octree-nn/octgpt.\n", "date": "2025-04-14", "categories": []}
{"id": "2504.10316", "title": "ESCT3D: Efficient and Selectively Controllable Text-Driven 3D Content\n  Generation with Gaussian Splatting", "abstract": "  In recent years, significant advancements have been made in text-driven 3D\ncontent generation. However, several challenges remain. In practical\napplications, users often provide extremely simple text inputs while expecting\nhigh-quality 3D content. Generating optimal results from such minimal text is a\ndifficult task due to the strong dependency of text-to-3D models on the quality\nof input prompts. Moreover, the generation process exhibits high variability,\nmaking it difficult to control. Consequently, multiple iterations are typically\nrequired to produce content that meets user expectations, reducing generation\nefficiency. To address this issue, we propose GPT-4V for self-optimization,\nwhich significantly enhances the efficiency of generating satisfactory content\nin a single attempt. Furthermore, the controllability of text-to-3D generation\nmethods has not been fully explored. Our approach enables users to not only\nprovide textual descriptions but also specify additional conditions, such as\nstyle, edges, scribbles, poses, or combinations of multiple conditions,\nallowing for more precise control over the generated 3D content. Additionally,\nduring training, we effectively integrate multi-view information, including\nmulti-view depth, masks, features, and images, to address the common Janus\nproblem in 3D content generation. Extensive experiments demonstrate that our\nmethod achieves robust generalization, facilitating the efficient and\ncontrollable generation of high-quality 3D content.\n", "date": "2025-04-14", "categories": []}
{"id": "2504.08100", "title": "ContrastiveGaussian: High-Fidelity 3D Generation with Contrastive\n  Learning and Gaussian Splatting", "abstract": "  Creating 3D content from single-view images is a challenging problem that has\nattracted considerable attention in recent years. Current approaches typically\nutilize score distillation sampling (SDS) from pre-trained 2D diffusion models\nto generate multi-view 3D representations. Although some methods have made\nnotable progress by balancing generation speed and model quality, their\nperformance is often limited by the visual inconsistencies of the diffusion\nmodel outputs. In this work, we propose ContrastiveGaussian, which integrates\ncontrastive learning into the generative process. By using a perceptual loss,\nwe effectively differentiate between positive and negative samples, leveraging\nthe visual inconsistencies to improve 3D generation quality. To further enhance\nsample differentiation and improve contrastive learning, we incorporate a\nsuper-resolution model and introduce another Quantity-Aware Triplet Loss to\naddress varying sample distributions during training. Our experiments\ndemonstrate that our approach achieves superior texture fidelity and improved\ngeometric consistency.\n", "date": "2025-04-10", "categories": []}
{"id": "2411.08033", "title": "GaussianAnything: Interactive Point Cloud Flow Matching For 3D Object\n  Generation", "abstract": "  While 3D content generation has advanced significantly, existing methods\nstill face challenges with input formats, latent space design, and output\nrepresentations. This paper introduces a novel 3D generation framework that\naddresses these challenges, offering scalable, high-quality 3D generation with\nan interactive Point Cloud-structured Latent space. Our framework employs a\nVariational Autoencoder (VAE) with multi-view posed RGB-D(epth)-N(ormal)\nrenderings as input, using a unique latent space design that preserves 3D shape\ninformation, and incorporates a cascaded latent flow-based model for improved\nshape-texture disentanglement. The proposed method, GaussianAnything, supports\nmulti-modal conditional 3D generation, allowing for point cloud, caption, and\nsingle image inputs. Notably, the newly proposed latent space naturally enables\ngeometry-texture disentanglement, thus allowing 3D-aware editing. Experimental\nresults demonstrate the effectiveness of our approach on multiple datasets,\noutperforming existing native 3D methods in both text- and image-conditioned 3D\ngeneration.\n", "date": "2024-11-12", "categories": []}
{"id": "2408.13055", "title": "Atlas Gaussians Diffusion for 3D Generation", "abstract": "  Using the latent diffusion model has proven effective in developing novel 3D\ngeneration techniques. To harness the latent diffusion model, a key challenge\nis designing a high-fidelity and efficient representation that links the latent\nspace and the 3D space. In this paper, we introduce Atlas Gaussians, a novel\nrepresentation for feed-forward native 3D generation. Atlas Gaussians represent\na shape as the union of local patches, and each patch can decode 3D Gaussians.\nWe parameterize a patch as a sequence of feature vectors and design a learnable\nfunction to decode 3D Gaussians from the feature vectors. In this process, we\nincorporate UV-based sampling, enabling the generation of a sufficiently large,\nand theoretically infinite, number of 3D Gaussian points. The large amount of\n3D Gaussians enables the generation of high-quality details. Moreover, due to\nlocal awareness of the representation, the transformer-based decoding procedure\noperates on a patch level, ensuring efficiency. We train a variational\nautoencoder to learn the Atlas Gaussians representation, and then apply a\nlatent diffusion model on its latent space for learning 3D Generation.\nExperiments show that our approach outperforms the prior arts of feed-forward\nnative 3D generation. Project page:\nhttps://yanghtr.github.io/projects/atlas_gaussians.\n", "date": "2024-08-23", "categories": []}
{"id": "2409.16294", "title": "GenCAD: Image-Conditioned Computer-Aided Design Generation with\n  Transformer-Based Contrastive Representation and Diffusion Priors", "abstract": "  The creation of manufacturable and editable 3D shapes through Computer-Aided\nDesign (CAD) remains a highly manual and time-consuming task, hampered by the\ncomplex topology of boundary representations of 3D solids and unintuitive\ndesign tools. While most work in the 3D shape generation literature focuses on\nrepresentations like meshes, voxels, or point clouds, practical engineering\napplications demand the modifiability and manufacturability of CAD models and\nthe ability for multi-modal conditional CAD model generation. This paper\nintroduces GenCAD, a generative model that employs autoregressive transformers\nwith a contrastive learning framework and latent diffusion models to transform\nimage inputs into parametric CAD command sequences, resulting in editable 3D\nshape representations. Extensive evaluations demonstrate that GenCAD\nsignificantly outperforms existing state-of-the-art methods in terms of the\nunconditional and conditional generations of CAD models. Additionally, the\ncontrastive learning framework of GenCAD facilitates the retrieval of CAD\nmodels using image queries from large CAD databases, which is a critical\nchallenge within the CAD community. Our results provide a significant step\nforward in highlighting the potential of generative models to expedite the\nentire design-to-production pipeline and seamlessly integrate different design\nmodalities.\n", "date": "2024-09-08", "categories": []}
{"id": "2504.05720", "title": "QEMesh: Employing A Quadric Error Metrics-Based Representation for Mesh\n  Generation", "abstract": "  Mesh generation plays a crucial role in 3D content creation, as mesh is\nwidely used in various industrial applications. Recent works have achieved\nimpressive results but still face several issues, such as unrealistic patterns\nor pits on surfaces, thin parts missing, and incomplete structures. Most of\nthese problems stem from the choice of shape representation or the capabilities\nof the generative network. To alleviate these, we extend PoNQ, a Quadric Error\nMetrics (QEM)-based representation, and propose a novel model, QEMesh, for\nhigh-quality mesh generation. PoNQ divides the shape surface into tiny patches,\neach represented by a point with its normal and QEM matrix, which preserves\nfine local geometry information. In our QEMesh, we regard these elements as\ngenerable parameters and design a unique latent diffusion model containing a\nnovel multi-decoder VAE for PoNQ parameters generation. Given the latent code\ngenerated by the diffusion model, three parameter decoders produce several PoNQ\nparameters within each voxel cell, and an occupancy decoder predicts which\nvoxel cells containing parameters to form the final shape. Extensive\nevaluations demonstrate that our method generates results with watertight\nsurfaces and is comparable to state-of-the-art methods in several main metrics.\n", "date": "2025-04-08", "categories": []}
{"id": "2410.15391", "title": "Layout-your-3D: Controllable and Precise 3D Generation with 2D Blueprint", "abstract": "  We present Layout-Your-3D, a framework that allows controllable and\ncompositional 3D generation from text prompts. Existing text-to-3D methods\noften struggle to generate assets with plausible object interactions or require\ntedious optimization processes. To address these challenges, our approach\nleverages 2D layouts as a blueprint to facilitate precise and plausible control\nover 3D generation. Starting with a 2D layout provided by a user or generated\nfrom a text description, we first create a coarse 3D scene using a carefully\ndesigned initialization process based on efficient reconstruction models. To\nenforce coherent global 3D layouts and enhance the quality of instance\nappearances, we propose a collision-aware layout optimization process followed\nby instance-wise refinement. Experimental results demonstrate that\nLayout-Your-3D yields more reasonable and visually appealing compositional 3D\nassets while significantly reducing the time required for each prompt.\nAdditionally, Layout-Your-3D can be easily applicable to downstream tasks, such\nas 3D editing and object insertion. Our project page is available\nat:https://colezwhy.github.io/layoutyour3d/\n", "date": "2024-10-20", "categories": []}
{"id": "2504.04454", "title": "PRISM: Probabilistic Representation for Integrated Shape Modeling and\n  Generation", "abstract": "  Despite the advancements in 3D full-shape generation, accurately modeling\ncomplex geometries and semantics of shape parts remains a significant\nchallenge, particularly for shapes with varying numbers of parts. Current\nmethods struggle to effectively integrate the contextual and structural\ninformation of 3D shapes into their generative processes. We address these\nlimitations with PRISM, a novel compositional approach for 3D shape generation\nthat integrates categorical diffusion models with Statistical Shape Models\n(SSM) and Gaussian Mixture Models (GMM). Our method employs compositional SSMs\nto capture part-level geometric variations and uses GMM to represent part\nsemantics in a continuous space. This integration enables both high fidelity\nand diversity in generated shapes while preserving structural coherence.\nThrough extensive experiments on shape generation and manipulation tasks, we\ndemonstrate that our approach significantly outperforms previous methods in\nboth quality and controllability of part-level operations. Our code will be\nmade publicly available.\n", "date": "2025-04-06", "categories": []}
{"id": "2412.07237", "title": "ArtFormer: Controllable Generation of Diverse 3D Articulated Objects", "abstract": "  This paper presents a novel framework for modeling and conditional generation\nof 3D articulated objects. Troubled by flexibility-quality tradeoffs, existing\nmethods are often limited to using predefined structures or retrieving shapes\nfrom static datasets. To address these challenges, we parameterize an\narticulated object as a tree of tokens and employ a transformer to generate\nboth the object's high-level geometry code and its kinematic relations.\nSubsequently, each sub-part's geometry is further decoded using a\nsigned-distance-function (SDF) shape prior, facilitating the synthesis of\nhigh-quality 3D shapes. Our approach enables the generation of diverse objects\nwith high-quality geometry and varying number of parts. Comprehensive\nexperiments on conditional generation from text descriptions demonstrate the\neffectiveness and flexibility of our method.\n", "date": "2024-12-10", "categories": []}
{"id": "2504.00457", "title": "Distilling Multi-view Diffusion Models into 3D Generators", "abstract": "  We introduce DD3G, a formulation that Distills a multi-view Diffusion model\n(MV-DM) into a 3D Generator using gaussian splatting. DD3G compresses and\nintegrates extensive visual and spatial geometric knowledge from the MV-DM by\nsimulating its ordinary differential equation (ODE) trajectory, ensuring the\ndistilled generator generalizes better than those trained solely on 3D data.\nUnlike previous amortized optimization approaches, we align the MV-DM and 3D\ngenerator representation spaces to transfer the teacher's probabilistic flow to\nthe student, thus avoiding inconsistencies in optimization objectives caused by\nprobabilistic sampling. The introduction of probabilistic flow and the coupling\nof various attributes in 3D Gaussians introduce challenges in the generation\nprocess. To tackle this, we propose PEPD, a generator consisting of Pattern\nExtraction and Progressive Decoding phases, which enables efficient fusion of\nprobabilistic flow and converts a single image into 3D Gaussians within 0.06\nseconds. Furthermore, to reduce knowledge loss and overcome sparse-view\nsupervision, we design a joint optimization objective that ensures the quality\nof generated samples through explicit supervision and implicit verification.\nLeveraging existing 2D generation models, we compile 120k high-quality RGBA\nimages for distillation. Experiments on synthetic and public datasets\ndemonstrate the effectiveness of our method. Our project is available at:\nhttps://qinbaigao.github.io/DD3G_project/\n", "date": "2025-04-01", "categories": []}
{"id": "2504.01512", "title": "High-fidelity 3D Object Generation from Single Image with RGBN-Volume\n  Gaussian Reconstruction Model", "abstract": "  Recently single-view 3D generation via Gaussian splatting has emerged and\ndeveloped quickly. They learn 3D Gaussians from 2D RGB images generated from\npre-trained multi-view diffusion (MVD) models, and have shown a promising\navenue for 3D generation through a single image. Despite the current progress,\nthese methods still suffer from the inconsistency jointly caused by the\ngeometric ambiguity in the 2D images, and the lack of structure of 3D\nGaussians, leading to distorted and blurry 3D object generation. In this paper,\nwe propose to fix these issues by GS-RGBN, a new RGBN-volume Gaussian\nReconstruction Model designed to generate high-fidelity 3D objects from\nsingle-view images. Our key insight is a structured 3D representation can\nsimultaneously mitigate the afore-mentioned two issues. To this end, we propose\na novel hybrid Voxel-Gaussian representation, where a 3D voxel representation\ncontains explicit 3D geometric information, eliminating the geometric ambiguity\nfrom 2D images. It also structures Gaussians during learning so that the\noptimization tends to find better local optima. Our 3D voxel representation is\nobtained by a fusion module that aligns RGB features and surface normal\nfeatures, both of which can be estimated from 2D images. Extensive experiments\ndemonstrate the superiority of our methods over prior works in terms of\nhigh-quality reconstruction results, robust generalization, and good\nefficiency.\n", "date": "2025-04-02", "categories": []}
{"id": "2411.16820", "title": "DetailGen3D: Generative 3D Geometry Enhancement via Data-Dependent Flow", "abstract": "  Modern 3D generation methods can rapidly create shapes from sparse or single\nviews, but their outputs often lack geometric detail due to computational\nconstraints. We present DetailGen3D, a generative approach specifically\ndesigned to enhance these generated 3D shapes. Our key insight is to model the\ncoarse-to-fine transformation directly through data-dependent flows in latent\nspace, avoiding the computational overhead of large-scale 3D generative models.\nWe introduce a token matching strategy that ensures accurate spatial\ncorrespondence during refinement, enabling local detail synthesis while\npreserving global structure. By carefully designing our training data to match\nthe characteristics of synthesized coarse shapes, our method can effectively\nenhance shapes produced by various 3D generation and reconstruction approaches,\nfrom single-view to sparse multi-view inputs. Extensive experiments demonstrate\nthat DetailGen3D achieves high-fidelity geometric detail synthesis while\nmaintaining efficiency in training.\n", "date": "2024-11-25", "categories": []}
{"id": "2503.22236", "title": "Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal\n  Bridging", "abstract": "  With the growing demand for high-fidelity 3D models from 2D images, existing\nmethods still face significant challenges in accurately reproducing\nfine-grained geometric details due to limitations in domain gaps and inherent\nambiguities in RGB images. To address these issues, we propose Hi3DGen, a novel\nframework for generating high-fidelity 3D geometry from images via normal\nbridging. Hi3DGen consists of three key components: (1) an image-to-normal\nestimator that decouples the low-high frequency image pattern with noise\ninjection and dual-stream training to achieve generalizable, stable, and sharp\nestimation; (2) a normal-to-geometry learning approach that uses\nnormal-regularized latent diffusion learning to enhance 3D geometry generation\nfidelity; and (3) a 3D data synthesis pipeline that constructs a high-quality\ndataset to support training. Extensive experiments demonstrate the\neffectiveness and superiority of our framework in generating rich geometric\ndetails, outperforming state-of-the-art methods in terms of fidelity. Our work\nprovides a new direction for high-fidelity 3D geometry generation from images\nby leveraging normal maps as an intermediate representation.\n", "date": "2025-03-28", "categories": []}
{"id": "2503.23022", "title": "MeshCraft: Exploring Efficient and Controllable Mesh Generation with\n  Flow-based DiTs", "abstract": "  In the domain of 3D content creation, achieving optimal mesh topology through\nAI models has long been a pursuit for 3D artists. Previous methods, such as\nMeshGPT, have explored the generation of ready-to-use 3D objects via mesh\nauto-regressive techniques. While these methods produce visually impressive\nresults, their reliance on token-by-token predictions in the auto-regressive\nprocess leads to several significant limitations. These include extremely slow\ngeneration speeds and an uncontrollable number of mesh faces. In this paper, we\nintroduce MeshCraft, a novel framework for efficient and controllable mesh\ngeneration, which leverages continuous spatial diffusion to generate discrete\ntriangle faces. Specifically, MeshCraft consists of two core components: 1) a\ntransformer-based VAE that encodes raw meshes into continuous face-level tokens\nand decodes them back to the original meshes, and 2) a flow-based diffusion\ntransformer conditioned on the number of faces, enabling the generation of\nhigh-quality 3D meshes with a predefined number of faces. By utilizing the\ndiffusion model for the simultaneous generation of the entire mesh topology,\nMeshCraft achieves high-fidelity mesh generation at significantly faster speeds\ncompared to auto-regressive methods. Specifically, MeshCraft can generate an\n800-face mesh in just 3.2 seconds (35$\\times$ faster than existing baselines).\nExtensive experiments demonstrate that MeshCraft outperforms state-of-the-art\ntechniques in both qualitative and quantitative evaluations on ShapeNet dataset\nand demonstrates superior performance on Objaverse dataset. Moreover, it\nintegrates seamlessly with existing conditional guidance strategies, showcasing\nits potential to relieve artists from the time-consuming manual work involved\nin mesh creation.\n", "date": "2025-03-29", "categories": []}
{"id": "2412.03889", "title": "CRAFT: Designing Creative and Functional 3D Objects", "abstract": "  For designing a wide range of everyday objects, the design process should be\naware of both the human body and the underlying semantics of the design\nspecification. However, these two objectives present significant challenges to\nthe current AI-based designing tools. In this work, we present a method to\nsynthesize body-aware 3D objects from a base mesh given an input body geometry\nand either text or image as guidance. The generated objects can be simulated on\nvirtual characters, or fabricated for real-world use. We propose to use a mesh\ndeformation procedure that optimizes for both semantic alignment as well as\ncontact and penetration losses. Using our method, users can generate both\nvirtual or real-world objects from text, image, or sketch, without the need for\nmanual artist intervention. We present both qualitative and quantitative\nresults on various object categories, demonstrating the effectiveness of our\napproach.\n", "date": "2024-12-05", "categories": []}
{"id": "2503.21830", "title": "Shape Generation via Weight Space Learning", "abstract": "  Foundation models for 3D shape generation have recently shown a remarkable\ncapacity to encode rich geometric priors across both global and local\ndimensions. However, leveraging these priors for downstream tasks can be\nchallenging as real-world data are often scarce or noisy, and traditional\nfine-tuning can lead to catastrophic forgetting. In this work, we treat the\nweight space of a large 3D shape-generative model as a data modality that can\nbe explored directly. We hypothesize that submanifolds within this\nhigh-dimensional weight space can modulate topological properties or\nfine-grained part features separately, demonstrating early-stage evidence via\ntwo experiments. First, we observe a sharp phase transition in global\nconnectivity when interpolating in conditioning space, suggesting that small\nchanges in weight space can drastically alter topology. Second, we show that\nlow-dimensional reparameterizations yield controlled local geometry changes\neven with very limited data. These results highlight the potential of weight\nspace learning to unlock new approaches for 3D shape generation and specialized\nfine-tuning.\n", "date": "2025-03-26", "categories": []}
{"id": "2502.06608", "title": "TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified\n  Flow Models", "abstract": "  Recent advancements in diffusion techniques have propelled image and video\ngeneration to unprecedented levels of quality, significantly accelerating the\ndeployment and application of generative AI. However, 3D shape generation\ntechnology has so far lagged behind, constrained by limitations in 3D data\nscale, complexity of 3D data processing, and insufficient exploration of\nadvanced techniques in the 3D domain. Current approaches to 3D shape generation\nface substantial challenges in terms of output quality, generalization\ncapability, and alignment with input conditions. We present TripoSG, a new\nstreamlined shape diffusion paradigm capable of generating high-fidelity 3D\nmeshes with precise correspondence to input images. Specifically, we propose:\n1) A large-scale rectified flow transformer for 3D shape generation, achieving\nstate-of-the-art fidelity through training on extensive, high-quality data. 2)\nA hybrid supervised training strategy combining SDF, normal, and eikonal losses\nfor 3D VAE, achieving high-quality 3D reconstruction performance. 3) A data\nprocessing pipeline to generate 2 million high-quality 3D samples, highlighting\nthe crucial rules for data quality and quantity in training 3D generative\nmodels. Through comprehensive experiments, we have validated the effectiveness\nof each component in our new framework. The seamless integration of these parts\nhas enabled TripoSG to achieve state-of-the-art performance in 3D shape\ngeneration. The resulting 3D shapes exhibit enhanced detail due to\nhigh-resolution capabilities and demonstrate exceptional fidelity to input\nimages. Moreover, TripoSG demonstrates improved versatility in generating 3D\nmodels from diverse image styles and contents, showcasing strong generalization\ncapabilities. To foster progress and innovation in the field of 3D generation,\nwe will make our model publicly available.\n", "date": "2025-02-10", "categories": []}
{"id": "2503.21694", "title": "Progressive Rendering Distillation: Adapting Stable Diffusion for\n  Instant Text-to-Mesh Generation without 3D Data", "abstract": "  It is highly desirable to obtain a model that can generate high-quality 3D\nmeshes from text prompts in just seconds. While recent attempts have adapted\npre-trained text-to-image diffusion models, such as Stable Diffusion (SD), into\ngenerators of 3D representations (e.g., Triplane), they often suffer from poor\nquality due to the lack of sufficient high-quality 3D training data. Aiming at\novercoming the data shortage, we propose a novel training scheme, termed as\nProgressive Rendering Distillation (PRD), eliminating the need for 3D\nground-truths by distilling multi-view diffusion models and adapting SD into a\nnative 3D generator. In each iteration of training, PRD uses the U-Net to\nprogressively denoise the latent from random noise for a few steps, and in each\nstep it decodes the denoised latent into 3D output. Multi-view diffusion\nmodels, including MVDream and RichDreamer, are used in joint with SD to distill\ntext-consistent textures and geometries into the 3D outputs through score\ndistillation. Since PRD supports training without 3D ground-truths, we can\neasily scale up the training data and improve generation quality for\nchallenging text prompts with creative concepts. Meanwhile, PRD can accelerate\nthe inference speed of the generation model in just a few steps. With PRD, we\ntrain a Triplane generator, namely TriplaneTurbo, which adds only $2.5\\%$\ntrainable parameters to adapt SD for Triplane generation. TriplaneTurbo\noutperforms previous text-to-3D generators in both efficiency and quality.\nSpecifically, it can produce high-quality 3D meshes in 1.2 seconds and\ngeneralize well for challenging text input. The code is available at\nhttps://github.com/theEricMa/TriplaneTurbo.\n", "date": "2025-03-27", "categories": []}
{"id": "2503.21732", "title": "SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling", "abstract": "  Creating high-fidelity 3D meshes with arbitrary topology, including open\nsurfaces and complex interiors, remains a significant challenge. Existing\nimplicit field methods often require costly and detail-degrading watertight\nconversion, while other approaches struggle with high resolutions. This paper\nintroduces SparseFlex, a novel sparse-structured isosurface representation that\nenables differentiable mesh reconstruction at resolutions up to $1024^3$\ndirectly from rendering losses. SparseFlex combines the accuracy of Flexicubes\nwith a sparse voxel structure, focusing computation on surface-adjacent regions\nand efficiently handling open surfaces. Crucially, we introduce a frustum-aware\nsectional voxel training strategy that activates only relevant voxels during\nrendering, dramatically reducing memory consumption and enabling\nhigh-resolution training. This also allows, for the first time, the\nreconstruction of mesh interiors using only rendering supervision. Building\nupon this, we demonstrate a complete shape modeling pipeline by training a\nvariational autoencoder (VAE) and a rectified flow transformer for high-quality\n3D shape generation. Our experiments show state-of-the-art reconstruction\naccuracy, with a ~82% reduction in Chamfer Distance and a ~88% increase in\nF-score compared to previous methods, and demonstrate the generation of\nhigh-resolution, detailed 3D shapes with arbitrary topology. By enabling\nhigh-resolution, differentiable mesh reconstruction and generation with\nrendering losses, SparseFlex significantly advances the state-of-the-art in 3D\nshape representation and modeling.\n", "date": "2025-03-27", "categories": []}
{"id": "2503.16302", "title": "Unleashing Vecset Diffusion Model for Fast Shape Generation", "abstract": "  3D shape generation has greatly flourished through the development of\nso-called \"native\" 3D diffusion, particularly through the Vecset Diffusion\nModel (VDM). While recent advancements have shown promising results in\ngenerating high-resolution 3D shapes, VDM still struggles with high-speed\ngeneration. Challenges exist because of difficulties not only in accelerating\ndiffusion sampling but also VAE decoding in VDM, areas under-explored in\nprevious works. To address these challenges, we present FlashVDM, a systematic\nframework for accelerating both VAE and DiT in VDM. For DiT, FlashVDM enables\nflexible diffusion sampling with as few as 5 inference steps and comparable\nquality, which is made possible by stabilizing consistency distillation with\nour newly introduced Progressive Flow Distillation. For VAE, we introduce a\nlightning vecset decoder equipped with Adaptive KV Selection, Hierarchical\nVolume Decoding, and Efficient Network Design. By exploiting the locality of\nthe vecset and the sparsity of shape surface in the volume, our decoder\ndrastically lowers FLOPs, minimizing the overall decoding overhead. We apply\nFlashVDM to Hunyuan3D-2 to obtain Hunyuan3D-2 Turbo. Through systematic\nevaluation, we show that our model significantly outperforms existing fast 3D\ngeneration methods, achieving comparable performance to the state-of-the-art\nwhile reducing inference time by over 45x for reconstruction and 32x for\ngeneration. Code and models are available at\nhttps://github.com/Tencent/FlashVDM.\n", "date": "2025-03-20", "categories": []}
{"id": "2503.20663", "title": "ARMO: Autoregressive Rigging for Multi-Category Objects", "abstract": "  Recent advancements in large-scale generative models have significantly\nimproved the quality and diversity of 3D shape generation. However, most\nexisting methods focus primarily on generating static 3D models, overlooking\nthe potentially dynamic nature of certain shapes, such as humanoids, animals,\nand insects. To address this gap, we focus on rigging, a fundamental task in\nanimation that establishes skeletal structures and skinning for 3D models. In\nthis paper, we introduce OmniRig, the first large-scale rigging dataset,\ncomprising 79,499 meshes with detailed skeleton and skinning information.\nUnlike traditional benchmarks that rely on predefined standard poses (e.g.,\nA-pose, T-pose), our dataset embraces diverse shape categories, styles, and\nposes. Leveraging this rich dataset, we propose ARMO, a novel rigging framework\nthat utilizes an autoregressive model to predict both joint positions and\nconnectivity relationships in a unified manner. By treating the skeletal\nstructure as a complete graph and discretizing it into tokens, we encode the\njoints using an auto-encoder to obtain a latent embedding and an autoregressive\nmodel to predict the tokens. A mesh-conditioned latent diffusion model is used\nto predict the latent embedding for conditional skeleton generation. Our method\naddresses the limitations of regression-based approaches, which often suffer\nfrom error accumulation and suboptimal connectivity estimation. Through\nextensive experiments on the OmniRig dataset, our approach achieves\nstate-of-the-art performance in skeleton prediction, demonstrating improved\ngeneralization across diverse object categories. The code and dataset will be\nmade public for academic use upon acceptance.\n", "date": "2025-03-26", "categories": []}
{"id": "2412.15618", "title": "3D Shape Tokenization via Latent Flow Matching", "abstract": "  We introduce a latent 3D representation that models 3D surfaces as\nprobability density functions in 3D, i.e., p(x,y,z), with flow-matching. Our\nrepresentation is specifically designed for consumption by machine learning\nmodels, offering continuity and compactness by construction while requiring\nonly point clouds and minimal data preprocessing. Despite being a data-driven\nmethod, our use of flow matching in the 3D space enables interesting geometry\nproperties, including the capabilities to perform zero-shot estimation of\nsurface normal and deformation field. We evaluate with several machine learning\ntasks, including 3D-CLIP, unconditional generative models, single-image\nconditioned generative model, and intersection-point estimation. Across all\nexperiments, our models achieve competitive performance to existing baselines,\nwhile requiring less preprocessing and auxiliary information from training\ndata.\n", "date": "2024-12-20", "categories": []}
{"id": "2502.08977", "title": "Text-driven 3D Human Generation via Contrastive Preference Optimization", "abstract": "  Recent advances in Score Distillation Sampling (SDS) have improved 3D human\ngeneration from textual descriptions. However, existing methods still face\nchallenges in accurately aligning 3D models with long and complex textual\ninputs. To address this challenge, we propose a novel framework that introduces\ncontrastive preferences, where human-level preference models, guided by both\npositive and negative prompts, assist SDS for improved alignment. Specifically,\nwe design a preference optimization module that integrates multiple models to\ncomprehensively capture the full range of textual features. Furthermore, we\nintroduce a negation preference module to mitigate over-optimization of\nirrelevant details by leveraging static-dynamic negation prompts, effectively\npreventing ``reward hacking\". Extensive experiments demonstrate that our method\nachieves state-of-the-art results, significantly enhancing texture realism and\nvisual alignment with textual descriptions, particularly for long and complex\ninputs.\n", "date": "2025-02-13", "categories": []}
{"id": "2411.16856", "title": "SAR3D: Autoregressive 3D Object Generation and Understanding via\n  Multi-scale 3D VQVAE", "abstract": "  Autoregressive models have demonstrated remarkable success across various\nfields, from large language models (LLMs) to large multimodal models (LMMs) and\n2D content generation, moving closer to artificial general intelligence (AGI).\nDespite these advances, applying autoregressive approaches to 3D object\ngeneration and understanding remains largely unexplored. This paper introduces\nScale AutoRegressive 3D (SAR3D), a novel framework that leverages a multi-scale\n3D vector-quantized variational autoencoder (VQVAE) to tokenize 3D objects for\nefficient autoregressive generation and detailed understanding. By predicting\nthe next scale in a multi-scale latent representation instead of the next\nsingle token, SAR3D reduces generation time significantly, achieving fast 3D\nobject generation in just 0.82 seconds on an A6000 GPU. Additionally, given the\ntokens enriched with hierarchical 3D-aware information, we finetune a\npretrained LLM on them, enabling multimodal comprehension of 3D content. Our\nexperiments show that SAR3D surpasses current 3D generation methods in both\nspeed and quality and allows LLMs to interpret and caption 3D models\ncomprehensively.\n", "date": "2024-11-25", "categories": []}
{"id": "2412.17808", "title": "Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders", "abstract": "  Recent 3D content generation pipelines commonly employ Variational\nAutoencoders (VAEs) to encode shapes into compact latent representations for\ndiffusion-based generation. However, the widely adopted uniform point sampling\nstrategy in Shape VAE training often leads to a significant loss of geometric\ndetails, limiting the quality of shape reconstruction and downstream generation\ntasks. We present Dora-VAE, a novel approach that enhances VAE reconstruction\nthrough our proposed sharp edge sampling strategy and a dual cross-attention\nmechanism. By identifying and prioritizing regions with high geometric\ncomplexity during training, our method significantly improves the preservation\nof fine-grained shape features. Such sampling strategy and the dual attention\nmechanism enable the VAE to focus on crucial geometric details that are\ntypically missed by uniform sampling approaches. To systematically evaluate VAE\nreconstruction quality, we additionally propose Dora-bench, a benchmark that\nquantifies shape complexity through the density of sharp edges, introducing a\nnew metric focused on reconstruction accuracy at these salient geometric\nfeatures. Extensive experiments on the Dora-bench demonstrate that Dora-VAE\nachieves comparable reconstruction quality to the state-of-the-art dense\nXCube-VAE while requiring a latent space at least 8$\\times$ smaller (1,280 vs.\n> 10,000 codes).\n", "date": "2024-12-23", "categories": []}
{"id": "2502.20732", "title": "CADDreamer: CAD Object Generation from Single-view Images", "abstract": "  Diffusion-based 3D generation has made remarkable progress in recent years.\nHowever, existing 3D generative models often produce overly dense and\nunstructured meshes, which stand in stark contrast to the compact, structured,\nand sharply-edged Computer-Aided Design (CAD) models crafted by human\ndesigners. To address this gap, we introduce CADDreamer, a novel approach for\ngenerating boundary representations (B-rep) of CAD objects from a single image.\nCADDreamer employs a primitive-aware multi-view diffusion model that captures\nboth local geometric details and high-level structural semantics during the\ngeneration process. By encoding primitive semantics into the color domain, the\nmethod leverages the strong priors of pre-trained diffusion models to align\nwith well-defined primitives. This enables the inference of multi-view normal\nmaps and semantic maps from a single image, facilitating the reconstruction of\na mesh with primitive labels. Furthermore, we introduce geometric optimization\ntechniques and topology-preserving extraction methods to mitigate noise and\ndistortion in the generated primitives. These enhancements result in a complete\nand seamless B-rep of the CAD model. Experimental results demonstrate that our\nmethod effectively recovers high-quality CAD objects from single-view images.\nCompared to existing 3D generation techniques, the B-rep models produced by\nCADDreamer are compact in representation, clear in structure, sharp in edges,\nand watertight in topology.\n", "date": "2025-02-28", "categories": []}
{"id": "2503.16653", "title": "iFlame: Interleaving Full and Linear Attention for Efficient Mesh\n  Generation", "abstract": "  This paper propose iFlame, a novel transformer-based network architecture for\nmesh generation. While attention-based models have demonstrated remarkable\nperformance in mesh generation, their quadratic computational complexity limits\nscalability, particularly for high-resolution 3D data. Conversely, linear\nattention mechanisms offer lower computational costs but often struggle to\ncapture long-range dependencies, resulting in suboptimal outcomes. To address\nthis trade-off, we propose an interleaving autoregressive mesh generation\nframework that combines the efficiency of linear attention with the expressive\npower of full attention mechanisms. To further enhance efficiency and leverage\nthe inherent structure of mesh representations, we integrate this interleaving\napproach into an hourglass architecture, which significantly boosts efficiency.\nOur approach reduces training time while achieving performance comparable to\npure attention-based models. To improve inference efficiency, we implemented a\ncaching algorithm that almost doubles the speed and reduces the KV cache size\nby seven-eighths compared to the original Transformer. We evaluate our\nframework on ShapeNet and Objaverse, demonstrating its ability to generate\nhigh-quality 3D meshes efficiently. Our results indicate that the proposed\ninterleaving framework effectively balances computational efficiency and\ngenerative performance, making it a practical solution for mesh generation. The\ntraining takes only 2 days with 4 GPUs on 39k data with a maximum of 4k faces\non Objaverse.\n", "date": "2025-03-20", "categories": []}
{"id": "2412.06699", "title": "You See it, You Got it: Learning 3D Creation on Pose-Free Videos at\n  Scale", "abstract": "  Recent 3D generation models typically rely on limited-scale 3D `gold-labels'\nor 2D diffusion priors for 3D content creation. However, their performance is\nupper-bounded by constrained 3D priors due to the lack of scalable learning\nparadigms. In this work, we present See3D, a visual-conditional multi-view\ndiffusion model trained on large-scale Internet videos for open-world 3D\ncreation. The model aims to Get 3D knowledge by solely Seeing the visual\ncontents from the vast and rapidly growing video data -- You See it, You Got\nit. To achieve this, we first scale up the training data using a proposed data\ncuration pipeline that automatically filters out multi-view inconsistencies and\ninsufficient observations from source videos. This results in a high-quality,\nrichly diverse, large-scale dataset of multi-view images, termed WebVi3D,\ncontaining 320M frames from 16M video clips. Nevertheless, learning generic 3D\npriors from videos without explicit 3D geometry or camera pose annotations is\nnontrivial, and annotating poses for web-scale videos is prohibitively\nexpensive. To eliminate the need for pose conditions, we introduce an\ninnovative visual-condition - a purely 2D-inductive visual signal generated by\nadding time-dependent noise to the masked video data. Finally, we introduce a\nnovel visual-conditional 3D generation framework by integrating See3D into a\nwarping-based pipeline for high-fidelity 3D generation. Our numerical and\nvisual comparisons on single and sparse reconstruction benchmarks show that\nSee3D, trained on cost-effective and scalable video data, achieves notable\nzero-shot and open-world generation capabilities, markedly outperforming models\ntrained on costly and constrained 3D datasets. Please refer to our project page\nat: https://vision.baai.ac.cn/see3d\n", "date": "2024-12-09", "categories": []}
{"id": "2503.01370", "title": "Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation", "abstract": "  Diffusion models have achieved great success in generating 2D images.\nHowever, the quality and generalizability of 3D content generation remain\nlimited. State-of-the-art methods often require large-scale 3D assets for\ntraining, which are challenging to collect. In this work, we introduce\nKiss3DGen (Keep It Simple and Straightforward in 3D Generation), an efficient\nframework for generating, editing, and enhancing 3D objects by repurposing a\nwell-trained 2D image diffusion model for 3D generation. Specifically, we\nfine-tune a diffusion model to generate ''3D Bundle Image'', a tiled\nrepresentation composed of multi-view images and their corresponding normal\nmaps. The normal maps are then used to reconstruct a 3D mesh, and the\nmulti-view images provide texture mapping, resulting in a complete 3D model.\nThis simple method effectively transforms the 3D generation problem into a 2D\nimage generation task, maximizing the utilization of knowledge in pretrained\ndiffusion models. Furthermore, we demonstrate that our Kiss3DGen model is\ncompatible with various diffusion model techniques, enabling advanced features\nsuch as 3D editing, mesh and texture enhancement, etc. Through extensive\nexperiments, we demonstrate the effectiveness of our approach, showcasing its\nability to produce high-quality 3D models efficiently.\n", "date": "2025-03-03", "categories": []}
{"id": "2410.16499", "title": "SINGAPO: Single Image Controlled Generation of Articulated Parts in\n  Objects", "abstract": "  We address the challenge of creating 3D assets for household articulated\nobjects from a single image. Prior work on articulated object creation either\nrequires multi-view multi-state input, or only allows coarse control over the\ngeneration process. These limitations hinder the scalability and practicality\nfor articulated object modeling. In this work, we propose a method to generate\narticulated objects from a single image. Observing the object in resting state\nfrom an arbitrary view, our method generates an articulated object that is\nvisually consistent with the input image. To capture the ambiguity in part\nshape and motion posed by a single view of the object, we design a diffusion\nmodel that learns the plausible variations of objects in terms of geometry and\nkinematics. To tackle the complexity of generating structured data with\nattributes in multiple domains, we design a pipeline that produces articulated\nobjects from high-level structure to geometric details in a coarse-to-fine\nmanner, where we use a part connectivity graph and part abstraction as proxies.\nOur experiments show that our method outperforms the state-of-the-art in\narticulated object creation by a large margin in terms of the generated object\nrealism, resemblance to the input image, and reconstruction quality.\n", "date": "2024-10-21", "categories": []}
{"id": "2503.15562", "title": "Shap-MeD", "abstract": "  We present Shap-MeD, a text-to-3D object generative model specialized in the\nbiomedical domain. The objective of this study is to develop an assistant that\nfacilitates the 3D modeling of medical objects, thereby reducing development\ntime. 3D modeling in medicine has various applications, including surgical\nprocedure simulation and planning, the design of personalized prosthetic\nimplants, medical education, the creation of anatomical models, and the\ndevelopment of research prototypes. To achieve this, we leverage Shap-e, an\nopen-source text-to-3D generative model developed by OpenAI, and fine-tune it\nusing a dataset of biomedical objects. Our model achieved a mean squared error\n(MSE) of 0.089 in latent generation on the evaluation set, compared to Shap-e's\nMSE of 0.147. Additionally, we conducted a qualitative evaluation, comparing\nour model with others in the generation of biomedical objects. Our results\nindicate that Shap-MeD demonstrates higher structural accuracy in biomedical\nobject generation.\n", "date": "2025-03-19", "categories": []}
{"id": "2503.15855", "title": "VideoRFSplat: Direct Scene-Level Text-to-3D Gaussian Splatting\n  Generation with Flexible Pose and Multi-View Joint Modeling", "abstract": "  We propose VideoRFSplat, a direct text-to-3D model leveraging a video\ngeneration model to generate realistic 3D Gaussian Splatting (3DGS) for\nunbounded real-world scenes. To generate diverse camera poses and unbounded\nspatial extent of real-world scenes, while ensuring generalization to arbitrary\ntext prompts, previous methods fine-tune 2D generative models to jointly model\ncamera poses and multi-view images. However, these methods suffer from\ninstability when extending 2D generative models to joint modeling due to the\nmodality gap, which necessitates additional models to stabilize training and\ninference. In this work, we propose an architecture and a sampling strategy to\njointly model multi-view images and camera poses when fine-tuning a video\ngeneration model. Our core idea is a dual-stream architecture that attaches a\ndedicated pose generation model alongside a pre-trained video generation model\nvia communication blocks, generating multi-view images and camera poses through\nseparate streams. This design reduces interference between the pose and image\nmodalities. Additionally, we propose an asynchronous sampling strategy that\ndenoises camera poses faster than multi-view images, allowing rapidly denoised\nposes to condition multi-view generation, reducing mutual ambiguity and\nenhancing cross-modal consistency. Trained on multiple large-scale real-world\ndatasets (RealEstate10K, MVImgNet, DL3DV-10K, ACID), VideoRFSplat outperforms\nexisting text-to-3D direct generation methods that heavily depend on post-hoc\nrefinement via score distillation sampling, achieving superior results without\nsuch refinement.\n", "date": "2025-03-20", "categories": []}
{"id": "2503.15975", "title": "Acc3D: Accelerating Single Image to 3D Diffusion Models via Edge\n  Consistency Guided Score Distillation", "abstract": "  We present Acc3D to tackle the challenge of accelerating the diffusion\nprocess to generate 3D models from single images. To derive high-quality\nreconstructions through few-step inferences, we emphasize the critical issue of\nregularizing the learning of score function in states of random noise. To this\nend, we propose edge consistency, i.e., consistent predictions across the high\nsignal-to-noise ratio region, to enhance a pre-trained diffusion model,\nenabling a distillation-based refinement of the endpoint score function.\nBuilding on those distilled diffusion models, we propose an adversarial\naugmentation strategy to further enrich the generation detail and boost overall\ngeneration quality. The two modules complement each other, mutually reinforcing\nto elevate generative performance. Extensive experiments demonstrate that our\nAcc3D not only achieves over a $20\\times$ increase in computational efficiency\nbut also yields notable quality improvements, compared to the\nstate-of-the-arts.\n", "date": "2025-03-20", "categories": []}
{"id": "2503.15265", "title": "DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement\n  Learning", "abstract": "  Triangle meshes play a crucial role in 3D applications for efficient\nmanipulation and rendering. While auto-regressive methods generate structured\nmeshes by predicting discrete vertex tokens, they are often constrained by\nlimited face counts and mesh incompleteness. To address these challenges, we\npropose DeepMesh, a framework that optimizes mesh generation through two key\ninnovations: (1) an efficient pre-training strategy incorporating a novel\ntokenization algorithm, along with improvements in data curation and\nprocessing, and (2) the introduction of Reinforcement Learning (RL) into 3D\nmesh generation to achieve human preference alignment via Direct Preference\nOptimization (DPO). We design a scoring standard that combines human evaluation\nwith 3D metrics to collect preference pairs for DPO, ensuring both visual\nappeal and geometric accuracy. Conditioned on point clouds and images, DeepMesh\ngenerates meshes with intricate details and precise topology, outperforming\nstate-of-the-art methods in both precision and quality. Project page:\nhttps://zhaorw02.github.io/DeepMesh/\n", "date": "2025-03-19", "categories": []}
{"id": "2406.15020", "title": "A3D: Does Diffusion Dream about 3D Alignment?", "abstract": "  We tackle the problem of text-driven 3D generation from a geometry alignment\nperspective. Given a set of text prompts, we aim to generate a collection of\nobjects with semantically corresponding parts aligned across them. Recent\nmethods based on Score Distillation have succeeded in distilling the knowledge\nfrom 2D diffusion models to high-quality representations of the 3D objects.\nThese methods handle multiple text queries separately, and therefore the\nresulting objects have a high variability in object pose and structure.\nHowever, in some applications, such as 3D asset design, it may be desirable to\nobtain a set of objects aligned with each other. In order to achieve the\nalignment of the corresponding parts of the generated objects, we propose to\nembed these objects into a common latent space and optimize the continuous\ntransitions between these objects. We enforce two kinds of properties of these\ntransitions: smoothness of the transition and plausibility of the intermediate\nobjects along the transition. We demonstrate that both of these properties are\nessential for good alignment. We provide several practical scenarios that\nbenefit from alignment between the objects, including 3D editing and object\nhybridization, and experimentally demonstrate the effectiveness of our method.\nhttps://voyleg.github.io/a3d/\n", "date": "2024-06-21", "categories": []}
{"id": "2409.12957", "title": "3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive\n  Diffusion", "abstract": "  The increasing demand for high-quality 3D assets across various industries\nnecessitates efficient and automated 3D content creation. Despite recent\nadvancements in 3D generative models, existing methods still face challenges\nwith optimization speed, geometric fidelity, and the lack of assets for\nphysically based rendering (PBR). In this paper, we introduce 3DTopia-XL, a\nscalable native 3D generative model designed to overcome these limitations.\n3DTopia-XL leverages a novel primitive-based 3D representation, PrimX, which\nencodes detailed shape, albedo, and material field into a compact tensorial\nformat, facilitating the modeling of high-resolution geometry with PBR assets.\nOn top of the novel representation, we propose a generative framework based on\nDiffusion Transformer (DiT), which comprises 1) Primitive Patch Compression, 2)\nand Latent Primitive Diffusion. 3DTopia-XL learns to generate high-quality 3D\nassets from textual or visual inputs. We conduct extensive qualitative and\nquantitative experiments to demonstrate that 3DTopia-XL significantly\noutperforms existing methods in generating high-quality 3D assets with\nfine-grained textures and materials, efficiently bridging the quality gap\nbetween generative models and real-world applications.\n", "date": "2024-09-19", "categories": []}
{"id": "2502.02187", "title": "ShapeShifter: 3D Variations Using Multiscale and Sparse Point-Voxel\n  Diffusion", "abstract": "  This paper proposes ShapeShifter, a new 3D generative model that learns to\nsynthesize shape variations based on a single reference model. While generative\nmethods for 3D objects have recently attracted much attention, current\ntechniques often lack geometric details and/or require long training times and\nlarge resources. Our approach remedies these issues by combining sparse voxel\ngrids and point, normal, and color sampling within a multiscale neural\narchitecture that can be trained efficiently and in parallel. We show that our\nresulting variations better capture the fine details of their original input\nand can handle more general types of surfaces than previous SDF-based methods.\nMoreover, we offer interactive generation of 3D shape variants, allowing more\nhuman control in the design loop if needed.\n", "date": "2025-02-04", "categories": []}
{"id": "2503.00605", "title": "GenVDM: Generating Vector Displacement Maps From a Single Image", "abstract": "  We introduce the first method for generating Vector Displacement Maps (VDMs):\nparameterized, detailed geometric stamps commonly used in 3D modeling. Given a\nsingle input image, our method first generates multi-view normal maps and then\nreconstructs a VDM from the normals via a novel reconstruction pipeline. We\nalso propose an efficient algorithm for extracting VDMs from 3D objects, and\npresent the first academic VDM dataset. Compared to existing 3D generative\nmodels focusing on complete shapes, we focus on generating parts that can be\nseamlessly attached to shape surfaces. The method gives artists rich control\nover adding geometric details to a 3D shape. Experiments demonstrate that our\napproach outperforms existing baselines. Generating VDMs offers additional\nbenefits, such as using 2D image editing to customize and refine 3D details.\n", "date": "2025-03-01", "categories": []}
{"id": "2503.12383", "title": "VRsketch2Gaussian: 3D VR Sketch Guided 3D Object Generation with\n  Gaussian Splatting", "abstract": "  We propose VRSketch2Gaussian, a first VR sketch-guided, multi-modal, native\n3D object generation framework that incorporates a 3D Gaussian Splatting\nrepresentation. As part of our work, we introduce VRSS, the first large-scale\npaired dataset containing VR sketches, text, images, and 3DGS, bridging the gap\nin multi-modal VR sketch-based generation. Our approach features the following\nkey innovations: 1) Sketch-CLIP feature alignment. We propose a two-stage\nalignment strategy that bridges the domain gap between sparse VR sketch\nembeddings and rich CLIP embeddings, facilitating both VR sketch-based\nretrieval and generation tasks. 2) Fine-Grained multi-modal conditioning. We\ndisentangle the 3D generation process by using explicit VR sketches for\ngeometric conditioning and text descriptions for appearance control. To\nfacilitate this, we propose a generalizable VR sketch encoder that effectively\naligns different modalities. 3) Efficient and high-fidelity 3D native\ngeneration. Our method leverages a 3D-native generation approach that enables\nfast and texture-rich 3D object synthesis. Experiments conducted on our VRSS\ndataset demonstrate that our method achieves high-quality, multi-modal VR\nsketch-based 3D generation. We believe our VRSS dataset and VRsketch2Gaussian\nmethod will be beneficial for the 3D generation community.\n", "date": "2025-03-16", "categories": []}
{"id": "2503.12834", "title": "PASTA: Part-Aware Sketch-to-3D Shape Generation with Text-Aligned Prior", "abstract": "  A fundamental challenge in conditional 3D shape generation is to minimize the\ninformation loss and maximize the intention of user input. Existing approaches\nhave predominantly focused on two types of isolated conditional signals, i.e.,\nuser sketches and text descriptions, each of which does not offer flexible\ncontrol of the generated shape. In this paper, we introduce PASTA, the flexible\napproach that seamlessly integrates a user sketch and a text description for 3D\nshape generation. The key idea is to use text embeddings from a vision-language\nmodel to enrich the semantic representation of sketches. Specifically, these\ntext-derived priors specify the part components of the object, compensating for\nmissing visual cues from ambiguous sketches. In addition, we introduce ISG-Net\nwhich employs two types of graph convolutional networks: IndivGCN, which\nprocesses fine-grained details, and PartGCN, which aggregates these details\ninto parts and refines the structure of objects. Extensive experiments\ndemonstrate that PASTA outperforms existing methods in part-level editing and\nachieves state-of-the-art results in sketch-to-3D shape generation.\n", "date": "2025-03-17", "categories": []}
{"id": "2503.13004", "title": "TFDM: Time-Variant Frequency-Based Point Cloud Diffusion with Mamba", "abstract": "  Diffusion models currently demonstrate impressive performance over various\ngenerative tasks. Recent work on image diffusion highlights the strong\ncapabilities of Mamba (state space models) due to its efficient handling of\nlong-range dependencies and sequential data modeling. Unfortunately, joint\nconsideration of state space models with 3D point cloud generation remains\nlimited. To harness the powerful capabilities of the Mamba model for 3D point\ncloud generation, we propose a novel diffusion framework containing dual latent\nMamba block (DM-Block) and a time-variant frequency encoder (TF-Encoder). The\nDM-Block apply a space-filling curve to reorder points into sequences suitable\nfor Mamba state-space modeling, while operating in a latent space to mitigate\nthe computational overhead that arises from direct 3D data processing.\nMeanwhile, the TF-Encoder takes advantage of the ability of the diffusion model\nto refine fine details in later recovery stages by prioritizing key points\nwithin the U-Net architecture. This frequency-based mechanism ensures enhanced\ndetail quality in the final stages of generation. Experimental results on the\nShapeNet-v2 dataset demonstrate that our method achieves state-of-the-art\nperformance (ShapeNet-v2: 0.14\\% on 1-NNA-Abs50 EMD and 57.90\\% on COV EMD) on\ncertain metrics for specific categories while reducing computational parameters\nand inference time by up to 10$\\times$ and 9$\\times$, respectively. Source code\nis available in Supplementary Materials and will be released upon accpetance.\n", "date": "2025-03-17", "categories": []}
{"id": "2503.13110", "title": "DTGBrepGen: A Novel B-rep Generative Model through Decoupling Topology\n  and Geometry", "abstract": "  Boundary representation (B-rep) of geometric models is a fundamental format\nin Computer-Aided Design (CAD). However, automatically generating valid and\nhigh-quality B-rep models remains challenging due to the complex\ninterdependence between the topology and geometry of the models. Existing\nmethods tend to prioritize geometric representation while giving insufficient\nattention to topological constraints, making it difficult to maintain\nstructural validity and geometric accuracy. In this paper, we propose\nDTGBrepGen, a novel topology-geometry decoupled framework for B-rep generation\nthat explicitly addresses both aspects. Our approach first generates valid\ntopological structures through a two-stage process that independently models\nedge-face and edge-vertex adjacency relationships. Subsequently, we employ\nTransformer-based diffusion models for sequential geometry generation,\nprogressively generating vertex coordinates, followed by edge geometries and\nface geometries which are represented as B-splines. Extensive experiments on\ndiverse CAD datasets show that DTGBrepGen significantly outperforms existing\nmethods in both topological validity and geometric accuracy, achieving higher\nvalidity rates and producing more diverse and realistic B-reps. Our code is\npublicly available at https://github.com/jinli99/DTGBrepGen.\n", "date": "2025-03-17", "categories": []}
{"id": "2503.11368", "title": "PBR3DGen: A VLM-guided Mesh Generation with High-quality PBR Texture", "abstract": "  Generating high-quality physically based rendering (PBR) materials is\nimportant to achieve realistic rendering in the downstream tasks, yet it\nremains challenging due to the intertwined effects of materials and lighting.\nWhile existing methods have made breakthroughs by incorporating material\ndecomposition in the 3D generation pipeline, they tend to bake highlights into\nalbedo and ignore spatially varying properties of metallicity and roughness. In\nthis work, we present PBR3DGen, a two-stage mesh generation method with\nhigh-quality PBR materials that integrates the novel multi-view PBR material\nestimation model and a 3D PBR mesh reconstruction model. Specifically, PBR3DGen\nleverages vision language models (VLM) to guide multi-view diffusion, precisely\ncapturing the spatial distribution and inherent attributes of\nreflective-metalness material. Additionally, we incorporate view-dependent\nillumination-aware conditions as pixel-aware priors to enhance spatially\nvarying material properties. Furthermore, our reconstruction model reconstructs\nhigh-quality mesh with PBR materials. Experimental results demonstrate that\nPBR3DGen significantly outperforms existing methods, achieving new\nstate-of-the-art results for PBR estimation and mesh generation. More results\nand visualization can be found on our project page:\nhttps://pbr3dgen1218.github.io/.\n", "date": "2025-03-14", "categories": []}
{"id": "2503.11629", "title": "TreeMeshGPT: Artistic Mesh Generation with Autoregressive Tree\n  Sequencing", "abstract": "  We introduce TreeMeshGPT, an autoregressive Transformer designed to generate\nhigh-quality artistic meshes aligned with input point clouds. Instead of the\nconventional next-token prediction in autoregressive Transformer, we propose a\nnovel Autoregressive Tree Sequencing where the next input token is retrieved\nfrom a dynamically growing tree structure that is built upon the triangle\nadjacency of faces within the mesh. Our sequencing enables the mesh to extend\nlocally from the last generated triangular face at each step, and therefore\nreduces training difficulty and improves mesh quality. Our approach represents\neach triangular face with two tokens, achieving a compression rate of\napproximately 22% compared to the naive face tokenization. This efficient\ntokenization enables our model to generate highly detailed artistic meshes with\nstrong point cloud conditioning, surpassing previous methods in both capacity\nand fidelity. Furthermore, our method generates mesh with strong normal\norientation constraints, minimizing flipped normals commonly encountered in\nprevious methods. Our experiments show that TreeMeshGPT enhances the mesh\ngeneration quality with refined details and normal orientation consistency.\n", "date": "2025-03-14", "categories": []}
{"id": "2503.10403", "title": "Hyper3D: Efficient 3D Representation via Hybrid Triplane and Octree\n  Feature for Enhanced 3D Shape Variational Auto-Encoders", "abstract": "  Recent 3D content generation pipelines often leverage Variational\nAutoencoders (VAEs) to encode shapes into compact latent representations,\nfacilitating diffusion-based generation. Efficiently compressing 3D shapes\nwhile preserving intricate geometric details remains a key challenge. Existing\n3D shape VAEs often employ uniform point sampling and 1D/2D latent\nrepresentations, such as vector sets or triplanes, leading to significant\ngeometric detail loss due to inadequate surface coverage and the absence of\nexplicit 3D representations in the latent space. Although recent work explores\n3D latent representations, their large scale hinders high-resolution encoding\nand efficient training. Given these challenges, we introduce Hyper3D, which\nenhances VAE reconstruction through efficient 3D representation that integrates\nhybrid triplane and octree features. First, we adopt an octree-based feature\nrepresentation to embed mesh information into the network, mitigating the\nlimitations of uniform point sampling in capturing geometric distributions\nalong the mesh surface. Furthermore, we propose a hybrid latent space\nrepresentation that integrates a high-resolution triplane with a low-resolution\n3D grid. This design not only compensates for the lack of explicit 3D\nrepresentations but also leverages a triplane to preserve high-resolution\ndetails. Experimental results demonstrate that Hyper3D outperforms traditional\nrepresentations by reconstructing 3D shapes with higher fidelity and finer\ndetails, making it well-suited for 3D generation pipelines.\n", "date": "2025-03-13", "categories": []}
{"id": "2412.05929", "title": "Text-to-3D Generation by 2D Editing", "abstract": "  Distilling 3D representations from pretrained 2D diffusion models is\nessential for 3D creative applications across gaming, film, and interior\ndesign. Current SDS-based methods are hindered by inefficient information\ndistillation from diffusion models, which prevents the creation of\nphotorealistic 3D contents. In this paper, we first reevaluate the SDS approach\nby analyzing its fundamental nature as a basic image editing process that\ncommonly results in over-saturation, over-smoothing, lack of rich content and\ndiversity due to the poor-quality single-step denoising. In light of this, we\nthen propose a novel method called 3D Generation by Editing (GE3D). Each\niteration of GE3D utilizes a 2D editing framework that combines a noising\ntrajectory to preserve the information of the input image, alongside a\ntext-guided denoising trajectory. We optimize the process by aligning the\nlatents across both trajectories. This approach fully exploits pretrained\ndiffusion models to distill multi-granularity information through multiple\ndenoising steps, resulting in photorealistic 3D outputs. Both theoretical and\nexperimental results confirm the effectiveness of our approach, which not only\nadvances 3D generation technology but also establishes a novel connection\nbetween 3D generation and 2D editing. This could potentially inspire further\nresearch in the field. Code and demos are released at\nhttps://jahnsonblack.github.io/GE3D/.\n", "date": "2024-12-08", "categories": []}
{"id": "2503.06485", "title": "A Mesh Is Worth 512 Numbers: Spectral-domain Diffusion Modeling for\n  High-dimension Shape Generation", "abstract": "  Recent advancements in learning latent codes derived from high-dimensional\nshapes have demonstrated impressive outcomes in 3D generative modeling.\nTraditionally, these approaches employ a trained autoencoder to acquire a\ncontinuous implicit representation of source shapes, which can be\ncomputationally expensive. This paper introduces a novel framework,\nspectral-domain diffusion for high-quality shape generation SpoDify, that\nutilizes singular value decomposition (SVD) for shape encoding. The resulting\neigenvectors can be stored for subsequent decoding, while generative modeling\nis performed on the eigenfeatures. This approach efficiently encodes complex\nmeshes into continuous implicit representations, such as encoding a 15k-vertex\nmesh to a 512-dimensional latent code without learning. Our method exhibits\nsignificant advantages in scenarios with limited samples or GPU resources. In\nmesh generation tasks, our approach produces high-quality shapes that are\ncomparable to state-of-the-art methods.\n", "date": "2025-03-09", "categories": []}
{"id": "2503.06900", "title": "DirectTriGS: Triplane-based Gaussian Splatting Field Representation for\n  3D Generation", "abstract": "  We present DirectTriGS, a novel framework designed for 3D object generation\nwith Gaussian Splatting (GS). GS-based rendering for 3D content has gained\nconsiderable attention recently. However, there has been limited exploration in\ndirectly generating 3D Gaussians compared to traditional generative modeling\napproaches. The main challenge lies in the complex data structure of GS\nrepresented by discrete point clouds with multiple channels. To overcome this\nchallenge, we propose employing the triplane representation, which allows us to\nrepresent Gaussian Splatting as an image-like continuous field. This\nrepresentation effectively encodes both the geometry and texture information,\nenabling smooth transformation back to Gaussian point clouds and rendering into\nimages by a TriRenderer, with only 2D supervisions. The proposed TriRenderer is\nfully differentiable, so that the rendering loss can supervise both texture and\ngeometry encoding. Furthermore, the triplane representation can be compressed\nusing a Variational Autoencoder (VAE), which can subsequently be utilized in\nlatent diffusion to generate 3D objects. The experiments demonstrate that the\nproposed generation framework can produce high-quality 3D object geometry and\nrendering results in the text-to-3D task.\n", "date": "2025-03-10", "categories": []}
{"id": "2411.05738", "title": "StdGEN: Semantic-Decomposed 3D Character Generation from Single Images", "abstract": "  We present StdGEN, an innovative pipeline for generating semantically\ndecomposed high-quality 3D characters from single images, enabling broad\napplications in virtual reality, gaming, and filmmaking, etc. Unlike previous\nmethods which struggle with limited decomposability, unsatisfactory quality,\nand long optimization times, StdGEN features decomposability, effectiveness and\nefficiency; i.e., it generates intricately detailed 3D characters with\nseparated semantic components such as the body, clothes, and hair, in three\nminutes. At the core of StdGEN is our proposed Semantic-aware Large\nReconstruction Model (S-LRM), a transformer-based generalizable model that\njointly reconstructs geometry, color and semantics from multi-view images in a\nfeed-forward manner. A differentiable multi-layer semantic surface extraction\nscheme is introduced to acquire meshes from hybrid implicit fields\nreconstructed by our S-LRM. Additionally, a specialized efficient multi-view\ndiffusion model and an iterative multi-layer surface refinement module are\nintegrated into the pipeline to facilitate high-quality, decomposable 3D\ncharacter generation. Extensive experiments demonstrate our state-of-the-art\nperformance in 3D anime character generation, surpassing existing baselines by\na significant margin in geometry, texture and decomposability. StdGEN offers\nready-to-use semantic-decomposed 3D characters and enables flexible\ncustomization for a wide range of applications. Project page:\nhttps://stdgen.github.io\n", "date": "2024-11-08", "categories": []}
{"id": "2502.09278", "title": "ConsistentDreamer: View-Consistent Meshes Through Balanced Multi-View\n  Gaussian Optimization", "abstract": "  Recent advances in diffusion models have significantly improved 3D\ngeneration, enabling the use of assets generated from an image for embodied AI\nsimulations. However, the one-to-many nature of the image-to-3D problem limits\ntheir use due to inconsistent content and quality across views. Previous models\noptimize a 3D model by sampling views from a view-conditioned diffusion prior,\nbut diffusion models cannot guarantee view consistency. Instead, we present\nConsistentDreamer, where we first generate a set of fixed multi-view prior\nimages and sample random views between them with another diffusion model\nthrough a score distillation sampling (SDS) loss. Thereby, we limit the\ndiscrepancies between the views guided by the SDS loss and ensure a consistent\nrough shape. In each iteration, we also use our generated multi-view prior\nimages for fine-detail reconstruction. To balance between the rough shape and\nthe fine-detail optimizations, we introduce dynamic task-dependent weights\nbased on homoscedastic uncertainty, updated automatically in each iteration.\nAdditionally, we employ opacity, depth distortion, and normal alignment losses\nto refine the surface for mesh extraction. Our method ensures better view\nconsistency and visual quality compared to the state-of-the-art.\n", "date": "2025-02-13", "categories": []}
{"id": "2502.14247", "title": "Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and\n  Texture Generation", "abstract": "  This report presents a comprehensive framework for generating high-quality 3D\nshapes and textures from diverse input prompts, including single images,\nmulti-view images, and text descriptions. The framework consists of 3D shape\ngeneration and texture generation. (1). The 3D shape generation pipeline\nemploys a Variational Autoencoder (VAE) to encode implicit 3D geometries into a\nlatent space and a diffusion network to generate latents conditioned on input\nprompts, with modifications to enhance model capacity. An alternative\nArtist-Created Mesh (AM) generation approach is also explored, yielding\npromising results for simpler geometries. (2). Texture generation involves a\nmulti-stage process starting with frontal images generation followed by\nmulti-view images generation, RGB-to-PBR texture conversion, and\nhigh-resolution multi-view texture refinement. A consistency scheduler is\nplugged into every stage, to enforce pixel-wise consistency among multi-view\ntextures during inference, ensuring seamless integration.\n  The pipeline demonstrates effective handling of diverse input formats,\nleveraging advanced neural architectures and novel methodologies to produce\nhigh-quality 3D content. This report details the system architecture,\nexperimental results, and potential future directions to improve and expand the\nframework. The source code and pretrained weights are released at:\nhttps://github.com/Tencent/Tencent-XR-3DGen.\n", "date": "2025-02-20", "categories": []}
{"id": "2502.16475", "title": "Dragen3D: Multiview Geometry Consistent 3D Gaussian Generation with\n  Drag-Based Control", "abstract": "  Single-image 3D generation has emerged as a prominent research topic, playing\na vital role in virtual reality, 3D modeling, and digital content creation.\nHowever, existing methods face challenges such as a lack of multi-view\ngeometric consistency and limited controllability during the generation\nprocess, which significantly restrict their usability. % To tackle these\nchallenges, we introduce Dragen3D, a novel approach that achieves geometrically\nconsistent and controllable 3D generation leveraging 3D Gaussian Splatting\n(3DGS). We introduce the Anchor-Gaussian Variational Autoencoder (Anchor-GS\nVAE), which encodes a point cloud and a single image into anchor latents and\ndecode these latents into 3DGS, enabling efficient latent-space generation. To\nenable multi-view geometry consistent and controllable generation, we propose a\nSeed-Point-Driven strategy: first generate sparse seed points as a coarse\ngeometry representation, then map them to anchor latents via the Seed-Anchor\nMapping Module. Geometric consistency is ensured by the easily learned sparse\nseed points, and users can intuitively drag the seed points to deform the final\n3DGS geometry, with changes propagated through the anchor latents. To the best\nof our knowledge, we are the first to achieve geometrically controllable 3D\nGaussian generation and editing without relying on 2D diffusion priors,\ndelivering comparable 3D generation quality to state-of-the-art methods.\n", "date": "2025-02-23", "categories": []}
{"id": "2410.18974", "title": "3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D\n  Generation", "abstract": "  Multi-view image diffusion models have significantly advanced open-domain 3D\nobject generation. However, most existing models rely on 2D network\narchitectures that lack inherent 3D biases, resulting in compromised geometric\nconsistency. To address this challenge, we introduce 3D-Adapter, a plug-in\nmodule designed to infuse 3D geometry awareness into pretrained image diffusion\nmodels. Central to our approach is the idea of 3D feedback augmentation: for\neach denoising step in the sampling loop, 3D-Adapter decodes intermediate\nmulti-view features into a coherent 3D representation, then re-encodes the\nrendered RGBD views to augment the pretrained base model through feature\naddition. We study two variants of 3D-Adapter: a fast feed-forward version\nbased on Gaussian splatting and a versatile training-free version utilizing\nneural fields and meshes. Our extensive experiments demonstrate that 3D-Adapter\nnot only greatly enhances the geometry quality of text-to-multi-view models\nsuch as Instant3D and Zero123++, but also enables high-quality 3D generation\nusing the plain text-to-image Stable Diffusion. Furthermore, we showcase the\nbroad application potential of 3D-Adapter by presenting high quality results in\ntext-to-3D, image-to-3D, text-to-texture, and text-to-avatar tasks.\n", "date": "2024-10-24", "categories": []}
{"id": "2502.14316", "title": "Textured 3D Regenerative Morphing with 3D Diffusion Prior", "abstract": "  Textured 3D morphing creates smooth and plausible interpolation sequences\nbetween two 3D objects, focusing on transitions in both shape and texture. This\nis important for creative applications like visual effects in filmmaking.\nPrevious methods rely on establishing point-to-point correspondences and\ndetermining smooth deformation trajectories, which inherently restrict them to\nshape-only morphing on untextured, topologically aligned datasets. This\nrestriction leads to labor-intensive preprocessing and poor generalization. To\novercome these challenges, we propose a method for 3D regenerative morphing\nusing a 3D diffusion prior. Unlike previous methods that depend on explicit\ncorrespondences and deformations, our method eliminates the additional need for\nobtaining correspondence and uses the 3D diffusion prior to generate morphing.\nSpecifically, we introduce a 3D diffusion model and interpolate the source and\ntarget information at three levels: initial noise, model parameters, and\ncondition features. We then explore an Attention Fusion strategy to generate\nmore smooth morphing sequences. To further improve the plausibility of semantic\ninterpolation and the generated 3D surfaces, we propose two strategies: (a)\nToken Reordering, where we match approximate tokens based on semantic analysis\nto guide implicit correspondences in the denoising process of the diffusion\nmodel, and (b) Low-Frequency Enhancement, where we enhance low-frequency\nsignals in the tokens to improve the quality of generated surfaces.\nExperimental results show that our method achieves superior smoothness and\nplausibility in 3D morphing across diverse cross-category object pairs,\noffering a novel regenerative method for 3D morphing with textured\nrepresentations.\n", "date": "2025-02-20", "categories": []}
{"id": "2312.11535", "title": "High-Quality 3D Creation from A Single Image Using Subject-Specific\n  Knowledge Prior", "abstract": "  In this paper, we address the critical bottleneck in robotics caused by the\nscarcity of diverse 3D data by presenting a novel two-stage approach for\ngenerating high-quality 3D models from a single image. This method is motivated\nby the need to efficiently expand 3D asset creation, particularly for robotics\ndatasets, where the variety of object types is currently limited compared to\ngeneral image datasets. Unlike previous methods that primarily rely on general\ndiffusion priors, which often struggle to align with the reference image, our\napproach leverages subject-specific prior knowledge. By incorporating\nsubject-specific priors in both geometry and texture, we ensure precise\nalignment between the generated 3D content and the reference object.\nSpecifically, we introduce a shading mode-aware prior into the NeRF\noptimization process, enhancing the geometry and refining texture in the coarse\noutputs to achieve superior quality. Extensive experiments demonstrate that our\nmethod significantly outperforms prior approaches.\n", "date": "2023-12-15", "categories": []}
{"id": "2502.11390", "title": "MARS: Mesh AutoRegressive Model for 3D Shape Detailization", "abstract": "  State-of-the-art methods for mesh detailization predominantly utilize\nGenerative Adversarial Networks (GANs) to generate detailed meshes from coarse\nones. These methods typically learn a specific style code for each category or\nsimilar categories without enforcing geometry supervision across different\nLevels of Detail (LODs). Consequently, such methods often fail to generalize\nacross a broader range of categories and cannot ensure shape consistency\nthroughout the detailization process. In this paper, we introduce MARS, a novel\napproach for 3D shape detailization. Our method capitalizes on a novel\nmulti-LOD, multi-category mesh representation to learn shape-consistent mesh\nrepresentations in latent space across different LODs. We further propose a\nmesh autoregressive model capable of generating such latent representations\nthrough next-LOD token prediction. This approach significantly enhances the\nrealism of the generated shapes. Extensive experiments conducted on the\nchallenging 3D Shape Detailization benchmark demonstrate that our proposed MARS\nmodel achieves state-of-the-art performance, surpassing existing methods in\nboth qualitative and quantitative assessments. Notably, the model's capability\nto generate fine-grained details while preserving the overall shape integrity\nis particularly commendable.\n", "date": "2025-02-17", "categories": []}
{"id": "2406.18581", "title": "Dream-in-Style: Text-to-3D Generation Using Stylized Score Distillation", "abstract": "  We present a method to generate 3D objects in styles. Our method takes a text\nprompt and a style reference image as input and reconstructs a neural radiance\nfield to synthesize a 3D model with the content aligning with the text prompt\nand the style following the reference image. To simultaneously generate the 3D\nobject and perform style transfer in one go, we propose a stylized score\ndistillation loss to guide a text-to-3D optimization process to output visually\nplausible geometry and appearance. Our stylized score distillation is based on\na combination of an original pretrained text-to-image model and its modified\nsibling with the key and value features of self-attention layers manipulated to\ninject styles from the reference image. Comparisons with state-of-the-art\nmethods demonstrated the strong visual performance of our method, further\nsupported by the quantitative results from our user study.\n", "date": "2024-06-05", "categories": []}
{"id": "2408.14899", "title": "MeshUp: Multi-Target Mesh Deformation via Blended Score Distillation", "abstract": "  We propose MeshUp, a technique that deforms a 3D mesh towards multiple target\nconcepts, and intuitively controls the region where each concept is expressed.\nConveniently, the concepts can be defined as either text queries, e.g., \"a dog\"\nand \"a turtle,\" or inspirational images, and the local regions can be selected\nas any number of vertices on the mesh. We can effectively control the influence\nof the concepts and mix them together using a novel score distillation\napproach, referred to as the Blended Score Distillation (BSD). BSD operates on\neach attention layer of the denoising U-Net of a diffusion model as it extracts\nand injects the per-objective activations into a unified denoising pipeline\nfrom which the deformation gradients are calculated. To localize the expression\nof these activations, we create a probabilistic Region of Interest (ROI) map on\nthe surface of the mesh, and turn it into 3D-consistent masks that we use to\ncontrol the expression of these activations. We demonstrate the effectiveness\nof BSD empirically and show that it can deform various meshes towards multiple\nobjectives. Our project page is at https://threedle.github.io/MeshUp.\n", "date": "2024-08-27", "categories": []}
{"id": "2409.20562", "title": "SpaceMesh: A Continuous Representation for Learning Manifold Surface\n  Meshes", "abstract": "  Meshes are ubiquitous in visual computing and simulation, yet most existing\nmachine learning techniques represent meshes only indirectly, e.g. as the level\nset of a scalar field or deformation of a template, or as a disordered triangle\nsoup lacking local structure. This work presents a scheme to directly generate\nmanifold, polygonal meshes of complex connectivity as the output of a neural\nnetwork. Our key innovation is to define a continuous latent connectivity space\nat each mesh vertex, which implies the discrete mesh. In particular, our vertex\nembeddings generate cyclic neighbor relationships in a halfedge mesh\nrepresentation, which gives a guarantee of edge-manifoldness and the ability to\nrepresent general polygonal meshes. This representation is well-suited to\nmachine learning and stochastic optimization, without restriction on\nconnectivity or topology. We first explore the basic properties of this\nrepresentation, then use it to fit distributions of meshes from large datasets.\nThe resulting models generate diverse meshes with tessellation structure\nlearned from the dataset population, with concise details and high-quality mesh\nelements. In applications, this approach not only yields high-quality outputs\nfrom generative models, but also enables directly learning challenging geometry\nprocessing tasks such as mesh repair.\n", "date": "2024-09-30", "categories": []}
{"id": "2403.09981", "title": "Controllable Text-to-3D Generation via Surface-Aligned Gaussian\n  Splatting", "abstract": "  While text-to-3D and image-to-3D generation tasks have received considerable\nattention, one important but under-explored field between them is controllable\ntext-to-3D generation, which we mainly focus on in this work. To address this\ntask, 1) we introduce Multi-view ControlNet (MVControl), a novel neural network\narchitecture designed to enhance existing pre-trained multi-view diffusion\nmodels by integrating additional input conditions, such as edge, depth, normal,\nand scribble maps. Our innovation lies in the introduction of a conditioning\nmodule that controls the base diffusion model using both local and global\nembeddings, which are computed from the input condition images and camera\nposes. Once trained, MVControl is able to offer 3D diffusion guidance for\noptimization-based 3D generation. And, 2) we propose an efficient multi-stage\n3D generation pipeline that leverages the benefits of recent large\nreconstruction models and score distillation algorithm. Building upon our\nMVControl architecture, we employ a unique hybrid diffusion guidance method to\ndirect the optimization process. In pursuit of efficiency, we adopt 3D\nGaussians as our representation instead of the commonly used implicit\nrepresentations. We also pioneer the use of SuGaR, a hybrid representation that\nbinds Gaussians to mesh triangle faces. This approach alleviates the issue of\npoor geometry in 3D Gaussians and enables the direct sculpting of fine-grained\ngeometry on the mesh. Extensive experiments demonstrate that our method\nachieves robust generalization and enables the controllable generation of\nhigh-quality 3D content. Project page: https://lizhiqi49.github.io/MVControl/.\n", "date": "2024-03-15", "categories": []}
{"id": "2502.04370", "title": "DreamDPO: Aligning Text-to-3D Generation with Human Preferences via\n  Direct Preference Optimization", "abstract": "  Text-to-3D generation automates 3D content creation from textual\ndescriptions, which offers transformative potential across various fields.\nHowever, existing methods often struggle to align generated content with human\npreferences, limiting their applicability and flexibility. To address these\nlimitations, in this paper, we propose DreamDPO, an optimization-based\nframework that integrates human preferences into the 3D generation process,\nthrough direct preference optimization. Practically, DreamDPO first constructs\npairwise examples, then compare their alignment with human preferences using\nreward or large multimodal models, and lastly optimizes the 3D representation\nwith a preference-driven loss function. By leveraging pairwise comparison to\nreflect preferences, DreamDPO reduces reliance on precise pointwise quality\nevaluations while enabling fine-grained controllability through\npreference-guided optimization. Experiments demonstrate that DreamDPO achieves\ncompetitive results, and provides higher-quality and more controllable 3D\ncontent compared to existing methods. The code and models will be open-sourced.\n", "date": "2025-02-05", "categories": []}
{"id": "2502.04762", "title": "Autoregressive Generation of Static and Growing Trees", "abstract": "  We propose a transformer architecture and training strategy for tree\ngeneration. The architecture processes data at multiple resolutions and has an\nhourglass shape, with middle layers processing fewer tokens than outer layers.\nSimilar to convolutional networks, we introduce longer range skip connections\nto completent this multi-resolution approach. The key advantage of this\narchitecture is the faster processing speed and lower memory consumption. We\nare therefore able to process more complex trees than would be possible with a\nvanilla transformer architecture. Furthermore, we extend this approach to\nperform image-to-tree and point-cloud-to-tree conditional generation and to\nsimulate the tree growth processes, generating 4D trees. Empirical results\nvalidate our approach in terms of speed, memory consumption, and generation\nquality.\n", "date": "2025-02-07", "categories": []}
{"id": "2309.16110", "title": "Learning Effective NeRFs and SDFs Representations with 3D Generative\n  Adversarial Networks for 3D Object Generation", "abstract": "  We present a solution for 3D object generation of ICCV 2023 OmniObject3D\nChallenge. In recent years, 3D object generation has made great process and\nachieved promising results, but it remains a challenging task due to the\ndifficulty of generating complex, textured, and high-fidelity results. To\nresolve this problem, we study learning effective NeRFs and SDFs\nrepresentations with 3D Generative Adversarial Networks (GANs) for 3D object\ngeneration. Specifically, inspired by recent works, we use the efficient\ngeometry-aware 3D GANs as the backbone incorporating with label embedding and\ncolor mapping, which enables to train the model on different taxonomies\nsimultaneously. Then, through a decoder, we aggregate the resulting features to\ngenerate Neural Radiance Fields (NeRFs) based representations for rendering\nhigh-fidelity synthetic images. Meanwhile, we optimize Signed Distance\nFunctions (SDFs) to effectively represent objects with 3D meshes. Besides, we\nobserve that this model can be effectively trained with only a few images of\neach object from a variety of classes, instead of using a great number of\nimages per object or training one model per class. With this pipeline, we can\noptimize an effective model for 3D object generation. This solution is among\nthe top 3 in the ICCV 2023 OmniObject3D Challenge.\n", "date": "2023-09-28", "categories": []}
{"id": "2408.13724", "title": "PhysPart: Physically Plausible Part Completion for Interactable Objects", "abstract": "  Interactable objects are ubiquitous in our daily lives. Recent advances in 3D\ngenerative models make it possible to automate the modeling of these objects,\nbenefiting a range of applications from 3D printing to the creation of robot\nsimulation environments. However, while significant progress has been made in\nmodeling 3D shapes and appearances, modeling object physics, particularly for\ninteractable objects, remains challenging due to the physical constraints\nimposed by inter-part motions. In this paper, we tackle the problem of\nphysically plausible part completion for interactable objects, aiming to\ngenerate 3D parts that not only fit precisely into the object but also allow\nsmooth part motions. To this end, we propose a diffusion-based part generation\nmodel that utilizes geometric conditioning through classifier-free guidance and\nformulates physical constraints as a set of stability and mobility losses to\nguide the sampling process. Additionally, we demonstrate the generation of\ndependent parts, paving the way toward sequential part generation for objects\nwith complex part-whole hierarchies. Experimentally, we introduce a new metric\nfor measuring physical plausibility based on motion success rates. Our model\noutperforms existing baselines over shape and physical metrics, especially\nthose that do not adequately model physical constraints. We also demonstrate\nour applications in 3D printing, robot manipulation, and sequential part\ngeneration, showing our strength in realistic tasks with the demand for high\nphysical plausibility.\n", "date": "2024-08-25", "categories": []}
{"id": "2501.17044", "title": "Synthesizing 3D Abstractions by Inverting Procedural Buildings with\n  Transformers", "abstract": "  We generate abstractions of buildings, reflecting the essential aspects of\ntheir geometry and structure, by learning to invert procedural models. We first\nbuild a dataset of abstract procedural building models paired with simulated\npoint clouds and then learn the inverse mapping through a transformer. Given a\npoint cloud, the trained transformer then infers the corresponding abstracted\nbuilding in terms of a programmatic language description. This approach\nleverages expressive procedural models developed for gaming and animation, and\nthereby retains desirable properties such as efficient rendering of the\ninferred abstractions and strong priors for regularity and symmetry. Our\napproach achieves good reconstruction accuracy in terms of geometry and\nstructure, as well as structurally consistent inpainting.\n", "date": "2025-01-28", "categories": []}
{"id": "2501.16764", "title": "DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian\n  Splat Generation", "abstract": "  Recent advancements in 3D content generation from text or a single image\nstruggle with limited high-quality 3D datasets and inconsistency from 2D\nmulti-view generation. We introduce DiffSplat, a novel 3D generative framework\nthat natively generates 3D Gaussian splats by taming large-scale text-to-image\ndiffusion models. It differs from previous 3D generative models by effectively\nutilizing web-scale 2D priors while maintaining 3D consistency in a unified\nmodel. To bootstrap the training, a lightweight reconstruction model is\nproposed to instantly produce multi-view Gaussian splat grids for scalable\ndataset curation. In conjunction with the regular diffusion loss on these\ngrids, a 3D rendering loss is introduced to facilitate 3D coherence across\narbitrary views. The compatibility with image diffusion models enables seamless\nadaptions of numerous techniques for image generation to the 3D realm.\nExtensive experiments reveal the superiority of DiffSplat in text- and\nimage-conditioned generation tasks and downstream applications. Thorough\nablation studies validate the efficacy of each critical design choice and\nprovide insights into the underlying mechanism.\n", "date": "2025-01-28", "categories": []}
{"id": "2501.16177", "title": "BAG: Body-Aligned 3D Wearable Asset Generation", "abstract": "  While recent advancements have shown remarkable progress in general 3D shape\ngeneration models, the challenge of leveraging these approaches to\nautomatically generate wearable 3D assets remains unexplored. To this end, we\npresent BAG, a Body-aligned Asset Generation method to output 3D wearable asset\nthat can be automatically dressed on given 3D human bodies. This is achived by\ncontrolling the 3D generation process using human body shape and pose\ninformation. Specifically, we first build a general single-image to consistent\nmultiview image diffusion model, and train it on the large Objaverse dataset to\nachieve diversity and generalizability. Then we train a Controlnet to guide the\nmultiview generator to produce body-aligned multiview images. The control\nsignal utilizes the multiview 2D projections of the target human body, where\npixel values represent the XYZ coordinates of the body surface in a canonical\nspace. The body-conditioned multiview diffusion generates body-aligned\nmultiview images, which are then fed into a native 3D diffusion model to\nproduce the 3D shape of the asset. Finally, by recovering the similarity\ntransformation using multiview silhouette supervision and addressing asset-body\npenetration with physics simulators, the 3D asset can be accurately fitted onto\nthe target human body. Experimental results demonstrate significant advantages\nover existing methods in terms of image prompt-following capability, shape\ndiversity, and shape quality. Our project page is available at\nhttps://bag-3d.github.io/.\n", "date": "2025-01-27", "categories": []}
{"id": "2411.02293", "title": "Hunyuan3D 1.0: A Unified Framework for Text-to-3D and Image-to-3D\n  Generation", "abstract": "  While 3D generative models have greatly improved artists' workflows, the\nexisting diffusion models for 3D generation suffer from slow generation and\npoor generalization. To address this issue, we propose a two-stage approach\nnamed Hunyuan3D 1.0 including a lite version and a standard version, that both\nsupport text- and image-conditioned generation. In the first stage, we employ a\nmulti-view diffusion model that efficiently generates multi-view RGB in\napproximately 4 seconds. These multi-view images capture rich details of the 3D\nasset from different viewpoints, relaxing the tasks from single-view to\nmulti-view reconstruction. In the second stage, we introduce a feed-forward\nreconstruction model that rapidly and faithfully reconstructs the 3D asset\ngiven the generated multi-view images in approximately 7 seconds. The\nreconstruction network learns to handle noises and in-consistency introduced by\nthe multi-view diffusion and leverages the available information from the\ncondition image to efficiently recover the 3D structure. Our framework involves\nthe text-to-image model, i.e., Hunyuan-DiT, making it a unified framework to\nsupport both text- and image-conditioned 3D generation. Our standard version\nhas 3x more parameters than our lite and other existing model. Our Hunyuan3D\n1.0 achieves an impressive balance between speed and quality, significantly\nreducing generation time while maintaining the quality and diversity of the\nproduced assets.\n", "date": "2024-11-04", "categories": []}
{"id": "2501.13449", "title": "MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware\n  Diffusion Guidance", "abstract": "  While single-concept customization has been studied in 3D, multi-concept\ncustomization remains largely unexplored. To address this, we propose\nMultiDreamer3D that can generate coherent multi-concept 3D content in a\ndivide-and-conquer manner. First, we generate 3D bounding boxes using an\nLLM-based layout controller. Next, a selective point cloud generator creates\ncoarse point clouds for each concept. These point clouds are placed in the 3D\nbounding boxes and initialized into 3D Gaussian Splatting with concept labels,\nenabling precise identification of concept attributions in 2D projections.\nFinally, we refine 3D Gaussians via concept-aware interval score matching,\nguided by concept-aware diffusion. Our experimental results show that\nMultiDreamer3D not only ensures object presence and preserves the distinct\nidentities of each concept but also successfully handles complex cases such as\nproperty change or interaction. To the best of our knowledge, we are the first\nto address the multi-concept customization in 3D.\n", "date": "2025-01-23", "categories": []}
{"id": "2408.05938", "title": "Deep Geometric Moments Promote Shape Consistency in Text-to-3D\n  Generation", "abstract": "  To address the data scarcity associated with 3D assets, 2D-lifting techniques\nsuch as Score Distillation Sampling (SDS) have become a widely adopted practice\nin text-to-3D generation pipelines. However, the diffusion models used in these\ntechniques are prone to viewpoint bias and thus lead to geometric\ninconsistencies such as the Janus problem. To counter this, we introduce MT3D,\na text-to-3D generative model that leverages a high-fidelity 3D object to\novercome viewpoint bias and explicitly infuse geometric understanding into the\ngeneration pipeline. Firstly, we employ depth maps derived from a high-quality\n3D model as control signals to guarantee that the generated 2D images preserve\nthe fundamental shape and structure, thereby reducing the inherent viewpoint\nbias. Next, we utilize deep geometric moments to ensure geometric consistency\nin the 3D representation explicitly. By incorporating geometric details from a\n3D asset, MT3D enables the creation of diverse and geometrically consistent\nobjects, thereby improving the quality and usability of our 3D representations.\nProject page and code: https://moment-3d.github.io/\n", "date": "2024-08-12", "categories": []}
{"id": "2501.09433", "title": "CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation", "abstract": "  The synthesis of high-quality 3D assets from textual or visual inputs has\nbecome a central objective in modern generative modeling. Despite the\nproliferation of 3D generation algorithms, they frequently grapple with\nchallenges such as multi-view inconsistency, slow generation times, low\nfidelity, and surface reconstruction problems. While some studies have\naddressed some of these issues, a comprehensive solution remains elusive. In\nthis paper, we introduce \\textbf{CaPa}, a carve-and-paint framework that\ngenerates high-fidelity 3D assets efficiently. CaPa employs a two-stage\nprocess, decoupling geometry generation from texture synthesis. Initially, a 3D\nlatent diffusion model generates geometry guided by multi-view inputs, ensuring\nstructural consistency across perspectives. Subsequently, leveraging a novel,\nmodel-agnostic Spatially Decoupled Attention, the framework synthesizes\nhigh-resolution textures (up to 4K) for a given geometry. Furthermore, we\npropose a 3D-aware occlusion inpainting algorithm that fills untextured\nregions, resulting in cohesive results across the entire model. This pipeline\ngenerates high-quality 3D assets in less than 30 seconds, providing\nready-to-use outputs for commercial applications. Experimental results\ndemonstrate that CaPa excels in both texture fidelity and geometric stability,\nestablishing a new standard for practical, scalable 3D asset generation.\n", "date": "2025-01-16", "categories": []}
{"id": "2501.07870", "title": "Make-A-Character 2: Animatable 3D Character Generation From a Single\n  Image", "abstract": "  This report introduces Make-A-Character 2, an advanced system for generating\nhigh-quality 3D characters from single portrait photographs, ideal for game\ndevelopment and digital human applications. Make-A-Character 2 builds upon its\npredecessor by incorporating several significant improvements for image-based\nhead generation. We utilize the IC-Light method to correct non-ideal\nillumination in input photos and apply neural network-based color correction to\nharmonize skin tones between the photos and game engine renders. We also employ\nthe Hierarchical Representation Network to capture high-frequency facial\nstructures and conduct adaptive skeleton calibration for accurate and\nexpressive facial animations. The entire image-to-3D-character generation\nprocess takes less than 2 minutes. Furthermore, we leverage transformer\narchitecture to generate co-speech facial and gesture actions, enabling\nreal-time conversation with the generated character. These technologies have\nbeen integrated into our conversational AI avatar products.\n", "date": "2025-01-14", "categories": []}
{"id": "2403.09236", "title": "Hyper-3DG: Text-to-3D Gaussian Generation via Hypergraph", "abstract": "  Text-to-3D generation represents an exciting field that has seen rapid\nadvancements, facilitating the transformation of textual descriptions into\ndetailed 3D models. However, current progress often neglects the intricate\nhigh-order correlation of geometry and texture within 3D objects, leading to\nchallenges such as over-smoothness, over-saturation and the Janus problem. In\nthis work, we propose a method named ``3D Gaussian Generation via Hypergraph\n(Hyper-3DG)'', designed to capture the sophisticated high-order correlations\npresent within 3D objects. Our framework is anchored by a well-established\nmainflow and an essential module, named ``Geometry and Texture Hypergraph\nRefiner (HGRefiner)''. This module not only refines the representation of 3D\nGaussians but also accelerates the update process of these 3D Gaussians by\nconducting the Patch-3DGS Hypergraph Learning on both explicit attributes and\nlatent visual features. Our framework allows for the production of finely\ngenerated 3D objects within a cohesive optimization, effectively circumventing\ndegradation. Extensive experimentation has shown that our proposed method\nsignificantly enhances the quality of 3D generation while incurring no\nadditional computational overhead for the underlying framework. (Project code:\nhttps://github.com/yjhboy/Hyper3DG)\n", "date": "2024-03-14", "categories": []}
{"id": "2404.06429", "title": "Magic-Boost: Boost 3D Generation with Multi-View Conditioned Diffusion", "abstract": "  Benefiting from the rapid development of 2D diffusion models, 3D content\ngeneration has witnessed significant progress. One promising solution is to\nfinetune the pre-trained 2D diffusion models to produce multi-view images and\nthen reconstruct them into 3D assets via feed-forward sparse-view\nreconstruction models. However, limited by the 3D inconsistency in the\ngenerated multi-view images and the low reconstruction resolution of the\nfeed-forward reconstruction models, the generated 3d assets are still limited\nto incorrect geometries and blurry textures. To address this problem, we\npresent a multi-view based refine method, named Magic-Boost, to further refine\nthe generation results. In detail, we first propose a novel multi-view\nconditioned diffusion model which extracts 3d prior from the synthesized\nmulti-view images to synthesize high-fidelity novel view images and then\nintroduce a novel iterative-update strategy to adopt it to provide precise\nguidance to refine the coarse generated results through a fast optimization\nprocess. Conditioned on the strong 3d priors extracted from the synthesized\nmulti-view images, Magic-Boost is capable of providing precise optimization\nguidance that well aligns with the coarse generated 3D assets, enriching the\nlocal detail in both geometry and texture within a short time ($\\sim15$min).\nExtensive experiments show Magic-Boost greatly enhances the coarse generated\ninputs, generates high-quality 3D assets with rich geometric and textural\ndetails. (Project Page: https://magic-research.github.io/magic-boost/)\n", "date": "2024-04-09", "categories": []}
{"id": "2501.04914", "title": "From Mesh Completion to AI Designed Crown", "abstract": "  Designing a dental crown is a time-consuming and labor intensive process. Our\ngoal is to simplify crown design and minimize the tediousness of making manual\nadjustments while still ensuring the highest level of accuracy and consistency.\nTo this end, we present a new end- to-end deep learning approach, coined Dental\nMesh Completion (DMC), to generate a crown mesh conditioned on a point cloud\ncontext. The dental context includes the tooth prepared to receive a crown and\nits surroundings, namely the two adjacent teeth and the three closest teeth in\nthe opposing jaw. We formulate crown generation in terms of completing this\npoint cloud context. A feature extractor first converts the input point cloud\ninto a set of feature vectors that represent local regions in the point cloud.\nThe set of feature vectors is then fed into a transformer to predict a new set\nof feature vectors for the missing region (crown). Subsequently, a point\nreconstruction head, followed by a multi-layer perceptron, is used to predict a\ndense set of points with normals. Finally, a differentiable point-to-mesh layer\nserves to reconstruct the crown surface mesh. We compare our DMC method to a\ngraph-based convolutional neural network which learns to deform a crown mesh\nfrom a generic crown shape to the target geometry. Extensive experiments on our\ndataset demonstrate the effectiveness of our method, which attains an average\nof 0.062 Chamfer Distance.The code is available\nat:https://github.com/Golriz-code/DMC.gi\n", "date": "2025-01-09", "categories": []}
{"id": "2501.05427", "title": "Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D\n  Generation", "abstract": "  Recent advances in 2D image generation have achieved remarkable\nquality,largely driven by the capacity of diffusion models and the availability\nof large-scale datasets. However, direct 3D generation is still constrained by\nthe scarcity and lower fidelity of 3D datasets. In this paper, we introduce\nZero-1-to-G, a novel approach that addresses this problem by enabling direct\nsingle-view generation on Gaussian splats using pretrained 2D diffusion models.\nOur key insight is that Gaussian splats, a 3D representation, can be decomposed\ninto multi-view images encoding different attributes. This reframes the\nchallenging task of direct 3D generation within a 2D diffusion framework,\nallowing us to leverage the rich priors of pretrained 2D diffusion models. To\nincorporate 3D awareness, we introduce cross-view and cross-attribute attention\nlayers, which capture complex correlations and enforce 3D consistency across\ngenerated splats. This makes Zero-1-to-G the first direct image-to-3D\ngenerative model to effectively utilize pretrained 2D diffusion priors,\nenabling efficient training and improved generalization to unseen objects.\nExtensive experiments on both synthetic and in-the-wild datasets demonstrate\nsuperior performance in 3D object generation, offering a new approach to\nhigh-quality 3D generation.\n", "date": "2025-01-09", "categories": []}
{"id": "2501.05445", "title": "Consistent Flow Distillation for Text-to-3D Generation", "abstract": "  Score Distillation Sampling (SDS) has made significant strides in distilling\nimage-generative models for 3D generation. However, its\nmaximum-likelihood-seeking behavior often leads to degraded visual quality and\ndiversity, limiting its effectiveness in 3D applications. In this work, we\npropose Consistent Flow Distillation (CFD), which addresses these limitations.\nWe begin by leveraging the gradient of the diffusion ODE or SDE sampling\nprocess to guide the 3D generation. From the gradient-based sampling\nperspective, we find that the consistency of 2D image flows across different\nviewpoints is important for high-quality 3D generation. To achieve this, we\nintroduce multi-view consistent Gaussian noise on the 3D object, which can be\nrendered from various viewpoints to compute the flow gradient. Our experiments\ndemonstrate that CFD, through consistent flows, significantly outperforms\nprevious methods in text-to-3D generation.\n", "date": "2025-01-09", "categories": []}
{"id": "2408.02993", "title": "DreamLCM: Towards High-Quality Text-to-3D Generation via Latent\n  Consistency Model", "abstract": "  Recently, the text-to-3D task has developed rapidly due to the appearance of\nthe SDS method. However, the SDS method always generates 3D objects with poor\nquality due to the over-smooth issue. This issue is attributed to two factors:\n1) the DDPM single-step inference produces poor guidance gradients; 2) the\nrandomness from the input noises and timesteps averages the details of the 3D\ncontents. In this paper, to address the issue, we propose DreamLCM which\nincorporates the Latent Consistency Model (LCM). DreamLCM leverages the\npowerful image generation capabilities inherent in LCM, enabling generating\nconsistent and high-quality guidance, i.e., predicted noises or images. Powered\nby the improved guidance, the proposed method can provide accurate and detailed\ngradients to optimize the target 3D models. In addition, we propose two\nstrategies to enhance the generation quality further. Firstly, we propose a\nguidance calibration strategy, utilizing Euler Solver to calibrate the guidance\ndistribution to accelerate 3D models to converge. Secondly, we propose a dual\ntimestep strategy, increasing the consistency of guidance and optimizing 3D\nmodels from geometry to appearance in DreamLCM. Experiments show that DreamLCM\nachieves state-of-the-art results in both generation quality and training\nefficiency. The code is available at https://github.com/1YimingZhong/DreamLCM.\n", "date": "2024-08-06", "categories": []}
{"id": "2501.00651", "title": "Taming Feed-forward Reconstruction Models as Latent Encoders for 3D\n  Generative Models", "abstract": "  Recent AI-based 3D content creation has largely evolved along two paths:\nfeed-forward image-to-3D reconstruction approaches and 3D generative models\ntrained with 2D or 3D supervision. In this work, we show that existing\nfeed-forward reconstruction methods can serve as effective latent encoders for\ntraining 3D generative models, thereby bridging these two paradigms. By reusing\npowerful pre-trained reconstruction models, we avoid computationally expensive\nencoder network training and obtain rich 3D latent features for generative\nmodeling for free. However, the latent spaces of reconstruction models are not\nwell-suited for generative modeling due to their unstructured nature. To enable\nflow-based model training on these latent features, we develop post-processing\npipelines, including protocols to standardize the features and spatial\nweighting to concentrate on important regions. We further incorporate a 2D\nimage space perceptual rendering loss to handle the high-dimensional latent\nspaces. Finally, we propose a multi-stream transformer-based rectified flow\narchitecture to achieve linear scaling and high-quality text-conditioned 3D\ngeneration. Our framework leverages the advancements of feed-forward\nreconstruction models to enhance the scalability of 3D generative modeling,\nachieving both high computational efficiency and state-of-the-art performance\nin text-to-3D generation.\n", "date": "2024-12-31", "categories": []}
{"id": "2501.02519", "title": "Layout2Scene: 3D Semantic Layout Guided Scene Generation via Geometry\n  and Appearance Diffusion Priors", "abstract": "  3D scene generation conditioned on text prompts has significantly progressed\ndue to the development of 2D diffusion generation models. However, the textual\ndescription of 3D scenes is inherently inaccurate and lacks fine-grained\ncontrol during training, leading to implausible scene generation. As an\nintuitive and feasible solution, the 3D layout allows for precise specification\nof object locations within the scene. To this end, we present a text-to-scene\ngeneration method (namely, Layout2Scene) using additional semantic layout as\nthe prompt to inject precise control of 3D object positions. Specifically, we\nfirst introduce a scene hybrid representation to decouple objects and\nbackgrounds, which is initialized via a pre-trained text-to-3D model. Then, we\npropose a two-stage scheme to optimize the geometry and appearance of the\ninitialized scene separately. To fully leverage 2D diffusion priors in geometry\nand appearance generation, we introduce a semantic-guided geometry diffusion\nmodel and a semantic-geometry guided diffusion model which are finetuned on a\nscene dataset. Extensive experiments demonstrate that our method can generate\nmore plausible and realistic scenes as compared to state-of-the-art approaches.\nFurthermore, the generated scene allows for flexible yet precise editing,\nthereby facilitating multiple downstream applications.\n", "date": "2025-01-05", "categories": []}
{"id": "2412.21117", "title": "Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D\n  Scene Generation", "abstract": "  In this work, we introduce Prometheus, a 3D-aware latent diffusion model for\ntext-to-3D generation at both object and scene levels in seconds. We formulate\n3D scene generation as multi-view, feed-forward, pixel-aligned 3D Gaussian\ngeneration within the latent diffusion paradigm. To ensure generalizability, we\nbuild our model upon pre-trained text-to-image generation model with only\nminimal adjustments, and further train it using a large number of images from\nboth single-view and multi-view datasets. Furthermore, we introduce an RGB-D\nlatent space into 3D Gaussian generation to disentangle appearance and geometry\ninformation, enabling efficient feed-forward generation of 3D Gaussians with\nbetter fidelity and geometry. Extensive experimental results demonstrate the\neffectiveness of our method in both feed-forward 3D Gaussian reconstruction and\ntext-to-3D generation. Project page:\nhttps://freemty.github.io/project-prometheus/\n", "date": "2024-12-30", "categories": []}
{"id": "2412.18608", "title": "PartGen: Part-level 3D Generation and Reconstruction with Multi-View\n  Diffusion Models", "abstract": "  Text- or image-to-3D generators and 3D scanners can now produce 3D assets\nwith high-quality shapes and textures. These assets typically consist of a\nsingle, fused representation, like an implicit neural field, a Gaussian\nmixture, or a mesh, without any useful structure. However, most applications\nand creative workflows require assets to be made of several meaningful parts\nthat can be manipulated independently. To address this gap, we introduce\nPartGen, a novel approach that generates 3D objects composed of meaningful\nparts starting from text, an image, or an unstructured 3D object. First, given\nmultiple views of a 3D object, generated or rendered, a multi-view diffusion\nmodel extracts a set of plausible and view-consistent part segmentations,\ndividing the object into parts. Then, a second multi-view diffusion model takes\neach part separately, fills in the occlusions, and uses those completed views\nfor 3D reconstruction by feeding them to a 3D reconstruction network. This\ncompletion process considers the context of the entire object to ensure that\nthe parts integrate cohesively. The generative completion model can make up for\nthe information missing due to occlusions; in extreme cases, it can hallucinate\nentirely invisible parts based on the input 3D asset. We evaluate our method on\ngenerated and real 3D assets and show that it outperforms segmentation and\npart-extraction baselines by a large margin. We also showcase downstream\napplications such as 3D part editing.\n", "date": "2024-12-24", "categories": []}
{"id": "2412.20470", "title": "JADE: Joint-aware Latent Diffusion for 3D Human Generative Modeling", "abstract": "  Generative modeling of 3D human bodies have been studied extensively in\ncomputer vision. The core is to design a compact latent representation that is\nboth expressive and semantically interpretable, yet existing approaches\nstruggle to achieve both requirements. In this work, we introduce JADE, a\ngenerative framework that learns the variations of human shapes with\nfined-grained control. Our key insight is a joint-aware latent representation\nthat decomposes human bodies into skeleton structures, modeled by joint\npositions, and local surface geometries, characterized by features attached to\neach joint. This disentangled latent space design enables geometric and\nsemantic interpretation, facilitating users with flexible controllability. To\ngenerate coherent and plausible human shapes under our proposed decomposition,\nwe also present a cascaded pipeline where two diffusions are employed to model\nthe distribution of skeleton structures and local surface geometries\nrespectively. Extensive experiments are conducted on public datasets, where we\ndemonstrate the effectiveness of JADE framework in multiple tasks in terms of\nautoencoding reconstruction accuracy, editing controllability and generation\nquality compared with existing methods.\n", "date": "2024-12-29", "categories": []}
{"id": "2406.16710", "title": "ID-Sculpt: ID-aware 3D Head Generation from Single In-the-wild Portrait\n  Image", "abstract": "  While recent works have achieved great success on image-to-3D object\ngeneration, high quality and fidelity 3D head generation from a single image\nremains a great challenge. Previous text-based methods for generating 3D heads\nwere limited by text descriptions and image-based methods struggled to produce\nhigh-quality head geometry. To handle this challenging problem, we propose a\nnovel framework, ID-Sculpt, to generate high-quality 3D heads while preserving\ntheir identities. Our work incorporates the identity information of the\nportrait image into three parts: 1) geometry initialization, 2) geometry\nsculpting, and 3) texture generation stages. Given a reference portrait image,\nwe first align the identity features with text features to realize ID-aware\nguidance enhancement, which contains the control signals representing the face\ninformation. We then use the canny map, ID features of the portrait image, and\na pre-trained text-to-normal/depth diffusion model to generate ID-aware\ngeometry supervision, and 3D-GAN inversion is employed to generate ID-aware\ngeometry initialization. Furthermore, with the ability to inject identity\ninformation into 3D head generation, we use ID-aware guidance to calculate\nID-aware Score Distillation (ISD) for geometry sculpting. For texture\ngeneration, we adopt the ID Consistent Texture Inpainting and Refinement which\nprogressively expands the view for texture inpainting to obtain an\ninitialization UV texture map. We then use the ID-aware guidance to provide\nimage-level supervision for noisy multi-view images to obtain a refined texture\nmap. Extensive experiments demonstrate that we can generate high-quality 3D\nheads with accurate geometry and texture from a single in-the-wild portrait\nimage.\n", "date": "2024-06-24", "categories": []}
{"id": "2412.16717", "title": "GANFusion: Feed-Forward Text-to-3D with Diffusion in GAN Space", "abstract": "  We train a feed-forward text-to-3D diffusion generator for human characters\nusing only single-view 2D data for supervision. Existing 3D generative models\ncannot yet match the fidelity of image or video generative models.\nState-of-the-art 3D generators are either trained with explicit 3D supervision\nand are thus limited by the volume and diversity of existing 3D data.\nMeanwhile, generators that can be trained with only 2D data as supervision\ntypically produce coarser results, cannot be text-conditioned, or must revert\nto test-time optimization. We observe that GAN- and diffusion-based generators\nhave complementary qualities: GANs can be trained efficiently with 2D\nsupervision to produce high-quality 3D objects but are hard to condition on\ntext. In contrast, denoising diffusion models can be conditioned efficiently\nbut tend to be hard to train with only 2D supervision. We introduce GANFusion,\nwhich starts by generating unconditional triplane features for 3D data using a\nGAN architecture trained with only single-view 2D data. We then generate random\nsamples from the GAN, caption them, and train a text-conditioned diffusion\nmodel that directly learns to sample from the space of good triplane features\nthat can be decoded into 3D objects.\n", "date": "2024-12-21", "categories": []}
{"id": "2412.14214", "title": "GraphicsDreamer: Image to 3D Generation with Physical Consistency", "abstract": "  Recently, the surge of efficient and automated 3D AI-generated content (AIGC)\nmethods has increasingly illuminated the path of transforming human imagination\ninto complex 3D structures. However, the automated generation of 3D content is\nstill significantly lags in industrial application. This gap exists because 3D\nmodeling demands high-quality assets with sharp geometry, exquisite topology,\nand physically based rendering (PBR), among other criteria. To narrow the\ndisparity between generated results and artists' expectations, we introduce\nGraphicsDreamer, a method for creating highly usable 3D meshes from single\nimages. To better capture the geometry and material details, we integrate the\nPBR lighting equation into our cross-domain diffusion model, concurrently\npredicting multi-view color, normal, depth images, and PBR materials. In the\ngeometry fusion stage, we continue to enforce the PBR constraints, ensuring\nthat the generated 3D objects possess reliable texture details, supporting\nrealistic relighting. Furthermore, our method incorporates topology\noptimization and fast UV unwrapping capabilities, allowing the 3D products to\nbe seamlessly imported into graphics engines. Extensive experiments demonstrate\nthat our model can produce high quality 3D assets in a reasonable time cost\ncompared to previous methods.\n", "date": "2024-12-18", "categories": []}
{"id": "2404.04363", "title": "Idea23D: Collaborative LMM Agents Enable 3D Model Generation from\n  Interleaved Multimodal Inputs", "abstract": "  With the success of 2D diffusion models, 2D AIGC content has already\ntransformed our lives. Recently, this success has been extended to 3D AIGC,\nwith state-of-the-art methods generating textured 3D models from single images\nor text. However, we argue that current 3D AIGC methods still do not fully\nunleash human creativity. We often imagine 3D content made from multimodal\ninputs, such as what it would look like if my pet bunny were eating a doughnut\non the table. In this paper, we explore a novel 3D AIGC approach: generating 3D\ncontent from IDEAs. An IDEA is a multimodal input composed of text, image, and\n3D models. To our knowledge, this challenging and exciting 3D AIGC setting has\nnot been studied before. We propose the new framework Idea23D, which combines\nthree agents based on large multimodal models (LMMs) and existing algorithmic\ntools. These three LMM-based agents are tasked with prompt generation, model\nselection, and feedback reflection. They collaborate and critique each other in\na fully automated loop, without human intervention. The framework then\ngenerates a text prompt to create 3D models that align closely with the input\nIDEAs. We demonstrate impressive 3D AIGC results that surpass previous methods.\nTo comprehensively assess the 3D AIGC capabilities of Idea23D, we introduce the\nEval3DAIGC-198 dataset, containing 198 multimodal inputs for 3D generation\ntasks. This dataset evaluates the alignment between generated 3D content and\ninput IDEAs. Our user study and quantitative results show that Idea23D\nsignificantly improves the success rate and accuracy of 3D generation, with\nexcellent compatibility across various LMM, Text-to-Image, and Image-to-3D\nmodels. Code and dataset are available at \\url{https://idea23d.github.io/}.\n", "date": "2024-04-05", "categories": []}
{"id": "2412.09648", "title": "DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion\n  Models", "abstract": "  Generating high-quality 3D content requires models capable of learning robust\ndistributions of complex scenes and the real-world objects within them. Recent\nGaussian-based 3D reconstruction techniques have achieved impressive results in\nrecovering high-fidelity 3D assets from sparse input images by predicting 3D\nGaussians in a feed-forward manner. However, these techniques often lack the\nextensive priors and expressiveness offered by Diffusion Models. On the other\nhand, 2D Diffusion Models, which have been successfully applied to denoise\nmultiview images, show potential for generating a wide range of photorealistic\n3D outputs but still fall short on explicit 3D priors and consistency. In this\nwork, we aim to bridge these two approaches by introducing DSplats, a novel\nmethod that directly denoises multiview images using Gaussian Splat-based\nReconstructors to produce a diverse array of realistic 3D assets. To harness\nthe extensive priors of 2D Diffusion Models, we incorporate a pretrained Latent\nDiffusion Model into the reconstructor backbone to predict a set of 3D\nGaussians. Additionally, the explicit 3D representation embedded in the\ndenoising network provides a strong inductive bias, ensuring geometrically\nconsistent novel view generation. Our qualitative and quantitative experiments\ndemonstrate that DSplats not only produces high-quality, spatially consistent\noutputs, but also sets a new standard in single-image to 3D reconstruction.\nWhen evaluated on the Google Scanned Objects dataset, DSplats achieves a PSNR\nof 20.38, an SSIM of 0.842, and an LPIPS of 0.109.\n", "date": "2024-12-11", "categories": []}
{"id": "2412.09008", "title": "MS2Mesh-XR: Multi-modal Sketch-to-Mesh Generation in XR Environments", "abstract": "  We present MS2Mesh-XR, a novel multi-modal sketch-to-mesh generation pipeline\nthat enables users to create realistic 3D objects in extended reality (XR)\nenvironments using hand-drawn sketches assisted by voice inputs. In specific,\nusers can intuitively sketch objects using natural hand movements in mid-air\nwithin a virtual environment. By integrating voice inputs, we devise ControlNet\nto infer realistic images based on the drawn sketches and interpreted text\nprompts. Users can then review and select their preferred image, which is\nsubsequently reconstructed into a detailed 3D mesh using the Convolutional\nReconstruction Model. In particular, our proposed pipeline can generate a\nhigh-quality 3D mesh in less than 20 seconds, allowing for immersive\nvisualization and manipulation in run-time XR scenes. We demonstrate the\npracticability of our pipeline through two use cases in XR settings. By\nleveraging natural user inputs and cutting-edge generative AI capabilities, our\napproach can significantly facilitate XR-based creative production and enhance\nuser experiences. Our code and demo will be available at:\nhttps://yueqiu0911.github.io/MS2Mesh-XR/\n", "date": "2024-12-12", "categories": []}
{"id": "2412.09548", "title": "Meshtron: High-Fidelity, Artist-Like 3D Mesh Generation at Scale", "abstract": "  Meshes are fundamental representations of 3D surfaces. However, creating\nhigh-quality meshes is a labor-intensive task that requires significant time\nand expertise in 3D modeling. While a delicate object often requires over\n$10^4$ faces to be accurately modeled, recent attempts at generating\nartist-like meshes are limited to $1.6$K faces and heavy discretization of\nvertex coordinates. Hence, scaling both the maximum face count and vertex\ncoordinate resolution is crucial to producing high-quality meshes of realistic,\ncomplex 3D objects. We present Meshtron, a novel autoregressive mesh generation\nmodel able to generate meshes with up to 64K faces at 1024-level coordinate\nresolution --over an order of magnitude higher face count and $8{\\times}$\nhigher coordinate resolution than current state-of-the-art methods. Meshtron's\nscalability is driven by four key components: (1) an hourglass neural\narchitecture, (2) truncated sequence training, (3) sliding window inference,\n(4) a robust sampling strategy that enforces the order of mesh sequences. This\nresults in over $50{\\%}$ less training memory, $2.5{\\times}$ faster throughput,\nand better consistency than existing works. Meshtron generates meshes of\ndetailed, complex 3D objects at unprecedented levels of resolution and\nfidelity, closely resembling those created by professional artists, and opening\nthe door to more realistic generation of detailed 3D assets for animation,\ngaming, and virtual environments.\n", "date": "2024-12-12", "categories": []}
{"id": "2412.05560", "title": "Text-to-3D Gaussian Splatting with Physics-Grounded Motion Generation", "abstract": "  Text-to-3D generation is a valuable technology in virtual reality and digital\ncontent creation. While recent works have pushed the boundaries of text-to-3D\ngeneration, producing high-fidelity 3D objects with inefficient prompts and\nsimulating their physics-grounded motion accurately still remain unsolved\nchallenges. To address these challenges, we present an innovative framework\nthat utilizes the Large Language Model (LLM)-refined prompts and diffusion\npriors-guided Gaussian Splatting (GS) for generating 3D models with accurate\nappearances and geometric structures. We also incorporate a continuum\nmechanics-based deformation map and color regularization to synthesize vivid\nphysics-grounded motion for the generated 3D Gaussians, adhering to the\nconservation of mass and momentum. By integrating text-to-3D generation with\nphysics-grounded motion synthesis, our framework renders photo-realistic 3D\nobjects that exhibit physics-aware motion, accurately reflecting the behaviors\nof the objects under various forces and constraints across different materials.\nExtensive experiments demonstrate that our approach achieves high-quality 3D\ngenerations with realistic physics-grounded motion.\n", "date": "2024-12-07", "categories": []}
{"id": "2412.06785", "title": "Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation", "abstract": "  3D generation methods have shown visually compelling results powered by\ndiffusion image priors. However, they often fail to produce realistic geometric\ndetails, resulting in overly smooth surfaces or geometric details inaccurately\nbaked in albedo maps. To address this, we introduce a new method that\nincorporates touch as an additional modality to improve the geometric details\nof generated 3D assets. We design a lightweight 3D texture field to synthesize\nvisual and tactile textures, guided by 2D diffusion model priors on both visual\nand tactile domains. We condition the visual texture generation on\nhigh-resolution tactile normals and guide the patch-based tactile texture\nrefinement with a customized TextureDreambooth. We further present a multi-part\ngeneration pipeline that enables us to synthesize different textures across\nvarious regions. To our knowledge, we are the first to leverage high-resolution\ntactile sensing to enhance geometric details for 3D generation tasks. We\nevaluate our method in both text-to-3D and image-to-3D settings. Our\nexperiments demonstrate that our method provides customized and realistic fine\ngeometric textures while maintaining accurate alignment between two modalities\nof vision and touch.\n", "date": "2024-12-09", "categories": []}
{"id": "2412.04470", "title": "Turbo3D: Ultra-fast Text-to-3D Generation", "abstract": "  We present Turbo3D, an ultra-fast text-to-3D system capable of generating\nhigh-quality Gaussian splatting assets in under one second. Turbo3D employs a\nrapid 4-step, 4-view diffusion generator and an efficient feed-forward Gaussian\nreconstructor, both operating in latent space. The 4-step, 4-view generator is\na student model distilled through a novel Dual-Teacher approach, which\nencourages the student to learn view consistency from a multi-view teacher and\nphoto-realism from a single-view teacher. By shifting the Gaussian\nreconstructor's inputs from pixel space to latent space, we eliminate the extra\nimage decoding time and halve the transformer sequence length for maximum\nefficiency. Our method demonstrates superior 3D generation results compared to\nprevious baselines, while operating in a fraction of their runtime.\n", "date": "2024-12-05", "categories": []}
{"id": "2412.02202", "title": "3D representation in 512-Byte:Variational tokenizer is the key for\n  autoregressive 3D generation", "abstract": "  Autoregressive transformers have revolutionized high-fidelity image\ngeneration. One crucial ingredient lies in the tokenizer, which compresses\nhigh-resolution image patches into manageable discrete tokens with a scanning\nor hierarchical order suitable for large language models. Extending these\ntokenizers to 3D generation, however, presents a significant challenge: unlike\nimage patches that naturally exhibit spatial sequence and multi-scale\nrelationships, 3D data lacks an inherent order, making it difficult to compress\ninto fewer tokens while preserving structural details. To address this, we\nintroduce the Variational Tokenizer (VAT), which transforms unordered 3D data\ninto compact latent tokens with an implicit hierarchy, suited for efficient and\nhigh-fidelity coarse-to-fine autoregressive modeling. VAT begins with an\nin-context transformer, which compress numerous unordered 3D features into a\nreduced token set with minimal information loss. This latent space is then\nmapped to a Gaussian distribution for residual quantization, with token counts\nprogressively increasing across scales. In this way, tokens at different scales\nnaturally establish the interconnections by allocating themselves into\ndifferent subspaces within the same Gaussian distribution, facilitating\ndiscrete modeling of token relationships across scales. During the decoding\nphase, a high-resolution triplane is utilized to convert these compact latent\ntokens into detailed 3D shapes. Extensive experiments demonstrate that VAT\nenables scalable and efficient 3D generation, outperforming existing methods in\nquality, efficiency, and generalization. Remarkably, VAT achieves up to a 250x\ncompression, reducing a 1MB mesh to just 3.9KB with a 96% F-score, and can\nfurther compress to 256 int8 tokens, achieving a 2000x reduction while\nmaintaining a 92% F-score.\n", "date": "2024-12-03", "categories": []}
{"id": "2412.02631", "title": "Sharp-It: A Multi-view to Multi-view Diffusion Model for 3D Synthesis\n  and Manipulation", "abstract": "  Advancements in text-to-image diffusion models have led to significant\nprogress in fast 3D content creation. One common approach is to generate a set\nof multi-view images of an object, and then reconstruct it into a 3D model.\nHowever, this approach bypasses the use of a native 3D representation of the\nobject and is hence prone to geometric artifacts and limited in controllability\nand manipulation capabilities. An alternative approach involves native 3D\ngenerative models that directly produce 3D representations. These models,\nhowever, are typically limited in their resolution, resulting in lower quality\n3D objects. In this work, we bridge the quality gap between methods that\ndirectly generate 3D representations and ones that reconstruct 3D objects from\nmulti-view images. We introduce a multi-view to multi-view diffusion model\ncalled Sharp-It, which takes a 3D consistent set of multi-view images rendered\nfrom a low-quality object and enriches its geometric details and texture. The\ndiffusion model operates on the multi-view set in parallel, in the sense that\nit shares features across the generated views. A high-quality 3D model can then\nbe reconstructed from the enriched multi-view set. By leveraging the advantages\nof both 2D and 3D approaches, our method offers an efficient and controllable\nmethod for high-quality 3D content creation. We demonstrate that Sharp-It\nenables various 3D applications, such as fast synthesis, editing, and\ncontrolled generation, while attaining high-quality assets.\n", "date": "2024-12-03", "categories": []}
{"id": "2408.02555", "title": "MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh\n  Tokenization", "abstract": "  Meshes are the de facto 3D representation in the industry but are\nlabor-intensive to produce. Recently, a line of research has focused on\nautoregressively generating meshes. This approach processes meshes into a\nsequence composed of vertices and then generates them vertex by vertex, similar\nto how a language model generates text. These methods have achieved some\nsuccess but still struggle to generate complex meshes. One primary reason for\nthis limitation is their inefficient tokenization methods. To address this\nissue, we introduce MeshAnything V2, an advanced mesh generation model designed\nto create Artist-Created Meshes that align precisely with specified shapes. A\nkey innovation behind MeshAnything V2 is our novel Adjacent Mesh Tokenization\n(AMT) method. Unlike traditional approaches that represent each face using\nthree vertices, AMT optimizes this by employing a single vertex wherever\nfeasible, effectively reducing the token sequence length by about half on\naverage. This not only streamlines the tokenization process but also results in\nmore compact and well-structured sequences, enhancing the efficiency of mesh\ngeneration. With these improvements, MeshAnything V2 effectively doubles the\nface limit compared to previous models, delivering superior performance without\nincreasing computational costs. We will make our code and models publicly\navailable. Project Page: https://buaacyw.github.io/meshanything-v2/\n", "date": "2024-08-05", "categories": []}
{"id": "2311.11221", "title": "GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion\n  Probabilistic Models with Structured Noise", "abstract": "  Text-to-3D, known for its efficient generation methods and expansive creative\npotential, has garnered significant attention in the AIGC domain. However, the\npixel-wise rendering of NeRF and its ray marching light sampling constrain the\nrendering speed, impacting its utility in downstream industrial applications.\nGaussian Splatting has recently shown a trend of replacing the traditional\npointwise sampling technique commonly used in NeRF-based methodologies, and it\nis changing various aspects of 3D reconstruction. This paper introduces a novel\ntext to 3D content generation framework, Gaussian Diffusion, based on Gaussian\nSplatting and produces more realistic renderings. The challenge of achieving\nmulti-view consistency in 3D generation significantly impedes modeling\ncomplexity and accuracy. Taking inspiration from SJC, we explore employing\nmulti-view noise distributions to perturb images generated by 3D Gaussian\nSplatting, aiming to rectify inconsistencies in multi-view geometry. We\ningeniously devise an efficient method to generate noise that produces Gaussian\nnoise from diverse viewpoints, all originating from a shared noise source.\nFurthermore, vanilla 3D Gaussian-based generation tends to trap models in local\nminima, causing artifacts like floaters, burrs, or proliferative elements. To\nmitigate these issues, we propose the variational Gaussian Splatting technique\nto enhance the quality and stability of 3D appearance. To our knowledge, our\napproach represents the first comprehensive utilization of Gaussian Diffusion\nacross the entire spectrum of 3D content generation processes.\n", "date": "2023-11-19", "categories": []}
{"id": "2411.16080", "title": "Boosting 3D Object Generation through PBR Materials", "abstract": "  Automatic 3D content creation has gained increasing attention recently, due\nto its potential in various applications such as video games, film industry,\nand AR/VR. Recent advancements in diffusion models and multimodal models have\nnotably improved the quality and efficiency of 3D object generation given a\nsingle RGB image. However, 3D objects generated even by state-of-the-art\nmethods are still unsatisfactory compared to human-created assets. Considering\nonly textures instead of materials makes these methods encounter challenges in\nphoto-realistic rendering, relighting, and flexible appearance editing. And\nthey also suffer from severe misalignment between geometry and high-frequency\ntexture details. In this work, we propose a novel approach to boost the quality\nof generated 3D objects from the perspective of Physics-Based Rendering (PBR)\nmaterials. By analyzing the components of PBR materials, we choose to consider\nalbedo, roughness, metalness, and bump maps. For albedo and bump maps, we\nleverage Stable Diffusion fine-tuned on synthetic data to extract these values,\nwith novel usages of these fine-tuned models to obtain 3D consistent albedo UV\nand bump UV for generated objects. In terms of roughness and metalness maps, we\nadopt a semi-automatic process to provide room for interactive adjustment,\nwhich we believe is more practical. Extensive experiments demonstrate that our\nmodel is generally beneficial for various state-of-the-art generation methods,\nsignificantly boosting the quality and realism of their generated 3D objects,\nwith natural relighting effects and substantially improved geometry.\n", "date": "2024-11-25", "categories": []}
{"id": "2411.14715", "title": "Any-to-3D Generation via Hybrid Diffusion Supervision", "abstract": "  Recent progress in 3D object generation has been fueled by the strong priors\noffered by diffusion models. However, existing models are tailored to specific\ntasks, accommodating only one modality at a time and necessitating retraining\nto change modalities. Given an image-to-3D model and a text prompt, a naive\napproach is to convert text prompts to images and then use the image-to-3D\nmodel for generation. This approach is both time-consuming and labor-intensive,\nresulting in unavoidable information loss during modality conversion. To\naddress this, we introduce XBind, a unified framework for any-to-3D generation\nusing cross-modal pre-alignment techniques. XBind integrates an\nmultimodal-aligned encoder with pre-trained diffusion models to generate 3D\nobjects from any modalities, including text, images, and audio. We subsequently\npresent a novel loss function, termed Modality Similarity (MS) Loss, which\naligns the embeddings of the modality prompts and the rendered images,\nfacilitating improved alignment of the 3D objects with multiple modalities.\nAdditionally, Hybrid Diffusion Supervision combined with a Three-Phase\nOptimization process improves the quality of the generated 3D objects.\nExtensive experiments showcase XBind's broad generation capabilities in\nany-to-3D scenarios. To our knowledge, this is the first method to generate 3D\nobjects from any modality prompts. Project page:\nhttps://zeroooooooow1440.github.io/.\n", "date": "2024-11-22", "categories": []}
{"id": "2410.09467", "title": "Enhancing Single Image to 3D Generation using Gaussian Splatting and\n  Hybrid Diffusion Priors", "abstract": "  3D object generation from a single image involves estimating the full 3D\ngeometry and texture of unseen views from an unposed RGB image captured in the\nwild. Accurately reconstructing an object's complete 3D structure and texture\nhas numerous applications in real-world scenarios, including robotic\nmanipulation, grasping, 3D scene understanding, and AR/VR. Recent advancements\nin 3D object generation have introduced techniques that reconstruct an object's\n3D shape and texture by optimizing the efficient representation of Gaussian\nSplatting, guided by pre-trained 2D or 3D diffusion models. However, a notable\ndisparity exists between the training datasets of these models, leading to\ndistinct differences in their outputs. While 2D models generate highly detailed\nvisuals, they lack cross-view consistency in geometry and texture. In contrast,\n3D models ensure consistency across different views but often result in overly\nsmooth textures. We propose bridging the gap between 2D and 3D diffusion models\nto address this limitation by integrating a two-stage frequency-based\ndistillation loss with Gaussian Splatting. Specifically, we leverage geometric\npriors in the low-frequency spectrum from a 3D diffusion model to maintain\nconsistent geometry and use a 2D diffusion model to refine the fidelity and\ntexture in the high-frequency spectrum of the generated 3D structure, resulting\nin more detailed and fine-grained outcomes. Our approach enhances geometric\nconsistency and visual quality, outperforming the current SOTA. Additionally,\nwe demonstrate the easy adaptability of our method for efficient object pose\nestimation and tracking.\n", "date": "2024-10-12", "categories": []}
{"id": "2403.11273", "title": "BrightDreamer: Generic 3D Gaussian Generative Framework for Fast\n  Text-to-3D Synthesis", "abstract": "  Text-to-3D synthesis has recently seen intriguing advances by combining the\ntext-to-image priors with 3D representation methods, e.g., 3D Gaussian\nSplatting (3D GS), via Score Distillation Sampling (SDS). However, a hurdle of\nexisting methods is the low efficiency, per-prompt optimization for a single 3D\nobject. Therefore, it is imperative for a paradigm shift from per-prompt\noptimization to feed-forward generation for any unseen text prompts, which yet\nremains challenging. An obstacle is how to directly generate a set of millions\nof 3D Gaussians to represent a 3D object. This paper presents BrightDreamer, an\nend-to-end feed-forward approach that can achieve generalizable and fast (77\nms) text-to-3D generation. Our key idea is to formulate the generation process\nas estimating the 3D deformation from an anchor shape with predefined\npositions. For this, we first propose a Text-guided Shape Deformation (TSD)\nnetwork to predict the deformed shape and its new positions, used as the\ncenters (one attribute) of 3D Gaussians. To estimate the other four attributes\n(i.e., scaling, rotation, opacity, and SH), we then design a novel Text-guided\nTriplane Generator (TTG) to generate a triplane representation for a 3D object.\nThe center of each Gaussian enables us to transform the spatial feature into\nthe four attributes. The generated 3D Gaussians can be finally rendered at 705\nframes per second. Extensive experiments demonstrate the superiority of our\nmethod over existing methods. Also, BrightDreamer possesses a strong semantic\nunderstanding capability even for complex text prompts. The code is available\nin the project page.\n", "date": "2024-03-17", "categories": []}
{"id": "2405.18515", "title": "Atlas3D: Physically Constrained Self-Supporting Text-to-3D for\n  Simulation and Fabrication", "abstract": "  Existing diffusion-based text-to-3D generation methods primarily focus on\nproducing visually realistic shapes and appearances, often neglecting the\nphysical constraints necessary for downstream tasks. Generated models\nfrequently fail to maintain balance when placed in physics-based simulations or\n3D printed. This balance is crucial for satisfying user design intentions in\ninteractive gaming, embodied AI, and robotics, where stable models are needed\nfor reliable interaction. Additionally, stable models ensure that 3D-printed\nobjects, such as figurines for home decoration, can stand on their own without\nrequiring additional supports. To fill this gap, we introduce Atlas3D, an\nautomatic and easy-to-implement method that enhances existing Score\nDistillation Sampling (SDS)-based text-to-3D tools. Atlas3D ensures the\ngeneration of self-supporting 3D models that adhere to physical laws of\nstability under gravity, contact, and friction. Our approach combines a novel\ndifferentiable simulation-based loss function with physically inspired\nregularization, serving as either a refinement or a post-processing module for\nexisting frameworks. We verify Atlas3D's efficacy through extensive generation\ntasks and validate the resulting 3D models in both simulated and real-world\nenvironments.\n", "date": "2024-05-28", "categories": []}
{"id": "2411.10947", "title": "Direct and Explicit 3D Generation from a Single Image", "abstract": "  Current image-to-3D approaches suffer from high computational costs and lack\nscalability for high-resolution outputs. In contrast, we introduce a novel\nframework to directly generate explicit surface geometry and texture using\nmulti-view 2D depth and RGB images along with 3D Gaussian features using a\nrepurposed Stable Diffusion model. We introduce a depth branch into U-Net for\nefficient and high quality multi-view, cross-domain generation and incorporate\nepipolar attention into the latent-to-pixel decoder for pixel-level multi-view\nconsistency. By back-projecting the generated depth pixels into 3D space, we\ncreate a structured 3D representation that can be either rendered via Gaussian\nsplatting or extracted to high-quality meshes, thereby leveraging additional\nnovel view synthesis loss to further improve our performance. Extensive\nexperiments demonstrate that our method surpasses existing baselines in\ngeometry and texture quality while achieving significantly faster generation\ntime.\n", "date": "2024-11-17", "categories": []}
{"id": "2411.10369", "title": "Towards High-Fidelity 3D Portrait Generation with Rich Details by\n  Cross-View Prior-Aware Diffusion", "abstract": "  Recent diffusion-based Single-image 3D portrait generation methods typically\nemploy 2D diffusion models to provide multi-view knowledge, which is then\ndistilled into 3D representations. However, these methods usually struggle to\nproduce high-fidelity 3D models, frequently yielding excessively blurred\ntextures. We attribute this issue to the insufficient consideration of\ncross-view consistency during the diffusion process, resulting in significant\ndisparities between different views and ultimately leading to blurred 3D\nrepresentations. In this paper, we address this issue by comprehensively\nexploiting multi-view priors in both the conditioning and diffusion procedures\nto produce consistent, detail-rich portraits. From the conditioning standpoint,\nwe propose a Hybrid Priors Diffsion model, which explicitly and implicitly\nincorporates multi-view priors as conditions to enhance the status consistency\nof the generated multi-view portraits. From the diffusion perspective,\nconsidering the significant impact of the diffusion noise distribution on\ndetailed texture generation, we propose a Multi-View Noise Resamplig Strategy\nintegrated within the optimization process leveraging cross-view priors to\nenhance representation consistency. Extensive experiments demonstrate that our\nmethod can produce 3D portraits with accurate geometry and rich details from a\nsingle image. The project page is at\n\\url{https://haoran-wei.github.io/Portrait-Diffusion}.\n", "date": "2024-11-15", "categories": []}
{"id": "2411.09595", "title": "LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models", "abstract": "  This work explores expanding the capabilities of large language models (LLMs)\npretrained on text to generate 3D meshes within a unified model. This offers\nkey advantages of (1) leveraging spatial knowledge already embedded in LLMs,\nderived from textual sources like 3D tutorials, and (2) enabling conversational\n3D generation and mesh understanding. A primary challenge is effectively\ntokenizing 3D mesh data into discrete tokens that LLMs can process seamlessly.\nTo address this, we introduce LLaMA-Mesh, a novel approach that represents the\nvertex coordinates and face definitions of 3D meshes as plain text, allowing\ndirect integration with LLMs without expanding the vocabulary. We construct a\nsupervised fine-tuning (SFT) dataset enabling pretrained LLMs to (1) generate\n3D meshes from text prompts, (2) produce interleaved text and 3D mesh outputs\nas required, and (3) understand and interpret 3D meshes. Our work is the first\nto demonstrate that LLMs can be fine-tuned to acquire complex spatial knowledge\nfor 3D mesh generation in a text-based format, effectively unifying the 3D and\ntext modalities. LLaMA-Mesh achieves mesh generation quality on par with models\ntrained from scratch while maintaining strong text generation performance.\n", "date": "2024-11-14", "categories": []}
{"id": "2411.08017", "title": "Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model\n  with Compact Wavelet Encodings", "abstract": "  Large-scale 3D generative models require substantial computational resources\nyet often fall short in capturing fine details and complex geometries at high\nresolutions. We attribute this limitation to the inefficiency of current\nrepresentations, which lack the compactness required to model the generative\nmodels effectively. To address this, we introduce a novel approach called\nWavelet Latent Diffusion, or WaLa, that encodes 3D shapes into wavelet-based,\ncompact latent encodings. Specifically, we compress a $256^3$ signed distance\nfield into a $12^3 \\times 4$ latent grid, achieving an impressive 2427x\ncompression ratio with minimal loss of detail. This high level of compression\nallows our method to efficiently train large-scale generative networks without\nincreasing the inference time. Our models, both conditional and unconditional,\ncontain approximately one billion parameters and successfully generate\nhigh-quality 3D shapes at $256^3$ resolution. Moreover, WaLa offers rapid\ninference, producing shapes within two to four seconds depending on the\ncondition, despite the model's scale. We demonstrate state-of-the-art\nperformance across multiple datasets, with significant improvements in\ngeneration quality, diversity, and computational efficiency. We open-source our\ncode and, to the best of our knowledge, release the largest pretrained 3D\ngenerative models across different modalities.\n", "date": "2024-11-12", "categories": []}
{"id": "2411.07025", "title": "Scaling Mesh Generation via Compressive Tokenization", "abstract": "  We propose a compressive yet effective mesh representation, Blocked and\nPatchified Tokenization (BPT), facilitating the generation of meshes exceeding\n8k faces. BPT compresses mesh sequences by employing block-wise indexing and\npatch aggregation, reducing their length by approximately 75\\% compared to the\noriginal sequences. This compression milestone unlocks the potential to utilize\nmesh data with significantly more faces, thereby enhancing detail richness and\nimproving generation robustness. Empowered with the BPT, we have built a\nfoundation mesh generative model training on scaled mesh data to support\nflexible control for point clouds and images. Our model demonstrates the\ncapability to generate meshes with intricate details and accurate topology,\nachieving SoTA performance on mesh generation and reaching the level for direct\nproduct usage.\n", "date": "2024-11-11", "categories": []}
{"id": "2411.07135", "title": "Edify 3D: Scalable High-Quality 3D Asset Generation", "abstract": "  We introduce Edify 3D, an advanced solution designed for high-quality 3D\nasset generation. Our method first synthesizes RGB and surface normal images of\nthe described object at multiple viewpoints using a diffusion model. The\nmulti-view observations are then used to reconstruct the shape, texture, and\nPBR materials of the object. Our method can generate high-quality 3D assets\nwith detailed geometry, clean shape topologies, high-resolution textures, and\nmaterials within 2 minutes of runtime.\n", "date": "2024-11-11", "categories": []}
{"id": "2401.15563", "title": "BrepGen: A B-rep Generative Diffusion Model with Structured Latent\n  Geometry", "abstract": "  This paper presents BrepGen, a diffusion-based generative approach that\ndirectly outputs a Boundary representation (B-rep) Computer-Aided Design (CAD)\nmodel. BrepGen represents a B-rep model as a novel structured latent geometry\nin a hierarchical tree. With the root node representing a whole CAD solid, each\nelement of a B-rep model (i.e., a face, an edge, or a vertex) progressively\nturns into a child-node from top to bottom. B-rep geometry information goes\ninto the nodes as the global bounding box of each primitive along with a latent\ncode describing the local geometric shape. The B-rep topology information is\nimplicitly represented by node duplication. When two faces share an edge, the\nedge curve will appear twice in the tree, and a T-junction vertex with three\nincident edges appears six times in the tree with identical node features.\nStarting from the root and progressing to the leaf, BrepGen employs\nTransformer-based diffusion models to sequentially denoise node features while\nduplicated nodes are detected and merged, recovering the B-Rep topology\ninformation. Extensive experiments show that BrepGen advances the task of CAD\nB-rep generation, surpassing existing methods on various benchmarks. Results on\nour newly collected furniture dataset further showcase its exceptional\ncapability in generating complicated geometry. While previous methods were\nlimited to generating simple prismatic shapes, BrepGen incorporates free-form\nand doubly-curved surfaces for the first time. Additional applications of\nBrepGen include CAD autocomplete and design interpolation. The code, pretrained\nmodels, and dataset are available at https://github.com/samxuxiang/BrepGen.\n", "date": "2024-01-28", "categories": []}
{"id": "2405.11914", "title": "PT43D: A Probabilistic Transformer for Generating 3D Shapes from Single\n  Highly-Ambiguous RGB Images", "abstract": "  Generating 3D shapes from single RGB images is essential in various\napplications such as robotics. Current approaches typically target images\ncontaining clear and complete visual descriptions of the object, without\nconsidering common realistic cases where observations of objects that are\nlargely occluded or truncated. We thus propose a transformer-based\nautoregressive model to generate the probabilistic distribution of 3D shapes\nconditioned on an RGB image containing potentially highly ambiguous\nobservations of the object. To handle realistic scenarios such as occlusion or\nfield-of-view truncation, we create simulated image-to-shape training pairs\nthat enable improved fine-tuning for real-world scenarios. We then adopt\ncross-attention to effectively identify the most relevant region of interest\nfrom the input image for shape generation. This enables inference of sampled\nshapes with reasonable diversity and strong alignment with the input image. We\ntrain and test our model on our synthetic data then fine-tune and test it on\nreal-world data. Experiments demonstrate that our model outperforms state of\nthe art in both scenarios.\n", "date": "2024-05-20", "categories": []}
{"id": "2406.09728", "title": "Neural Pose Representation Learning for Generating and Transferring\n  Non-Rigid Object Poses", "abstract": "  We propose a novel method for learning representations of poses for 3D\ndeformable objects, which specializes in 1) disentangling pose information from\nthe object's identity, 2) facilitating the learning of pose variations, and 3)\ntransferring pose information to other object identities. Based on these\nproperties, our method enables the generation of 3D deformable objects with\ndiversity in both identities and poses, using variations of a single object. It\ndoes not require explicit shape parameterization such as skeletons or joints,\npoint-level or shape-level correspondence supervision, or variations of the\ntarget object for pose transfer. To achieve pose disentanglement, compactness\nfor generative models, and transferability, we first design the pose extractor\nto represent the pose as a keypoint-based hybrid representation and the pose\napplier to learn an implicit deformation field. To better distill pose\ninformation from the object's geometry, we propose the implicit pose applier to\noutput an intrinsic mesh property, the face Jacobian. Once the extracted pose\ninformation is transferred to the target object, the pose applier is fine-tuned\nin a self-supervised manner to better describe the target object's shapes with\npose variations. The extracted poses are also used to train a cascaded\ndiffusion model to enable the generation of novel poses. Our experiments with\nthe DeformThings4D and Human datasets demonstrate state-of-the-art performance\nin pose transfer and the ability to generate diverse deformed shapes with\nvarious objects and poses.\n", "date": "2024-06-14", "categories": []}
{"id": "2411.01602", "title": "DreamPolish: Domain Score Distillation With Progressive Geometry\n  Generation", "abstract": "  We introduce DreamPolish, a text-to-3D generation model that excels in\nproducing refined geometry and high-quality textures. In the geometry\nconstruction phase, our approach leverages multiple neural representations to\nenhance the stability of the synthesis process. Instead of relying solely on a\nview-conditioned diffusion prior in the novel sampled views, which often leads\nto undesired artifacts in the geometric surface, we incorporate an additional\nnormal estimator to polish the geometry details, conditioned on viewpoints with\nvarying field-of-views. We propose to add a surface polishing stage with only a\nfew training steps, which can effectively refine the artifacts attributed to\nlimited guidance from previous stages and produce 3D objects with more\ndesirable geometry. The key topic of texture generation using pretrained\ntext-to-image models is to find a suitable domain in the vast latent\ndistribution of these models that contains photorealistic and consistent\nrenderings. In the texture generation phase, we introduce a novel score\ndistillation objective, namely domain score distillation (DSD), to guide neural\nrepresentations toward such a domain. We draw inspiration from the\nclassifier-free guidance (CFG) in textconditioned image generation tasks and\nshow that CFG and variational distribution guidance represent distinct aspects\nin gradient guidance and are both imperative domains for the enhancement of\ntexture quality. Extensive experiments show our proposed model can produce 3D\nassets with polished surfaces and photorealistic textures, outperforming\nexisting state-of-the-art methods.\n", "date": "2024-11-03", "categories": []}
{"id": "2403.19655", "title": "GaussianCube: A Structured and Explicit Radiance Representation for 3D\n  Generative Modeling", "abstract": "  We introduce a radiance representation that is both structured and fully\nexplicit and thus greatly facilitates 3D generative modeling. Existing radiance\nrepresentations either require an implicit feature decoder, which significantly\ndegrades the modeling power of the representation, or are spatially\nunstructured, making them difficult to integrate with mainstream 3D diffusion\nmethods. We derive GaussianCube by first using a novel\ndensification-constrained Gaussian fitting algorithm, which yields\nhigh-accuracy fitting using a fixed number of free Gaussians, and then\nrearranging these Gaussians into a predefined voxel grid via Optimal Transport.\nSince GaussianCube is a structured grid representation, it allows us to use\nstandard 3D U-Net as our backbone in diffusion modeling without elaborate\ndesigns. More importantly, the high-accuracy fitting of the Gaussians allows us\nto achieve a high-quality representation with orders of magnitude fewer\nparameters than previous structured representations for comparable quality,\nranging from one to two orders of magnitude. The compactness of GaussianCube\ngreatly eases the difficulty of 3D generative modeling. Extensive experiments\nconducted on unconditional and class-conditioned object generation, digital\navatar creation, and text-to-3D synthesis all show that our model achieves\nstate-of-the-art generation results both qualitatively and quantitatively,\nunderscoring the potential of GaussianCube as a highly accurate and versatile\nradiance representation for 3D generative modeling. Project page:\nhttps://gaussiancube.github.io/.\n", "date": "2024-03-28", "categories": []}
{"id": "2410.21299", "title": "TV-3DG: Mastering Text-to-3D Customized Generation with Visual Prompt", "abstract": "  In recent years, advancements in generative models have significantly\nexpanded the capabilities of text-to-3D generation. Many approaches rely on\nScore Distillation Sampling (SDS) technology. However, SDS struggles to\naccommodate multi-condition inputs, such as text and visual prompts, in\ncustomized generation tasks. To explore the core reasons, we decompose SDS into\na difference term and a classifier-free guidance term. Our analysis identifies\nthe core issue as arising from the difference term and the random noise\naddition during the optimization process, both contributing to deviations from\nthe target mode during distillation. To address this, we propose a novel\nalgorithm, Classifier Score Matching (CSM), which removes the difference term\nin SDS and uses a deterministic noise addition process to reduce noise during\noptimization, effectively overcoming the low-quality limitations of SDS in our\ncustomized generation framework. Based on CSM, we integrate visual prompt\ninformation with an attention fusion mechanism and sampling guidance\ntechniques, forming the Visual Prompt CSM (VPCSM) algorithm. Furthermore, we\nintroduce a Semantic-Geometry Calibration (SGC) module to enhance quality\nthrough improved textual information integration. We present our approach as\nTV-3DG, with extensive experiments demonstrating its capability to achieve\nstable, high-quality, customized 3D generation. Project page:\n\\url{https://yjhboy.github.io/TV-3DG}\n", "date": "2024-10-16", "categories": []}
{"id": "2410.19657", "title": "DiffGS: Functional Gaussian Splatting Diffusion", "abstract": "  3D Gaussian Splatting (3DGS) has shown convincing performance in rendering\nspeed and fidelity, yet the generation of Gaussian Splatting remains a\nchallenge due to its discreteness and unstructured nature. In this work, we\npropose DiffGS, a general Gaussian generator based on latent diffusion models.\nDiffGS is a powerful and efficient 3D generative model which is capable of\ngenerating Gaussian primitives at arbitrary numbers for high-fidelity rendering\nwith rasterization. The key insight is to represent Gaussian Splatting in a\ndisentangled manner via three novel functions to model Gaussian probabilities,\ncolors and transforms. Through the novel disentanglement of 3DGS, we represent\nthe discrete and unstructured 3DGS with continuous Gaussian Splatting\nfunctions, where we then train a latent diffusion model with the target of\ngenerating these Gaussian Splatting functions both unconditionally and\nconditionally. Meanwhile, we introduce a discretization algorithm to extract\nGaussians at arbitrary numbers from the generated functions via octree-guided\nsampling and optimization. We explore DiffGS for various tasks, including\nunconditional generation, conditional generation from text, image, and partial\n3DGS, as well as Point-to-Gaussian generation. We believe that DiffGS provides\na new direction for flexibly modeling and generating Gaussian Splatting.\n", "date": "2024-10-25", "categories": []}
{"id": "2405.20343", "title": "Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single\n  Image", "abstract": "  In this work, we introduce Unique3D, a novel image-to-3D framework for\nefficiently generating high-quality 3D meshes from single-view images,\nfeaturing state-of-the-art generation fidelity and strong generalizability.\nPrevious methods based on Score Distillation Sampling (SDS) can produce\ndiversified 3D results by distilling 3D knowledge from large 2D diffusion\nmodels, but they usually suffer from long per-case optimization time with\ninconsistent issues. Recent works address the problem and generate better 3D\nresults either by finetuning a multi-view diffusion model or training a fast\nfeed-forward model. However, they still lack intricate textures and complex\ngeometries due to inconsistency and limited generated resolution. To\nsimultaneously achieve high fidelity, consistency, and efficiency in single\nimage-to-3D, we propose a novel framework Unique3D that includes a multi-view\ndiffusion model with a corresponding normal diffusion model to generate\nmulti-view images with their normal maps, a multi-level upscale process to\nprogressively improve the resolution of generated orthographic multi-views, as\nwell as an instant and consistent mesh reconstruction algorithm called ISOMER,\nwhich fully integrates the color and geometric priors into mesh results.\nExtensive experiments demonstrate that our Unique3D significantly outperforms\nother image-to-3D baselines in terms of geometric and textural details.\n", "date": "2024-05-30", "categories": []}
{"id": "2410.20723", "title": "CompGS: Unleashing 2D Compositionality for Compositional Text-to-3D via\n  Dynamically Optimizing 3D Gaussians", "abstract": "  Recent breakthroughs in text-guided image generation have significantly\nadvanced the field of 3D generation. While generating a single high-quality 3D\nobject is now feasible, generating multiple objects with reasonable\ninteractions within a 3D space, a.k.a. compositional 3D generation, presents\nsubstantial challenges. This paper introduces CompGS, a novel generative\nframework that employs 3D Gaussian Splatting (GS) for efficient, compositional\ntext-to-3D content generation. To achieve this goal, two core designs are\nproposed: (1) 3D Gaussians Initialization with 2D compositionality: We transfer\nthe well-established 2D compositionality to initialize the Gaussian parameters\non an entity-by-entity basis, ensuring both consistent 3D priors for each\nentity and reasonable interactions among multiple entities; (2) Dynamic\nOptimization: We propose a dynamic strategy to optimize 3D Gaussians using\nScore Distillation Sampling (SDS) loss. CompGS first automatically decomposes\n3D Gaussians into distinct entity parts, enabling optimization at both the\nentity and composition levels. Additionally, CompGS optimizes across objects of\nvarying scales by dynamically adjusting the spatial parameters of each entity,\nenhancing the generation of fine-grained details, particularly in smaller\nentities. Qualitative comparisons and quantitative evaluations on T3Bench\ndemonstrate the effectiveness of CompGS in generating compositional 3D objects\nwith superior image quality and semantic alignment over existing methods.\nCompGS can also be easily extended to controllable 3D editing, facilitating\nscene generation. We hope CompGS will provide new insights to the compositional\n3D generation. Project page: https://chongjiange.github.io/compgs.html.\n", "date": "2024-10-28", "categories": []}
{"id": "2305.06131", "title": "Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era", "abstract": "  Generative AI has made significant progress in recent years, with text-guided\ncontent generation being the most practical as it facilitates interaction\nbetween human instructions and AI-generated content (AIGC). Thanks to\nadvancements in text-to-image and 3D modeling technologies, like neural\nradiance field (NeRF), text-to-3D has emerged as a nascent yet highly active\nresearch field. Our work conducts a comprehensive survey on this topic and\nfollows up on subsequent research progress in the overall field, aiming to help\nreaders interested in this direction quickly catch up with its rapid\ndevelopment. First, we introduce 3D data representations, including both\nStructured and non-Structured data. Building on this pre-requisite, we\nintroduce various core technologies to achieve satisfactory text-to-3D results.\nAdditionally, we present mainstream baselines and research directions in recent\ntext-to-3D technology, including fidelity, efficiency, consistency,\ncontrollability, diversity, and applicability. Furthermore, we summarize the\nusage of text-to-3D technology in various applications, including avatar\ngeneration, texture generation, scene generation and 3D editing. Finally, we\ndiscuss the agenda for the future development of text-to-3D.\n", "date": "2023-05-10", "categories": []}
{"id": "2410.18986", "title": "VehicleSDF: A 3D generative model for constrained engineering design via\n  surrogate modeling", "abstract": "  A main challenge in mechanical design is to efficiently explore the design\nspace while satisfying engineering constraints. This work explores the use of\n3D generative models to explore the design space in the context of vehicle\ndevelopment, while estimating and enforcing engineering constraints.\nSpecifically, we generate diverse 3D models of cars that meet a given set of\ngeometric specifications, while also obtaining quick estimates of performance\nparameters such as aerodynamic drag. For this, we employ a data-driven approach\n(using the ShapeNet dataset) to train VehicleSDF, a DeepSDF based model that\nrepresents potential designs in a latent space witch can be decoded into a 3D\nmodel. We then train surrogate models to estimate engineering parameters from\nthis latent space representation, enabling us to efficiently optimize latent\nvectors to match specifications. Our experiments show that we can generate\ndiverse 3D models while matching the specified geometric parameters. Finally,\nwe demonstrate that other performance parameters such as aerodynamic drag can\nbe estimated in a differentiable pipeline.\n", "date": "2024-10-09", "categories": []}
{"id": "2410.17802", "title": "GenUDC: High Quality 3D Mesh Generation with Unsigned Dual Contouring\n  Representation", "abstract": "  Generating high-quality meshes with complex structures and realistic surfaces\nis the primary goal of 3D generative models. Existing methods typically employ\nsequence data or deformable tetrahedral grids for mesh generation. However,\nsequence-based methods have difficulty producing complex structures with many\nfaces due to memory limits. The deformable tetrahedral grid-based method\nMeshDiffusion fails to recover realistic surfaces due to the inherent ambiguity\nin deformable grids. We propose the GenUDC framework to address these\nchallenges by leveraging the Unsigned Dual Contouring (UDC) as the mesh\nrepresentation. UDC discretizes a mesh in a regular grid and divides it into\nthe face and vertex parts, recovering both complex structures and fine details.\nAs a result, the one-to-one mapping between UDC and mesh resolves the ambiguity\nproblem. In addition, GenUDC adopts a two-stage, coarse-to-fine generative\nprocess for 3D mesh generation. It first generates the face part as a rough\nshape and then the vertex part to craft a detailed shape. Extensive evaluations\ndemonstrate the superiority of UDC as a mesh representation and the favorable\nperformance of GenUDC in mesh generation. The code and trained models are\navailable at https://github.com/TrepangCat/GenUDC.\n", "date": "2024-10-23", "categories": []}
{"id": "2310.05375", "title": "IPDreamer: Appearance-Controllable 3D Object Generation with Complex\n  Image Prompts", "abstract": "  Recent advances in 3D generation have been remarkable, with methods such as\nDreamFusion leveraging large-scale text-to-image diffusion-based models to\nguide 3D object generation. These methods enable the synthesis of detailed and\nphotorealistic textured objects. However, the appearance of 3D objects produced\nby such text-to-3D models is often unpredictable, and it is hard for\nsingle-image-to-3D methods to deal with images lacking a clear subject,\ncomplicating the generation of appearance-controllable 3D objects from complex\nimages. To address these challenges, we present IPDreamer, a novel method that\ncaptures intricate appearance features from complex $\\textbf{I}$mage\n$\\textbf{P}$rompts and aligns the synthesized 3D object with these extracted\nfeatures, enabling high-fidelity, appearance-controllable 3D object generation.\nOur experiments demonstrate that IPDreamer consistently generates high-quality\n3D objects that align with both the textual and complex image prompts,\nhighlighting its promising capability in appearance-controlled, complex 3D\nobject generation. Our code is available at\nhttps://github.com/zengbohan0217/IPDreamer.\n", "date": "2023-10-09", "categories": []}
{"id": "2404.19525", "title": "MicroDreamer: Efficient 3D Generation in $\\sim$20 Seconds by Score-based\n  Iterative Reconstruction", "abstract": "  Optimization-based approaches, such as score distillation sampling (SDS),\nshow promise in zero-shot 3D generation but suffer from low efficiency,\nprimarily due to the high number of function evaluations (NFEs) required for\neach sample and the limitation of optimization confined to latent space. This\npaper introduces score-based iterative reconstruction (SIR), an efficient and\ngeneral algorithm mimicking a differentiable 3D reconstruction process to\nreduce the NFEs and enable optimization in pixel space. Given a single set of\nimages sampled from a multi-view score-based diffusion model, SIR repeatedly\noptimizes 3D parameters, unlike the single-step optimization in SDS. With other\nimprovements in training, we present an efficient approach called MicroDreamer\nthat generally applies to various 3D representations and 3D generation tasks.\nIn particular, MicroDreamer is 5-20 times faster than SDS in generating neural\nradiance field while retaining a comparable performance and takes about 20\nseconds to create meshes from 3D Gaussian splatting on a single A100 GPU,\nhalving the time of the fastest optimization-based baseline DreamGaussian with\nsignificantly superior performance compared to the measurement standard\ndeviation. Our code is available at https://github.com/ML-GSAI/MicroDreamer.\n", "date": "2024-04-30", "categories": []}
{"id": "2410.12928", "title": "DreamCraft3D++: Efficient Hierarchical 3D Generation with Multi-Plane\n  Reconstruction Model", "abstract": "  We introduce DreamCraft3D++, an extension of DreamCraft3D that enables\nefficient high-quality generation of complex 3D assets. DreamCraft3D++ inherits\nthe multi-stage generation process of DreamCraft3D, but replaces the\ntime-consuming geometry sculpting optimization with a feed-forward multi-plane\nbased reconstruction model, speeding up the process by 1000x. For texture\nrefinement, we propose a training-free IP-Adapter module that is conditioned on\nthe enhanced multi-view images to enhance texture and geometry consistency,\nproviding a 4x faster alternative to DreamCraft3D's DreamBooth fine-tuning.\nExperiments on diverse datasets demonstrate DreamCraft3D++'s ability to\ngenerate creative 3D assets with intricate geometry and realistic 360{\\deg}\ntextures, outperforming state-of-the-art image-to-3D methods in quality and\nspeed. The full implementation will be open-sourced to enable new possibilities\nin 3D content creation.\n", "date": "2024-10-16", "categories": []}
{"id": "2410.13530", "title": "L3DG: Latent 3D Gaussian Diffusion", "abstract": "  We propose L3DG, the first approach for generative 3D modeling of 3D\nGaussians through a latent 3D Gaussian diffusion formulation. This enables\neffective generative 3D modeling, scaling to generation of entire room-scale\nscenes which can be very efficiently rendered. To enable effective synthesis of\n3D Gaussians, we propose a latent diffusion formulation, operating in a\ncompressed latent space of 3D Gaussians. This compressed latent space is\nlearned by a vector-quantized variational autoencoder (VQ-VAE), for which we\nemploy a sparse convolutional architecture to efficiently operate on room-scale\nscenes. This way, the complexity of the costly generation process via diffusion\nis substantially reduced, allowing higher detail on object-level generation, as\nwell as scalability to large scenes. By leveraging the 3D Gaussian\nrepresentation, the generated scenes can be rendered from arbitrary viewpoints\nin real-time. We demonstrate that our approach significantly improves visual\nquality over prior work on unconditional object-level radiance field synthesis\nand showcase its applicability to room-scale scene generation.\n", "date": "2024-10-17", "categories": []}
{"id": "2410.10399", "title": "Parameterize Structure with Differentiable Template for 3D Shape\n  Generation", "abstract": "  Structural representation is crucial for reconstructing and generating\neditable 3D shapes with part semantics. Recent 3D shape generation works employ\ncomplicated networks and structure definitions relying on hierarchical\nannotations and pay less attention to the details inside parts. In this paper,\nwe propose the method that parameterizes the shared structure in the same\ncategory using a differentiable template and corresponding fixed-length\nparameters. Specific parameters are fed into the template to calculate cuboids\nthat indicate a concrete shape. We utilize the boundaries of three-view\ndrawings of each cuboid to further describe the inside details. Shapes are\nrepresented with the parameters and three-view details inside cuboids, from\nwhich the SDF can be calculated to recover the object. Benefiting from our\nfixed-length parameters and three-view details, our networks for reconstruction\nand generation are simple and effective to learn the latent space. Our method\ncan reconstruct or generate diverse shapes with complicated details, and\ninterpolate them smoothly. Extensive evaluations demonstrate the superiority of\nour method on reconstruction from point cloud, generation, and interpolation.\n", "date": "2024-10-14", "categories": []}
{"id": "2407.12291", "title": "JointDreamer: Ensuring Geometry Consistency and Text Congruence in\n  Text-to-3D Generation via Joint Score Distillation", "abstract": "  Score Distillation Sampling (SDS) by well-trained 2D diffusion models has\nshown great promise in text-to-3D generation. However, this paradigm distills\nview-agnostic 2D image distributions into the rendering distribution of 3D\nrepresentation for each view independently, overlooking the coherence across\nviews and yielding 3D inconsistency in generations. In this work, we propose\n\\textbf{J}oint \\textbf{S}core \\textbf{D}istillation (JSD), a new paradigm that\nensures coherent 3D generations. Specifically, we model the joint image\ndistribution, which introduces an energy function to capture the coherence\namong denoised images from the diffusion model. We then derive the joint score\ndistillation on multiple rendered views of the 3D representation, as opposed to\na single view in SDS. In addition, we instantiate three universal view-aware\nmodels as energy functions, demonstrating compatibility with JSD. Empirically,\nJSD significantly mitigates the 3D inconsistency problem in SDS, while\nmaintaining text congruence. Moreover, we introduce the Geometry Fading scheme\nand Classifier-Free Guidance (CFG) Switching strategy to enhance generative\ndetails. Our framework, JointDreamer, establishes a new benchmark in text-to-3D\ngeneration, achieving outstanding results with an 88.5\\% CLIP R-Precision and\n27.7\\% CLIP Score. These metrics demonstrate exceptional text congruence, as\nwell as remarkable geometric consistency and texture fidelity.\n", "date": "2024-07-17", "categories": []}
{"id": "2410.09592", "title": "ControLRM: Fast and Controllable 3D Generation via Large Reconstruction\n  Model", "abstract": "  Despite recent advancements in 3D generation methods, achieving\ncontrollability still remains a challenging issue. Current approaches utilizing\nscore-distillation sampling are hindered by laborious procedures that consume a\nsignificant amount of time. Furthermore, the process of first generating 2D\nrepresentations and then mapping them to 3D lacks internal alignment between\nthe two forms of representation. To address these challenges, we introduce\nControLRM, an end-to-end feed-forward model designed for rapid and controllable\n3D generation using a large reconstruction model (LRM). ControLRM comprises a\n2D condition generator, a condition encoding transformer, and a triplane\ndecoder transformer. Instead of training our model from scratch, we advocate\nfor a joint training framework. In the condition training branch, we lock the\ntriplane decoder and reuses the deep and robust encoding layers pretrained with\nmillions of 3D data in LRM. In the image training branch, we unlock the\ntriplane decoder to establish an implicit alignment between the 2D and 3D\nrepresentations. To ensure unbiased evaluation, we curate evaluation samples\nfrom three distinct datasets (G-OBJ, GSO, ABO) rather than relying on\ncherry-picking manual generation. The comprehensive experiments conducted on\nquantitative and qualitative comparisons of 3D controllability and generation\nquality demonstrate the strong generalization capacity of our proposed\napproach.\n", "date": "2024-10-12", "categories": []}
{"id": "2410.10037", "title": "GALA: Geometry-Aware Local Adaptive Grids for Detailed 3D Generation", "abstract": "  We propose GALA, a novel representation of 3D shapes that (i) excels at\ncapturing and reproducing complex geometry and surface details, (ii) is\ncomputationally efficient, and (iii) lends itself to 3D generative modelling\nwith modern, diffusion-based schemes. The key idea of GALA is to exploit both\nthe global sparsity of surfaces within a 3D volume and their local surface\nproperties. Sparsity is promoted by covering only the 3D object boundaries, not\nempty space, with an ensemble of tree root voxels. Each voxel contains an\noctree to further limit storage and compute to regions that contain surfaces.\nAdaptivity is achieved by fitting one local and geometry-aware coordinate frame\nin each non-empty leaf node. Adjusting the orientation of the local grid, as\nwell as the anisotropic scales of its axes, to the local surface shape greatly\nincreases the amount of detail that can be stored in a given amount of memory,\nwhich in turn allows for quantization without loss of quality. With our\noptimized C++/CUDA implementation, GALA can be fitted to an object in less than\n10 seconds. Moreover, the representation can efficiently be flattened and\nmanipulated with transformer networks. We provide a cascaded generation\npipeline capable of generating 3D shapes with great geometric detail.\n", "date": "2024-10-13", "categories": []}
{"id": "2406.01579", "title": "Tetrahedron Splatting for 3D Generation", "abstract": "  3D representation is essential to the significant advance of 3D generation\nwith 2D diffusion priors. As a flexible representation, NeRF has been first\nadopted for 3D representation. With density-based volumetric rendering, it\nhowever suffers both intensive computational overhead and inaccurate mesh\nextraction. Using a signed distance field and Marching Tetrahedra, DMTet allows\nfor precise mesh extraction and real-time rendering but is limited in handling\nlarge topological changes in meshes, leading to optimization challenges.\nAlternatively, 3D Gaussian Splatting (3DGS) is favored in both training and\nrendering efficiency while falling short in mesh extraction. In this work, we\nintroduce a novel 3D representation, Tetrahedron Splatting (TeT-Splatting),\nthat supports easy convergence during optimization, precise mesh extraction,\nand real-time rendering simultaneously. This is achieved by integrating\nsurface-based volumetric rendering within a structured tetrahedral grid while\npreserving the desired ability of precise mesh extraction, and a tile-based\ndifferentiable tetrahedron rasterizer. Furthermore, we incorporate eikonal and\nnormal consistency regularization terms for the signed distance field to\nimprove generation quality and stability. Critically, our representation can be\ntrained without mesh extraction, making the optimization process easier to\nconverge. Our TeT-Splatting can be readily integrated in existing 3D generation\npipelines, along with polygonal mesh for texture optimization. Extensive\nexperiments show that our TeT-Splatting strikes a superior tradeoff among\nconvergence speed, render efficiency, and mesh quality as compared to previous\nalternatives under varying 3D generation settings.\n", "date": "2024-06-03", "categories": []}
{"id": "2410.09009", "title": "Semantic Score Distillation Sampling for Compositional Text-to-3D\n  Generation", "abstract": "  Generating high-quality 3D assets from textual descriptions remains a pivotal\nchallenge in computer graphics and vision research. Due to the scarcity of 3D\ndata, state-of-the-art approaches utilize pre-trained 2D diffusion priors,\noptimized through Score Distillation Sampling (SDS). Despite progress, crafting\ncomplex 3D scenes featuring multiple objects or intricate interactions is still\ndifficult. To tackle this, recent methods have incorporated box or layout\nguidance. However, these layout-guided compositional methods often struggle to\nprovide fine-grained control, as they are generally coarse and lack\nexpressiveness. To overcome these challenges, we introduce a novel SDS\napproach, Semantic Score Distillation Sampling (SemanticSDS), designed to\neffectively improve the expressiveness and accuracy of compositional text-to-3D\ngeneration. Our approach integrates new semantic embeddings that maintain\nconsistency across different rendering views and clearly differentiate between\nvarious objects and parts. These embeddings are transformed into a semantic\nmap, which directs a region-specific SDS process, enabling precise optimization\nand compositional generation. By leveraging explicit semantic guidance, our\nmethod unlocks the compositional capabilities of existing pre-trained diffusion\nmodels, thereby achieving superior quality in 3D content generation,\nparticularly for complex objects and scenes. Experimental results demonstrate\nthat our SemanticSDS framework is highly effective for generating\nstate-of-the-art complex 3D content. Code:\nhttps://github.com/YangLing0818/SemanticSDS-3D\n", "date": "2024-10-11", "categories": []}
{"id": "2405.15891", "title": "Score Distillation via Reparametrized DDIM", "abstract": "  While 2D diffusion models generate realistic, high-detail images, 3D shape\ngeneration methods like Score Distillation Sampling (SDS) built on these 2D\ndiffusion models produce cartoon-like, over-smoothed shapes. To help explain\nthis discrepancy, we show that the image guidance used in Score Distillation\ncan be understood as the velocity field of a 2D denoising generative process,\nup to the choice of a noise term. In particular, after a change of variables,\nSDS resembles a high-variance version of Denoising Diffusion Implicit Models\n(DDIM) with a differently-sampled noise term: SDS introduces noise i.i.d.\nrandomly at each step, while DDIM infers it from the previous noise\npredictions. This excessive variance can lead to over-smoothing and unrealistic\noutputs. We show that a better noise approximation can be recovered by\ninverting DDIM in each SDS update step. This modification makes SDS's\ngenerative process for 2D images almost identical to DDIM. In 3D, it removes\nover-smoothing, preserves higher-frequency detail, and brings the generation\nquality closer to that of 2D samplers. Experimentally, our method achieves\nbetter or similar 3D generation quality compared to other state-of-the-art\nScore Distillation methods, all without training additional neural networks or\nmulti-view supervision, and providing useful insights into relationship between\n2D and 3D asset generation with diffusion models.\n", "date": "2024-05-24", "categories": []}
{"id": "2410.08181", "title": "RGM: Reconstructing High-fidelity 3D Car Assets with Relightable 3D-GS\n  Generative Model from a Single Image", "abstract": "  The generation of high-quality 3D car assets is essential for various\napplications, including video games, autonomous driving, and virtual reality.\nCurrent 3D generation methods utilizing NeRF or 3D-GS as representations for 3D\nobjects, generate a Lambertian object under fixed lighting and lack separated\nmodelings for material and global illumination. As a result, the generated\nassets are unsuitable for relighting under varying lighting conditions,\nlimiting their applicability in downstream tasks. To address this challenge, we\npropose a novel relightable 3D object generative framework that automates the\ncreation of 3D car assets, enabling the swift and accurate reconstruction of a\nvehicle's geometry, texture, and material properties from a single input image.\nOur approach begins with introducing a large-scale synthetic car dataset\ncomprising over 1,000 high-precision 3D vehicle models. We represent 3D objects\nusing global illumination and relightable 3D Gaussian primitives integrating\nwith BRDF parameters. Building on this representation, we introduce a\nfeed-forward model that takes images as input and outputs both relightable 3D\nGaussians and global illumination parameters. Experimental results demonstrate\nthat our method produces photorealistic 3D car assets that can be seamlessly\nintegrated into road scenes with different illuminations, which offers\nsubstantial practical benefits for industrial applications.\n", "date": "2024-10-10", "categories": []}
{"id": "2405.20669", "title": "Hybrid Fourier Score Distillation for Efficient One Image to 3D Object\n  Generation", "abstract": "  Single image-to-3D generation is pivotal for crafting controllable 3D assets.\nGiven its under-constrained nature, we attempt to leverage 3D geometric priors\nfrom a novel view diffusion model and 2D appearance priors from an image\ngeneration model to guide the optimization process. We note that there is a\ndisparity between the generation priors of these two diffusion models, leading\nto their different appearance outputs. Specifically, image generation models\ntend to deliver more detailed visuals, whereas novel view models produce\nconsistent yet over-smooth results across different views. Directly combining\nthem leads to suboptimal effects due to their appearance conflicts. Hence, we\npropose a 2D-3D hybrid Fourier Score Distillation objective function, hy-FSD.\nIt optimizes 3D Gaussians using 3D priors in spatial domain to ensure geometric\nconsistency, while exploiting 2D priors in the frequency domain through Fourier\ntransform for better visual quality. hy-FSD can be integrated into existing 3D\ngeneration methods and produce significant performance gains. With this\ntechnique, we further develop an image-to-3D generation pipeline to create\nhigh-quality 3D objects within one minute, named Fourier123. Extensive\nexperiments demonstrate that Fourier123 excels in efficient generation with\nrapid convergence speed and visually-friendly generation results.\n", "date": "2024-05-31", "categories": []}
{"id": "2406.10163", "title": "MeshAnything: Artist-Created Mesh Generation with Autoregressive\n  Transformers", "abstract": "  Recently, 3D assets created via reconstruction and generation have matched\nthe quality of manually crafted assets, highlighting their potential for\nreplacement. However, this potential is largely unrealized because these assets\nalways need to be converted to meshes for 3D industry applications, and the\nmeshes produced by current mesh extraction methods are significantly inferior\nto Artist-Created Meshes (AMs), i.e., meshes created by human artists.\nSpecifically, current mesh extraction methods rely on dense faces and ignore\ngeometric features, leading to inefficiencies, complicated post-processing, and\nlower representation quality. To address these issues, we introduce\nMeshAnything, a model that treats mesh extraction as a generation problem,\nproducing AMs aligned with specified shapes. By converting 3D assets in any 3D\nrepresentation into AMs, MeshAnything can be integrated with various 3D asset\nproduction methods, thereby enhancing their application across the 3D industry.\nThe architecture of MeshAnything comprises a VQ-VAE and a shape-conditioned\ndecoder-only transformer. We first learn a mesh vocabulary using the VQ-VAE,\nthen train the shape-conditioned decoder-only transformer on this vocabulary\nfor shape-conditioned autoregressive mesh generation. Our extensive experiments\nshow that our method generates AMs with hundreds of times fewer faces,\nsignificantly improving storage, rendering, and simulation efficiencies, while\nachieving precision comparable to previous methods.\n", "date": "2024-06-14", "categories": []}
{"id": "2408.05008", "title": "FlowDreamer: Exploring High Fidelity Text-to-3D Generation via Rectified\n  Flow", "abstract": "  Recent advances in text-to-3D generation have made significant progress. In\nparticular, with the pretrained diffusion models, existing methods\npredominantly use Score Distillation Sampling (SDS) to train 3D models such as\nNeural RaRecent advances in text-to-3D generation have made significant\nprogress. In particular, with the pretrained diffusion models, existing methods\npredominantly use Score Distillation Sampling (SDS) to train 3D models such as\nNeural Radiance Fields (NeRF) and 3D Gaussian Splatting (3D GS). However, a\nhurdle is that they often encounter difficulties with over-smoothing textures\nand over-saturating colors. The rectified flow model -- which utilizes a simple\nordinary differential equation (ODE) to represent a straight trajectory --\nshows promise as an alternative prior to text-to-3D generation. It learns a\ntime-independent vector field, thereby reducing the ambiguity in 3D model\nupdate gradients that are calculated using time-dependent scores in the SDS\nframework. In light of this, we first develop a mathematical analysis to\nseamlessly integrate SDS with rectified flow model, paving the way for our\ninitial framework known as Vector Field Distillation Sampling (VFDS). However,\nempirical findings indicate that VFDS still results in over-smoothing outcomes.\nTherefore, we analyze the grounding reasons for such a failure from the\nperspective of ODE trajectories. On top, we propose a novel framework, named\nFlowDreamer, which yields high fidelity results with richer textual details and\nfaster convergence. The key insight is to leverage the coupling and reversible\nproperties of the rectified flow model to search for the corresponding noise,\nrather than using randomly sampled noise as in VFDS. Accordingly, we introduce\na novel Unique Couple Matching (UCM) loss, which guides the 3D model to\noptimize along the same trajectory.\n", "date": "2024-08-09", "categories": []}
{"id": "2410.03417", "title": "Img2CAD: Conditioned 3D CAD Model Generation from Single Image with\n  Structured Visual Geometry", "abstract": "  In this paper, we propose Img2CAD, the first approach to our knowledge that\nuses 2D image inputs to generate CAD models with editable parameters. Unlike\nexisting AI methods for 3D model generation using text or image inputs often\nrely on mesh-based representations, which are incompatible with CAD tools and\nlack editability and fine control, Img2CAD enables seamless integration between\nAI-based 3D reconstruction and CAD software. We have identified an innovative\nintermediate representation called Structured Visual Geometry (SVG),\ncharacterized by vectorized wireframes extracted from objects. This\nrepresentation significantly enhances the performance of generating conditioned\nCAD models. Additionally, we introduce two new datasets to further support\nresearch in this area: ABC-mono, the largest known dataset comprising over\n200,000 3D CAD models with rendered images, and KOCAD, the first dataset\nfeaturing real-world captured objects alongside their ground truth CAD models,\nsupporting further research in conditioned CAD model generation.\n", "date": "2024-10-04", "categories": []}
{"id": "2404.02148", "title": "Diffusion$^2$: Dynamic 3D Content Generation via Score Composition of\n  Video and Multi-view Diffusion Models", "abstract": "  Recent advancements in 3D generation are predominantly propelled by\nimprovements in 3D-aware image diffusion models. These models are pretrained on\nInternet-scale image data and fine-tuned on massive 3D data, offering the\ncapability of producing highly consistent multi-view images. However, due to\nthe scarcity of synchronized multi-view video data, it remains challenging to\nadapt this paradigm to 4D generation directly. Despite that, the available\nvideo and 3D data are adequate for training video and multi-view diffusion\nmodels separately that can provide satisfactory dynamic and geometric priors\nrespectively. To take advantage of both, this paper presents Diffusion$^2$, a\nnovel framework for dynamic 3D content creation that reconciles the knowledge\nabout geometric consistency and temporal smoothness from these models to\ndirectly sample dense multi-view multi-frame images which can be employed to\noptimize continuous 4D representation. Specifically, we design a simple yet\neffective denoising strategy via score composition of pretrained video and\nmulti-view diffusion models based on the probability structure of the target\nimage array. To alleviate the potential conflicts between two heterogeneous\nscores, we further introduce variance-reducing sampling via interpolated steps,\nfacilitating smooth and stable generation. Owing to the high parallelism of the\nproposed image generation process and the efficiency of the modern 4D\nreconstruction pipeline, our framework can generate 4D content within few\nminutes. Notably, our method circumvents the reliance on expensive and\nhard-to-scale 4D data, thereby having the potential to benefit from the scaling\nof the foundation video and multi-view diffusion models. Extensive experiments\ndemonstrate the efficacy of our proposed framework in generating highly\nseamless and consistent 4D assets under various types of conditions.\n", "date": "2024-04-02", "categories": []}
{"id": "2410.01226", "title": "Towards Native Generative Model for 3D Head Avatar", "abstract": "  Creating 3D head avatars is a significant yet challenging task for many\napplicated scenarios. Previous studies have set out to learn 3D human head\ngenerative models using massive 2D image data. Although these models are highly\ngeneralizable for human appearance, their result models are not\n360$^\\circ$-renderable, and the predicted 3D geometry is unreliable. Therefore,\nsuch results cannot be used in VR, game modeling, and other scenarios that\nrequire 360$^\\circ$-renderable 3D head models. An intuitive idea is that 3D\nhead models with limited amount but high 3D accuracy are more reliable training\ndata for a high-quality 3D generative model. In this vein, we delve into how to\nlearn a native generative model for 360$^\\circ$ full head from a limited 3D\nhead dataset. Specifically, three major problems are studied: 1) how to\neffectively utilize various representations for generating the\n360$^\\circ$-renderable human head; 2) how to disentangle the appearance, shape,\nand motion of human faces to generate a 3D head model that can be edited by\nappearance and driven by motion; 3) and how to extend the generalization\ncapability of the generative model to support downstream tasks. Comprehensive\nexperiments are conducted to verify the effectiveness of the proposed model. We\nhope the proposed models and artist-designed dataset can inspire future\nresearch on learning native generative 3D head models from limited 3D datasets.\n", "date": "2024-10-02", "categories": []}
{"id": "2312.02647", "title": "TPA3D: Triplane Attention for Fast Text-to-3D Generation", "abstract": "  Due to the lack of large-scale text-3D correspondence data, recent text-to-3D\ngeneration works mainly rely on utilizing 2D diffusion models for synthesizing\n3D data. Since diffusion-based methods typically require significant\noptimization time for both training and inference, the use of GAN-based models\nwould still be desirable for fast 3D generation. In this work, we propose\nTriplane Attention for text-guided 3D generation (TPA3D), an end-to-end\ntrainable GAN-based deep learning model for fast text-to-3D generation. With\nonly 3D shape data and their rendered 2D images observed during training, our\nTPA3D is designed to retrieve detailed visual descriptions for synthesizing the\ncorresponding 3D mesh data. This is achieved by the proposed attention\nmechanisms on the extracted sentence and word-level text features. In our\nexperiments, we show that TPA3D generates high-quality 3D textured shapes\naligned with fine-grained descriptions, while impressive computation efficiency\ncan be observed.\n", "date": "2023-12-05", "categories": []}
{"id": "2409.18114", "title": "EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation", "abstract": "  Current auto-regressive mesh generation methods suffer from issues such as\nincompleteness, insufficient detail, and poor generalization. In this paper, we\npropose an Auto-regressive Auto-encoder (ArAE) model capable of generating\nhigh-quality 3D meshes with up to 4,000 faces at a spatial resolution of\n$512^3$. We introduce a novel mesh tokenization algorithm that efficiently\ncompresses triangular meshes into 1D token sequences, significantly enhancing\ntraining efficiency. Furthermore, our model compresses variable-length\ntriangular meshes into a fixed-length latent space, enabling training latent\ndiffusion models for better generalization. Extensive experiments demonstrate\nthe superior quality, diversity, and generalization capabilities of our model\nin both point cloud and image-conditioned mesh generation tasks.\n", "date": "2024-09-26", "categories": []}
{"id": "2401.09384", "title": "Diverse Part Synthesis for 3D Shape Creation", "abstract": "  Methods that use neural networks for synthesizing 3D shapes in the form of a\npart-based representation have been introduced over the last few years. These\nmethods represent shapes as a graph or hierarchy of parts and enable a variety\nof applications such as shape sampling and reconstruction. However, current\nmethods do not allow easily regenerating individual shape parts according to\nuser preferences. In this paper, we investigate techniques that allow the user\nto generate multiple, diverse suggestions for individual parts. Specifically,\nwe experiment with multimodal deep generative models that allow sampling\ndiverse suggestions for shape parts and focus on models which have not been\nconsidered in previous work on shape synthesis. To provide a comparative study\nof these techniques, we introduce a method for synthesizing 3D shapes in a\npart-based representation and evaluate all the part suggestion techniques\nwithin this synthesis method. In our method, which is inspired by previous\nwork, shapes are represented as a set of parts in the form of implicit\nfunctions which are then positioned in space to form the final shape. Synthesis\nin this representation is enabled by a neural network architecture based on an\nimplicit decoder and a spatial transformer. We compare the various multimodal\ngenerative models by evaluating their performance in generating part\nsuggestions. Our contribution is to show with qualitative and quantitative\nevaluations which of the new techniques for multimodal part generation perform\nthe best and that a synthesis method based on the top-performing techniques\nallows the user to more finely control the parts that are generated in the 3D\nshapes while maintaining high shape fidelity when reconstructing shapes.\n", "date": "2024-01-17", "categories": []}
{"id": "2409.05099", "title": "DreamMapping: High-Fidelity Text-to-3D Generation via Variational\n  Distribution Mapping", "abstract": "  Score Distillation Sampling (SDS) has emerged as a prevalent technique for\ntext-to-3D generation, enabling 3D content creation by distilling\nview-dependent information from text-to-2D guidance. However, they frequently\nexhibit shortcomings such as over-saturated color and excess smoothness. In\nthis paper, we conduct a thorough analysis of SDS and refine its formulation,\nfinding that the core design is to model the distribution of rendered images.\nFollowing this insight, we introduce a novel strategy called Variational\nDistribution Mapping (VDM), which expedites the distribution modeling process\nby regarding the rendered images as instances of degradation from\ndiffusion-based generation. This special design enables the efficient training\nof variational distribution by skipping the calculations of the Jacobians in\nthe diffusion U-Net. We also introduce timestep-dependent Distribution\nCoefficient Annealing (DCA) to further improve distilling precision. Leveraging\nVDM and DCA, we use Gaussian Splatting as the 3D representation and build a\ntext-to-3D generation framework. Extensive experiments and evaluations\ndemonstrate the capability of VDM and DCA to generate high-fidelity and\nrealistic assets with optimization efficiency.\n", "date": "2024-09-08", "categories": []}
{"id": "2409.12193", "title": "Vista3D: Unravel the 3D Darkside of a Single Image", "abstract": "  We embark on the age-old quest: unveiling the hidden dimensions of objects\nfrom mere glimpses of their visible parts. To address this, we present Vista3D,\na framework that realizes swift and consistent 3D generation within a mere 5\nminutes. At the heart of Vista3D lies a two-phase approach: the coarse phase\nand the fine phase. In the coarse phase, we rapidly generate initial geometry\nwith Gaussian Splatting from a single image. In the fine phase, we extract a\nSigned Distance Function (SDF) directly from learned Gaussian Splatting,\noptimizing it with a differentiable isosurface representation. Furthermore, it\nelevates the quality of generation by using a disentangled representation with\ntwo independent implicit functions to capture both visible and obscured aspects\nof objects. Additionally, it harmonizes gradients from 2D diffusion prior with\n3D-aware diffusion priors by angular diffusion prior composition. Through\nextensive evaluation, we demonstrate that Vista3D effectively sustains a\nbalance between the consistency and diversity of the generated 3D objects.\nDemos and code will be available at https://github.com/florinshen/Vista3D.\n", "date": "2024-09-18", "categories": []}
{"id": "2409.11406", "title": "Phidias: A Generative Model for Creating 3D Content from Text, Image,\n  and 3D Conditions with Reference-Augmented Diffusion", "abstract": "  In 3D modeling, designers often use an existing 3D model as a reference to\ncreate new ones. This practice has inspired the development of Phidias, a novel\ngenerative model that uses diffusion for reference-augmented 3D generation.\nGiven an image, our method leverages a retrieved or user-provided 3D reference\nmodel to guide the generation process, thereby enhancing the generation\nquality, generalization ability, and controllability. Our model integrates\nthree key components: 1) meta-ControlNet that dynamically modulates the\nconditioning strength, 2) dynamic reference routing that mitigates misalignment\nbetween the input image and 3D reference, and 3) self-reference augmentations\nthat enable self-supervised training with a progressive curriculum.\nCollectively, these designs result in a clear improvement over existing\nmethods. Phidias establishes a unified framework for 3D generation using text,\nimage, and 3D conditions with versatile applications.\n", "date": "2024-09-17", "categories": []}
{"id": "2409.10180", "title": "RealDiff: Real-world 3D Shape Completion using Self-Supervised Diffusion\n  Models", "abstract": "  Point cloud completion aims to recover the complete 3D shape of an object\nfrom partial observations. While approaches relying on synthetic shape priors\nachieved promising results in this domain, their applicability and\ngeneralizability to real-world data are still limited. To tackle this problem,\nwe propose a self-supervised framework, namely RealDiff, that formulates point\ncloud completion as a conditional generation problem directly on real-world\nmeasurements. To better deal with noisy observations without resorting to\ntraining on synthetic data, we leverage additional geometric cues.\nSpecifically, RealDiff simulates a diffusion process at the missing object\nparts while conditioning the generation on the partial input to address the\nmultimodal nature of the task. We further regularize the training by matching\nobject silhouettes and depth maps, predicted by our method, with the externally\nestimated ones. Experimental results show that our method consistently\noutperforms state-of-the-art methods in real-world point cloud completion.\n", "date": "2024-09-16", "categories": []}
{"id": "2409.08271", "title": "DreamBeast: Distilling 3D Fantastical Animals with Part-Aware Knowledge\n  Transfer", "abstract": "  We present DreamBeast, a novel method based on score distillation sampling\n(SDS) for generating fantastical 3D animal assets composed of distinct parts.\nExisting SDS methods often struggle with this generation task due to a limited\nunderstanding of part-level semantics in text-to-image diffusion models. While\nrecent diffusion models, such as Stable Diffusion 3, demonstrate a better\npart-level understanding, they are prohibitively slow and exhibit other common\nproblems associated with single-view diffusion models. DreamBeast overcomes\nthis limitation through a novel part-aware knowledge transfer mechanism. For\neach generated asset, we efficiently extract part-level knowledge from the\nStable Diffusion 3 model into a 3D Part-Affinity implicit representation. This\nenables us to instantly generate Part-Affinity maps from arbitrary camera\nviews, which we then use to modulate the guidance of a multi-view diffusion\nmodel during SDS to create 3D assets of fantastical animals. DreamBeast\nsignificantly enhances the quality of generated 3D creatures with\nuser-specified part compositions while reducing computational overhead, as\ndemonstrated by extensive quantitative and qualitative evaluations.\n", "date": "2024-09-12", "categories": []}
{"id": "2406.01592", "title": "Text-guided Controllable Mesh Refinement for Interactive 3D Modeling", "abstract": "  We propose a novel technique for adding geometric details to an input coarse\n3D mesh guided by a text prompt. Our method is composed of three stages. First,\nwe generate a single-view RGB image conditioned on the input coarse geometry\nand the input text prompt. This single-view image generation step allows the\nuser to pre-visualize the result and offers stronger conditioning for\nsubsequent multi-view generation. Second, we use our novel multi-view normal\ngeneration architecture to jointly generate six different views of the normal\nimages. The joint view generation reduces inconsistencies and leads to sharper\ndetails. Third, we optimize our mesh with respect to all views and generate a\nfine, detailed geometry as output. The resulting method produces an output\nwithin seconds and offers explicit user control over the coarse structure,\npose, and desired details of the resulting 3D mesh.\n", "date": "2024-06-03", "categories": []}
{"id": "2409.07452", "title": "Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video\n  Diffusion Models", "abstract": "  Despite having tremendous progress in image-to-3D generation, existing\nmethods still struggle to produce multi-view consistent images with\nhigh-resolution textures in detail, especially in the paradigm of 2D diffusion\nthat lacks 3D awareness. In this work, we present High-resolution Image-to-3D\nmodel (Hi3D), a new video diffusion based paradigm that redefines a single\nimage to multi-view images as 3D-aware sequential image generation (i.e.,\norbital video generation). This methodology delves into the underlying temporal\nconsistency knowledge in video diffusion model that generalizes well to\ngeometry consistency across multiple views in 3D generation. Technically, Hi3D\nfirst empowers the pre-trained video diffusion model with 3D-aware prior\n(camera pose condition), yielding multi-view images with low-resolution texture\ndetails. A 3D-aware video-to-video refiner is learnt to further scale up the\nmulti-view images with high-resolution texture details. Such high-resolution\nmulti-view images are further augmented with novel views through 3D Gaussian\nSplatting, which are finally leveraged to obtain high-fidelity meshes via 3D\nreconstruction. Extensive experiments on both novel view synthesis and single\nview reconstruction demonstrate that our Hi3D manages to produce superior\nmulti-view consistency images with highly-detailed textures. Source code and\ndata are available at \\url{https://github.com/yanghb22-fdu/Hi3D-Official}.\n", "date": "2024-09-11", "categories": []}
{"id": "2409.07454", "title": "DreamMesh: Jointly Manipulating and Texturing Triangle Meshes for\n  Text-to-3D Generation", "abstract": "  Learning radiance fields (NeRF) with powerful 2D diffusion models has\ngarnered popularity for text-to-3D generation. Nevertheless, the implicit 3D\nrepresentations of NeRF lack explicit modeling of meshes and textures over\nsurfaces, and such surface-undefined way may suffer from the issues, e.g.,\nnoisy surfaces with ambiguous texture details or cross-view inconsistency. To\nalleviate this, we present DreamMesh, a novel text-to-3D architecture that\npivots on well-defined surfaces (triangle meshes) to generate high-fidelity\nexplicit 3D model. Technically, DreamMesh capitalizes on a distinctive\ncoarse-to-fine scheme. In the coarse stage, the mesh is first deformed by\ntext-guided Jacobians and then DreamMesh textures the mesh with an interlaced\nuse of 2D diffusion models in a tuning free manner from multiple viewpoints. In\nthe fine stage, DreamMesh jointly manipulates the mesh and refines the texture\nmap, leading to high-quality triangle meshes with high-fidelity textured\nmaterials. Extensive experiments demonstrate that DreamMesh significantly\noutperforms state-of-the-art text-to-3D methods in faithfully generating 3D\ncontent with richer textual details and enhanced geometry. Our project page is\navailable at https://dreammesh.github.io.\n", "date": "2024-09-11", "categories": []}
{"id": "2401.11067", "title": "Make-A-Shape: a Ten-Million-scale 3D Shape Model", "abstract": "  Significant progress has been made in training large generative models for\nnatural language and images. Yet, the advancement of 3D generative models is\nhindered by their substantial resource demands for training, along with\ninefficient, non-compact, and less expressive representations. This paper\nintroduces Make-A-Shape, a new 3D generative model designed for efficient\ntraining on a vast scale, capable of utilizing 10 millions publicly-available\nshapes. Technical-wise, we first innovate a wavelet-tree representation to\ncompactly encode shapes by formulating the subband coefficient filtering scheme\nto efficiently exploit coefficient relations. We then make the representation\ngeneratable by a diffusion model by devising the subband coefficients packing\nscheme to layout the representation in a low-resolution grid. Further, we\nderive the subband adaptive training strategy to train our model to effectively\nlearn to generate coarse and detail wavelet coefficients. Last, we extend our\nframework to be controlled by additional input conditions to enable it to\ngenerate shapes from assorted modalities, e.g., single/multi-view images, point\nclouds, and low-resolution voxels. In our extensive set of experiments, we\ndemonstrate various applications, such as unconditional generation, shape\ncompletion, and conditional generation on a wide range of modalities. Our\napproach not only surpasses the state of the art in delivering high-quality\nresults but also efficiently generates shapes within a few seconds, often\nachieving this in just 2 seconds for most conditions. Our source code is\navailable at https://github.com/AutodeskAILab/Make-a-Shape.\n", "date": "2024-01-20", "categories": []}
{"id": "2409.06129", "title": "DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned\n  Geometry Enhancement", "abstract": "  We present a 3D modeling method which enables end-users to refine or\ndetailize 3D shapes using machine learning, expanding the capabilities of\nAI-assisted 3D content creation. Given a coarse voxel shape (e.g., one produced\nwith a simple box extrusion tool or via generative modeling), a user can\ndirectly \"paint\" desired target styles representing compelling geometric\ndetails, from input exemplar shapes, over different regions of the coarse\nshape. These regions are then up-sampled into high-resolution geometries which\nadhere with the painted styles. To achieve such controllable and localized 3D\ndetailization, we build on top of a Pyramid GAN by making it masking-aware. We\ndevise novel structural losses and priors to ensure that our method preserves\nboth desired coarse structures and fine-grained features even if the painted\nstyles are borrowed from diverse sources, e.g., different semantic parts and\neven different shape categories. Through extensive experiments, we show that\nour ability to localize details enables novel interactive creative workflows\nand applications. Our experiments further demonstrate that in comparison to\nprior techniques built on global detailization, our method generates\nstructure-preserving, high-resolution stylized geometries with more coherent\nshape details and style transitions.\n", "date": "2024-09-10", "categories": []}
{"id": "2409.06322", "title": "G3PT: Unleash the power of Autoregressive Modeling in 3D Generation via\n  Cross-scale Querying Transformer", "abstract": "  Autoregressive transformers have revolutionized generative models in language\nprocessing and shown substantial promise in image and video generation.\nHowever, these models face significant challenges when extended to 3D\ngeneration tasks due to their reliance on next-token prediction to learn token\nsequences, which is incompatible with the unordered nature of 3D data. Instead\nof imposing an artificial order on 3D data, in this paper, we introduce G3PT, a\nscalable coarse-to-fine 3D generative model utilizing a cross-scale querying\ntransformer. The key is to map point-based 3D data into discrete tokens with\ndifferent levels of detail, naturally establishing a sequential relationship\nbetween different levels suitable for autoregressive modeling. Additionally,\nthe cross-scale querying transformer connects tokens globally across different\nlevels of detail without requiring an ordered sequence. Benefiting from this\napproach, G3PT features a versatile 3D generation pipeline that effortlessly\nsupports diverse conditional structures, enabling the generation of 3D shapes\nfrom various types of conditions. Extensive experiments demonstrate that G3PT\nachieves superior generation quality and generalization ability compared to\nprevious 3D generation methods. Most importantly, for the first time in 3D\ngeneration, scaling up G3PT reveals distinct power-law scaling behaviors.\n", "date": "2024-09-10", "categories": []}
{"id": "2409.06620", "title": "MVGaussian: High-Fidelity text-to-3D Content Generation with Multi-View\n  Guidance and Surface Densification", "abstract": "  The field of text-to-3D content generation has made significant progress in\ngenerating realistic 3D objects, with existing methodologies like Score\nDistillation Sampling (SDS) offering promising guidance. However, these methods\noften encounter the \"Janus\" problem-multi-face ambiguities due to imprecise\nguidance. Additionally, while recent advancements in 3D gaussian splitting have\nshown its efficacy in representing 3D volumes, optimization of this\nrepresentation remains largely unexplored. This paper introduces a unified\nframework for text-to-3D content generation that addresses these critical gaps.\nOur approach utilizes multi-view guidance to iteratively form the structure of\nthe 3D model, progressively enhancing detail and accuracy. We also introduce a\nnovel densification algorithm that aligns gaussians close to the surface,\noptimizing the structural integrity and fidelity of the generated models.\nExtensive experiments validate our approach, demonstrating that it produces\nhigh-quality visual outputs with minimal time cost. Notably, our method\nachieves high-quality results within half an hour of training, offering a\nsubstantial efficiency gain over most existing methods, which require hours of\ntraining time to achieve comparable results.\n", "date": "2024-09-10", "categories": []}
{"id": "2409.03718", "title": "Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with\n  Image-Based Surface Representation", "abstract": "  Generating high-quality 3D objects from textual descriptions remains a\nchallenging problem due to computational cost, the scarcity of 3D data, and\ncomplex 3D representations. We introduce Geometry Image Diffusion\n(GIMDiffusion), a novel Text-to-3D model that utilizes geometry images to\nefficiently represent 3D shapes using 2D images, thereby avoiding the need for\ncomplex 3D-aware architectures. By integrating a Collaborative Control\nmechanism, we exploit the rich 2D priors of existing Text-to-Image models such\nas Stable Diffusion. This enables strong generalization even with limited 3D\ntraining data (allowing us to use only high-quality training data) as well as\nretaining compatibility with guidance techniques such as IPAdapter. In short,\nGIMDiffusion enables the generation of 3D assets at speeds comparable to\ncurrent Text-to-Image models. The generated objects consist of semantically\nmeaningful, separate parts and include internal structures, enhancing both\nusability and versatility.\n", "date": "2024-09-05", "categories": []}
{"id": "2408.10846", "title": "Harmonizing Attention: Training-free Texture-aware Geometry Transfer", "abstract": "  Extracting geometry features from photographic images independently of\nsurface texture and transferring them onto different materials remains a\ncomplex challenge. In this study, we introduce Harmonizing Attention, a novel\ntraining-free approach that leverages diffusion models for texture-aware\ngeometry transfer. Our method employs a simple yet effective modification of\nself-attention layers, allowing the model to query information from multiple\nreference images within these layers. This mechanism is seamlessly integrated\ninto the inversion process as Texture-aligning Attention and into the\ngeneration process as Geometry-aligning Attention. This dual-attention approach\nensures the effective capture and transfer of material-independent geometry\nfeatures while maintaining material-specific textural continuity, all without\nthe need for model fine-tuning.\n", "date": "2024-08-19", "categories": []}
{"id": "2409.00590", "title": "COMOGen: A Controllable Text-to-3D Multi-object Generation Framework", "abstract": "  The controllability of 3D object generation methods is achieved through input\ntext. Existing text-to-3D object generation methods primarily focus on\ngenerating a single object based on a single object description. However, these\nmethods often face challenges in producing results that accurately correspond\nto our desired positions when the input text involves multiple objects. To\naddress the issue of controllability in generating multiple objects, this paper\nintroduces COMOGen, a COntrollable text-to-3D Multi-Object Generation\nframework. COMOGen enables the simultaneous generation of multiple 3D objects\nby the distillation of layout and multi-view prior knowledge. The framework\nconsists of three modules: the layout control module, the multi-view\nconsistency control module, and the 3D content enhancement module. Moreover, to\nintegrate these three modules as an integral framework, we propose Layout\nMulti-view Score Distillation, which unifies two prior knowledge and further\nenhances the diversity and quality of generated 3D content. Comprehensive\nexperiments demonstrate the effectiveness of our approach compared to the\nstate-of-the-art methods, which represents a significant step forward in\nenabling more controlled and versatile text-based 3D content generation.\n", "date": "2024-09-01", "categories": []}
{"id": "2403.16210", "title": "Frankenstein: Generating Semantic-Compositional 3D Scenes in One\n  Tri-Plane", "abstract": "  We present Frankenstein, a diffusion-based framework that can generate\nsemantic-compositional 3D scenes in a single pass. Unlike existing methods that\noutput a single, unified 3D shape, Frankenstein simultaneously generates\nmultiple separated shapes, each corresponding to a semantically meaningful\npart. The 3D scene information is encoded in one single tri-plane tensor, from\nwhich multiple Singed Distance Function (SDF) fields can be decoded to\nrepresent the compositional shapes. During training, an auto-encoder compresses\ntri-planes into a latent space, and then the denoising diffusion process is\nemployed to approximate the distribution of the compositional scenes.\nFrankenstein demonstrates promising results in generating room interiors as\nwell as human avatars with automatically separated parts. The generated scenes\nfacilitate many downstream applications, such as part-wise re-texturing, object\nrearrangement in the room or avatar cloth re-targeting. Our project page is\navailable at: https://wolfball.github.io/frankenstein/.\n", "date": "2024-03-24", "categories": []}
{"id": "2307.10895", "title": "Variational Autoencoding of Dental Point Clouds", "abstract": "  Digital dentistry has made significant advancements, yet numerous challenges\nremain. This paper introduces the FDI 16 dataset, an extensive collection of\ntooth meshes and point clouds. Additionally, we present a novel approach:\nVariational FoldingNet (VF-Net), a fully probabilistic variational autoencoder\nfor point clouds. Notably, prior latent variable models for point clouds lack a\none-to-one correspondence between input and output points. Instead, they rely\non optimizing Chamfer distances, a metric that lacks a normalized\ndistributional counterpart, rendering it unsuitable for probabilistic modeling.\nWe replace the explicit minimization of Chamfer distances with a suitable\nencoder, increasing computational efficiency while simplifying the\nprobabilistic extension. This allows for straightforward application in various\ntasks, including mesh generation, shape completion, and representation\nlearning. Empirically, we provide evidence of lower reconstruction error in\ndental reconstruction and interpolation, showcasing state-of-the-art\nperformance in dental sample generation while identifying valuable latent\nrepresentations\n", "date": "2023-07-20", "categories": []}
{"id": "2408.14860", "title": "DiffSurf: A Transformer-based Diffusion Model for Generating and\n  Reconstructing 3D Surfaces in Pose", "abstract": "  This paper presents DiffSurf, a transformer-based denoising diffusion model\nfor generating and reconstructing 3D surfaces. Specifically, we design a\ndiffusion transformer architecture that predicts noise from noisy 3D surface\nvertices and normals. With this architecture, DiffSurf is able to generate 3D\nsurfaces in various poses and shapes, such as human bodies, hands, animals and\nman-made objects. Further, DiffSurf is versatile in that it can address various\n3D downstream tasks including morphing, body shape variation and 3D human mesh\nfitting to 2D keypoints. Experimental results on 3D human model benchmarks\ndemonstrate that DiffSurf can generate shapes with greater diversity and higher\nquality than previous generative models. Furthermore, when applied to the task\nof single-image 3D human mesh recovery, DiffSurf achieves accuracy comparable\nto prior techniques at a near real-time rate.\n", "date": "2024-08-27", "categories": []}
{"id": "2312.01129", "title": "ControlDreamer: Blending Geometry and Style in Text-to-3D", "abstract": "  Recent advancements in text-to-3D generation have significantly contributed\nto the automation and democratization of 3D content creation. Building upon\nthese developments, we aim to address the limitations of current methods in\nblending geometries and styles in text-to-3D generation. We introduce\nmulti-view ControlNet, a novel depth-aware multi-view diffusion model trained\non generated datasets from a carefully curated text corpus. Our multi-view\nControlNet is then integrated into our two-stage pipeline, ControlDreamer,\nenabling text-guided generation of stylized 3D models. Additionally, we present\na comprehensive benchmark for 3D style editing, encompassing a broad range of\nsubjects, including objects, animals, and characters, to further facilitate\nresearch on diverse 3D generation. Our comparative analysis reveals that this\nnew pipeline outperforms existing text-to-3D methods as evidenced by human\nevaluations and CLIP score metrics. Project page:\nhttps://controldreamer.github.io\n", "date": "2023-12-02", "categories": []}
{"id": "2405.20327", "title": "GECO: Generative Image-to-3D within a SECOnd", "abstract": "  Recent years have seen significant advancements in 3D generation. While\nmethods like score distillation achieve impressive results, they often require\nextensive per-scene optimization, which limits their time efficiency. On the\nother hand, reconstruction-based approaches are more efficient but tend to\ncompromise quality due to their limited ability to handle uncertainty. We\nintroduce GECO, a novel method for high-quality 3D generative modeling that\noperates within a second. Our approach addresses the prevalent issues of\nuncertainty and inefficiency in existing methods through a two-stage approach.\nIn the first stage, we train a single-step multi-view generative model with\nscore distillation. Then, a second-stage distillation is applied to address the\nchallenge of view inconsistency in the multi-view generation. This two-stage\nprocess ensures a balanced approach to 3D generation, optimizing both quality\nand efficiency. Our comprehensive experiments demonstrate that GECO achieves\nhigh-quality image-to-3D mesh generation with an unprecedented level of\nefficiency. We will make the code and model publicly available.\n", "date": "2024-05-30", "categories": []}
{"id": "2408.10935", "title": "Large Point-to-Gaussian Model for Image-to-3D Generation", "abstract": "  Recently, image-to-3D approaches have significantly advanced the generation\nquality and speed of 3D assets based on large reconstruction models,\nparticularly 3D Gaussian reconstruction models. Existing large 3D Gaussian\nmodels directly map 2D image to 3D Gaussian parameters, while regressing 2D\nimage to 3D Gaussian representations is challenging without 3D priors. In this\npaper, we propose a large Point-to-Gaussian model, that inputs the initial\npoint cloud produced from large 3D diffusion model conditional on 2D image to\ngenerate the Gaussian parameters, for image-to-3D generation. The point cloud\nprovides initial 3D geometry prior for Gaussian generation, thus significantly\nfacilitating image-to-3D Generation. Moreover, we present the\n\\textbf{A}ttention mechanism, \\textbf{P}rojection mechanism, and \\textbf{P}oint\nfeature extractor, dubbed as \\textbf{APP} block, for fusing the image features\nwith point cloud features. The qualitative and quantitative experiments\nextensively demonstrate the effectiveness of the proposed approach on GSO and\nObjaverse datasets, and show the proposed method achieves state-of-the-art\nperformance.\n", "date": "2024-08-20", "categories": []}
{"id": "2406.17342", "title": "Masked Generative Extractor for Synergistic Representation and 3D\n  Generation of Point Clouds", "abstract": "  Representation and generative learning, as reconstruction-based methods, have\ndemonstrated their potential for mutual reinforcement across various domains.\nIn the field of point cloud processing, although existing studies have adopted\ntraining strategies from generative models to enhance representational\ncapabilities, these methods are limited by their inability to genuinely\ngenerate 3D shapes. To explore the benefits of deeply integrating 3D\nrepresentation learning and generative learning, we propose an innovative\nframework called \\textit{Point-MGE}. Specifically, this framework first\nutilizes a vector quantized variational autoencoder to reconstruct a neural\nfield representation of 3D shapes, thereby learning discrete semantic features\nof point patches. Subsequently, we design a sliding masking ratios to smooth\nthe transition from representation learning to generative learning. Moreover,\nour method demonstrates strong generalization capability in learning\nhigh-capacity models, achieving new state-of-the-art performance across\nmultiple downstream tasks. In shape classification, Point-MGE achieved an\naccuracy of 94.2% (+1.0%) on the ModelNet40 dataset and 92.9% (+5.5%) on the\nScanObjectNN dataset. Experimental results also confirmed that Point-MGE can\ngenerate high-quality 3D shapes in both unconditional and conditional settings.\n", "date": "2024-06-25", "categories": []}
{"id": "2312.11459", "title": "VolumeDiffusion: Flexible Text-to-3D Generation with Efficient\n  Volumetric Encoder", "abstract": "  This paper introduces a pioneering 3D volumetric encoder designed for\ntext-to-3D generation. To scale up the training data for the diffusion model, a\nlightweight network is developed to efficiently acquire feature volumes from\nmulti-view images. The 3D volumes are then trained on a diffusion model for\ntext-to-3D generation using a 3D U-Net. This research further addresses the\nchallenges of inaccurate object captions and high-dimensional feature volumes.\nThe proposed model, trained on the public Objaverse dataset, demonstrates\npromising outcomes in producing diverse and recognizable samples from text\nprompts. Notably, it empowers finer control over object part characteristics\nthrough textual cues, fostering model creativity by seamlessly combining\nmultiple concepts within a single object. This research significantly\ncontributes to the progress of 3D generation by introducing an efficient,\nflexible, and scalable representation methodology.\n", "date": "2023-12-18", "categories": []}
{"id": "2408.06145", "title": "Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel\n  Diffusion Models", "abstract": "  We propose a novel point cloud U-Net diffusion architecture for 3D generative\nmodeling capable of generating high-quality and diverse 3D shapes while\nmaintaining fast generation times. Our network employs a dual-branch\narchitecture, combining the high-resolution representations of points with the\ncomputational efficiency of sparse voxels. Our fastest variant outperforms all\nnon-diffusion generative approaches on unconditional shape generation, the most\npopular benchmark for evaluating point cloud generative models, while our\nlargest model achieves state-of-the-art results among diffusion methods, with a\nruntime approximately 70% of the previously state-of-the-art PVD. Beyond\nunconditional generation, we perform extensive evaluations, including\nconditional generation on all categories of ShapeNet, demonstrating the\nscalability of our model to larger datasets, and implicit generation which\nallows our network to produce high quality point clouds on fewer timesteps,\nfurther decreasing the generation time. Finally, we evaluate the architecture's\nperformance in point cloud completion and super-resolution. Our model excels in\nall tasks, establishing it as a state-of-the-art diffusion U-Net for point\ncloud generative modeling. The code is publicly available at\nhttps://github.com/JohnRomanelis/SPVD.git.\n", "date": "2024-08-12", "categories": []}
{"id": "2211.13220", "title": "TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape Generation", "abstract": "  Probabilistic denoising diffusion models (DDMs) have set a new standard for\n2D image generation. Extending DDMs for 3D content creation is an active field\nof research. Here, we propose TetraDiffusion, a diffusion model that operates\non a tetrahedral partitioning of 3D space to enable efficient, high-resolution\n3D shape generation. Our model introduces operators for convolution and\ntranspose convolution that act directly on the tetrahedral partition, and\nseamlessly includes additional attributes such as color. Remarkably,\nTetraDiffusion enables rapid sampling of detailed 3D objects in nearly\nreal-time with unprecedented resolution. It's also adaptable for generating 3D\nshapes conditioned on 2D images. Compared to existing 3D mesh diffusion\ntechniques, our method is up to 200 times faster in inference speed, works on\nstandard consumer hardware, and delivers superior results.\n", "date": "2022-11-23", "categories": []}
{"id": "2312.00588", "title": "LucidDreaming: Controllable Object-Centric 3D Generation", "abstract": "  With the recent development of generative models, Text-to-3D generations have\nalso seen significant growth, opening a door for creating video-game 3D assets\nfrom a more general public. Nonetheless, people without any professional 3D\nediting experience would find it hard to achieve precise control over the 3D\ngeneration, especially if there are multiple objects in the prompt, as using\ntext to control often leads to missing objects and imprecise locations. In this\npaper, we present LucidDreaming as an effective pipeline capable of spatial and\nnumerical control over 3D generation from only textual prompt commands or 3D\nbounding boxes. Specifically, our research demonstrates that Large Language\nModels (LLMs) possess 3D spatial awareness and can effectively translate\ntextual 3D information into precise 3D bounding boxes. We leverage LLMs to get\nindividual object information and their 3D bounding boxes as the initial step\nof our process. Then with the bounding boxes, We further propose clipped ray\nsampling and object-centric density blob bias to generate 3D objects aligning\nwith the bounding boxes. We show that our method exhibits remarkable\nadaptability across a spectrum of mainstream Score Distillation Sampling-based\n3D generation frameworks and our pipeline can even used to insert objects into\nan existing NeRF scene. Moreover, we also provide a dataset of prompts with 3D\nbounding boxes, benchmarking 3D spatial controllability. With extensive\nqualitative and quantitative experiments, we demonstrate that LucidDreaming\nachieves superior results in object placement precision and generation fidelity\ncompared to current approaches, while maintaining flexibility and ease of use\nfor non-expert users.\n", "date": "2023-11-30", "categories": []}
{"id": "2408.03178", "title": "An Object is Worth 64x64 Pixels: Generating 3D Object via Image\n  Diffusion", "abstract": "  We introduce a new approach for generating realistic 3D models with UV maps\nthrough a representation termed \"Object Images.\" This approach encapsulates\nsurface geometry, appearance, and patch structures within a 64x64 pixel image,\neffectively converting complex 3D shapes into a more manageable 2D format. By\ndoing so, we address the challenges of both geometric and semantic irregularity\ninherent in polygonal meshes. This method allows us to use image generation\nmodels, such as Diffusion Transformers, directly for 3D shape generation.\nEvaluated on the ABO dataset, our generated shapes with patch structures\nachieve point cloud FID comparable to recent 3D generative models, while\nnaturally supporting PBR material generation.\n", "date": "2024-08-06", "categories": []}
{"id": "2312.14124", "title": "Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance\n  Generation", "abstract": "  Controllable generation of 3D assets is important for many practical\napplications like content creation in movies, games and engineering, as well as\nin AR/VR. Recently, diffusion models have shown remarkable results in\ngeneration quality of 3D objects. However, none of the existing models enable\ndisentangled generation to control the shape and appearance separately. For the\nfirst time, we present a suitable representation for 3D diffusion models to\nenable such disentanglement by introducing a hybrid point cloud and neural\nradiance field approach. We model a diffusion process over point positions\njointly with a high-dimensional feature space for a local density and radiance\ndecoder. While the point positions represent the coarse shape of the object,\nthe point features allow modeling the geometry and appearance details. This\ndisentanglement enables us to sample both independently and therefore to\ncontrol both separately. Our approach sets a new state of the art in generation\ncompared to previous disentanglement-capable methods by reduced FID scores of\n30-90% and is on-par with other non disentanglement-capable state-of-the art\nmethods.\n", "date": "2023-12-21", "categories": []}
{"id": "2407.21428", "title": "Deformable 3D Shape Diffusion Model", "abstract": "  The Gaussian diffusion model, initially designed for image generation, has\nrecently been adapted for 3D point cloud generation. However, these adaptations\nhave not fully considered the intrinsic geometric characteristics of 3D shapes,\nthereby constraining the diffusion model's potential for 3D shape manipulation.\nTo address this limitation, we introduce a novel deformable 3D shape diffusion\nmodel that facilitates comprehensive 3D shape manipulation, including point\ncloud generation, mesh deformation, and facial animation. Our approach\ninnovatively incorporates a differential deformation kernel, which deconstructs\nthe generation of geometric structures into successive non-rigid deformation\nstages. By leveraging a probabilistic diffusion model to simulate this\nstep-by-step process, our method provides a versatile and efficient solution\nfor a wide range of applications, spanning from graphics rendering to facial\nexpression animation. Empirical evidence highlights the effectiveness of our\napproach, demonstrating state-of-the-art performance in point cloud generation\nand competitive results in mesh deformation. Additionally, extensive visual\ndemonstrations reveal the significant potential of our approach for practical\napplications. Our method presents a unique pathway for advancing 3D shape\nmanipulation and unlocking new opportunities in the realm of virtual reality.\n", "date": "2024-07-31", "categories": []}
{"id": "2403.01807", "title": "ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models", "abstract": "  3D asset generation is getting massive amounts of attention, inspired by the\nrecent success of text-guided 2D content creation. Existing text-to-3D methods\nuse pretrained text-to-image diffusion models in an optimization problem or\nfine-tune them on synthetic data, which often results in non-photorealistic 3D\nobjects without backgrounds. In this paper, we present a method that leverages\npretrained text-to-image models as a prior, and learn to generate multi-view\nimages in a single denoising process from real-world data. Concretely, we\npropose to integrate 3D volume-rendering and cross-frame-attention layers into\neach block of the existing U-Net network of the text-to-image model. Moreover,\nwe design an autoregressive generation that renders more 3D-consistent images\nat any viewpoint. We train our model on real-world datasets of objects and\nshowcase its capabilities to generate instances with a variety of high-quality\nshapes and textures in authentic surroundings. Compared to the existing\nmethods, the results generated by our method are consistent, and have favorable\nvisual quality (-30% FID, -37% KID).\n", "date": "2024-03-04", "categories": []}
{"id": "2405.10988", "title": "Flow Score Distillation for Diverse Text-to-3D Generation", "abstract": "  Recent advancements in Text-to-3D generation have yielded remarkable\nprogress, particularly through methods that rely on Score Distillation Sampling\n(SDS). While SDS exhibits the capability to create impressive 3D assets, it is\nhindered by its inherent maximum-likelihood-seeking essence, resulting in\nlimited diversity in generation outcomes. In this paper, we discover that the\nDenoise Diffusion Implicit Models (DDIM) generation process (\\ie PF-ODE) can be\nsuccinctly expressed using an analogue of SDS loss. One step further, one can\nsee SDS as a generalized DDIM generation process. Following this insight, we\nshow that the noise sampling strategy in the noise addition stage significantly\nrestricts the diversity of generation results. To address this limitation, we\npresent an innovative noise sampling approach and introduce a novel text-to-3D\nmethod called Flow Score Distillation (FSD). Our validation experiments across\nvarious text-to-image Diffusion Models demonstrate that FSD substantially\nenhances generation diversity without compromising quality.\n", "date": "2024-05-16", "categories": []}
{"id": "2407.19035", "title": "ScalingGaussian: Enhancing 3D Content Creation with Generative Gaussian\n  Splatting", "abstract": "  The creation of high-quality 3D assets is paramount for applications in\ndigital heritage preservation, entertainment, and robotics. Traditionally, this\nprocess necessitates skilled professionals and specialized software for the\nmodeling, texturing, and rendering of 3D objects. However, the rising demand\nfor 3D assets in gaming and virtual reality (VR) has led to the creation of\naccessible image-to-3D technologies, allowing non-professionals to produce 3D\ncontent and decreasing dependence on expert input. Existing methods for 3D\ncontent generation struggle to simultaneously achieve detailed textures and\nstrong geometric consistency. We introduce a novel 3D content creation\nframework, ScalingGaussian, which combines 3D and 2D diffusion models to\nachieve detailed textures and geometric consistency in generated 3D assets.\nInitially, a 3D diffusion model generates point clouds, which are then\ndensified through a process of selecting local regions, introducing Gaussian\nnoise, followed by using local density-weighted selection. To refine the 3D\ngaussians, we utilize a 2D diffusion model with Score Distillation Sampling\n(SDS) loss, guiding the 3D Gaussians to clone and split. Finally, the 3D\nGaussians are converted into meshes, and the surface textures are optimized\nusing Mean Square Error(MSE) and Gradient Profile Prior(GPP) losses. Our method\naddresses the common issue of sparse point clouds in 3D diffusion, resulting in\nimproved geometric structure and detailed textures. Experiments on image-to-3D\ntasks demonstrate that our approach efficiently generates high-quality 3D\nassets.\n", "date": "2024-07-26", "categories": []}
{"id": "2407.19225", "title": "Magic3DSketch: Create Colorful 3D Models From Sketch-Based 3D Modeling\n  Guided by Text and Language-Image Pre-Training", "abstract": "  The requirement for 3D content is growing as AR/VR application emerges. At\nthe same time, 3D modelling is only available for skillful experts, because\ntraditional methods like Computer-Aided Design (CAD) are often too\nlabor-intensive and skill-demanding, making it challenging for novice users.\nOur proposed method, Magic3DSketch, employs a novel technique that encodes\nsketches to predict a 3D mesh, guided by text descriptions and leveraging\nexternal prior knowledge obtained through text and language-image pre-training.\nThe integration of language-image pre-trained neural networks complements the\nsparse and ambiguous nature of single-view sketch inputs. Our method is also\nmore useful and offers higher degree of controllability compared to existing\ntext-to-3D approaches, according to our user study. Moreover, Magic3DSketch\nachieves state-of-the-art performance in both synthetic and real dataset with\nthe capability of producing more detailed structures and realistic shapes with\nthe help of text input. Users are also more satisfied with models obtained by\nMagic3DSketch according to our user study. Additionally, we are also the first,\nto our knowledge, add color based on text description to the sketch-derived\nshapes. By combining sketches and text guidance with the help of language-image\npretrained models, our Magic3DSketch can allow novice users to create custom 3D\nmodels with minimal effort and maximum creative freedom, with the potential to\nrevolutionize future 3D modeling pipelines.\n", "date": "2024-07-27", "categories": []}
{"id": "2407.19548", "title": "Cycle3D: High-quality and Consistent Image-to-3D Generation via\n  Generation-Reconstruction Cycle", "abstract": "  Recent 3D large reconstruction models typically employ a two-stage process,\nincluding first generate multi-view images by a multi-view diffusion model, and\nthen utilize a feed-forward model to reconstruct images to 3D content.However,\nmulti-view diffusion models often produce low-quality and inconsistent images,\nadversely affecting the quality of the final 3D reconstruction. To address this\nissue, we propose a unified 3D generation framework called Cycle3D, which\ncyclically utilizes a 2D diffusion-based generation module and a feed-forward\n3D reconstruction module during the multi-step diffusion process. Concretely,\n2D diffusion model is applied for generating high-quality texture, and the\nreconstruction model guarantees multi-view consistency.Moreover, 2D diffusion\nmodel can further control the generated content and inject reference-view\ninformation for unseen views, thereby enhancing the diversity and texture\nconsistency of 3D generation during the denoising process. Extensive\nexperiments demonstrate the superior ability of our method to create 3D content\nwith high-quality and consistency compared with state-of-the-art baselines.\n", "date": "2024-07-28", "categories": []}
{"id": "2311.17050", "title": "Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using\n  Diffusion Models", "abstract": "  We present Surf-D, a novel method for generating high-quality 3D shapes as\nSurfaces with arbitrary topologies using Diffusion models. Previous methods\nexplored shape generation with different representations and they suffer from\nlimited topologies and poor geometry details. To generate high-quality surfaces\nof arbitrary topologies, we use the Unsigned Distance Field (UDF) as our\nsurface representation to accommodate arbitrary topologies. Furthermore, we\npropose a new pipeline that employs a point-based AutoEncoder to learn a\ncompact and continuous latent space for accurately encoding UDF and support\nhigh-resolution mesh extraction. We further show that our new pipeline\nsignificantly outperforms the prior approaches to learning the distance fields,\nsuch as the grid-based AutoEncoder, which is not scalable and incapable of\nlearning accurate UDF. In addition, we adopt a curriculum learning strategy to\nefficiently embed various surfaces. With the pretrained shape latent space, we\nemploy a latent diffusion model to acquire the distribution of various shapes.\nExtensive experiments are presented on using Surf-D for unconditional\ngeneration, category conditional generation, image conditional generation, and\ntext-to-shape tasks. The experiments demonstrate the superior performance of\nSurf-D in shape generation across multiple modalities as conditions. Visit our\nproject page at https://yzmblog.github.io/projects/SurfD/.\n", "date": "2023-11-28", "categories": []}
{"id": "2407.16260", "title": "DreamDissector: Learning Disentangled Text-to-3D Generation from 2D\n  Diffusion Priors", "abstract": "  Text-to-3D generation has recently seen significant progress. To enhance its\npracticality in real-world applications, it is crucial to generate multiple\nindependent objects with interactions, similar to layer-compositing in 2D image\nediting. However, existing text-to-3D methods struggle with this task, as they\nare designed to generate either non-independent objects or independent objects\nlacking spatially plausible interactions. Addressing this, we propose\nDreamDissector, a text-to-3D method capable of generating multiple independent\nobjects with interactions. DreamDissector accepts a multi-object text-to-3D\nNeRF as input and produces independent textured meshes. To achieve this, we\nintroduce the Neural Category Field (NeCF) for disentangling the input NeRF.\nAdditionally, we present the Category Score Distillation Sampling (CSDS),\nfacilitated by a Deep Concept Mining (DCM) module, to tackle the concept gap\nissue in diffusion models. By leveraging NeCF and CSDS, we can effectively\nderive sub-NeRFs from the original scene. Further refinement enhances geometry\nand texture. Our experimental results validate the effectiveness of\nDreamDissector, providing users with novel means to control 3D synthesis at the\nobject level and potentially opening avenues for various creative applications\nin the future.\n", "date": "2024-07-23", "categories": []}
{"id": "2312.05804", "title": "Layered 3D Human Generation via Semantic-Aware Diffusion Model", "abstract": "  The generation of 3D clothed humans has attracted increasing attention in\nrecent years. However, existing work cannot generate layered high-quality 3D\nhumans with consistent body structures. As a result, these methods are unable\nto arbitrarily and separately change and edit the body and clothing of the\nhuman. In this paper, we propose a text-driven layered 3D human generation\nframework based on a novel physically-decoupled semantic-aware diffusion model.\nTo keep the generated clothing consistent with the target text, we propose a\nsemantic-confidence strategy for clothing that can eliminate the non-clothing\ncontent generated by the model. To match the clothing with different body\nshapes, we propose a SMPL-driven implicit field deformation network that\nenables the free transfer and reuse of clothing. Besides, we introduce uniform\nshape priors based on the SMPL model for body and clothing, respectively, which\ngenerates more diverse 3D content without being constrained by specific\ntemplates. The experimental results demonstrate that the proposed method not\nonly generates 3D humans with consistent body structures but also allows free\nediting in a layered manner. The source code will be made public.\n", "date": "2023-12-10", "categories": []}
{"id": "2403.12034", "title": "VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion\n  Models", "abstract": "  This paper presents a novel method for building scalable 3D generative models\nutilizing pre-trained video diffusion models. The primary obstacle in\ndeveloping foundation 3D generative models is the limited availability of 3D\ndata. Unlike images, texts, or videos, 3D data are not readily accessible and\nare difficult to acquire. This results in a significant disparity in scale\ncompared to the vast quantities of other types of data. To address this issue,\nwe propose using a video diffusion model, trained with extensive volumes of\ntext, images, and videos, as a knowledge source for 3D data. By unlocking its\nmulti-view generative capabilities through fine-tuning, we generate a\nlarge-scale synthetic multi-view dataset to train a feed-forward 3D generative\nmodel. The proposed model, VFusion3D, trained on nearly 3M synthetic multi-view\ndata, can generate a 3D asset from a single image in seconds and achieves\nsuperior performance when compared to current SOTA feed-forward 3D generative\nmodels, with users preferring our results over 90% of the time.\n", "date": "2024-03-18", "categories": []}
{"id": "2407.13976", "title": "PlacidDreamer: Advancing Harmony in Text-to-3D Generation", "abstract": "  Recently, text-to-3D generation has attracted significant attention,\nresulting in notable performance enhancements. Previous methods utilize\nend-to-end 3D generation models to initialize 3D Gaussians, multi-view\ndiffusion models to enforce multi-view consistency, and text-to-image diffusion\nmodels to refine details with score distillation algorithms. However, these\nmethods exhibit two limitations. Firstly, they encounter conflicts in\ngeneration directions since different models aim to produce diverse 3D assets.\nSecondly, the issue of over-saturation in score distillation has not been\nthoroughly investigated and solved. To address these limitations, we propose\nPlacidDreamer, a text-to-3D framework that harmonizes initialization,\nmulti-view generation, and text-conditioned generation with a single multi-view\ndiffusion model, while simultaneously employing a novel score distillation\nalgorithm to achieve balanced saturation. To unify the generation direction, we\nintroduce the Latent-Plane module, a training-friendly plug-in extension that\nenables multi-view diffusion models to provide fast geometry reconstruction for\ninitialization and enhanced multi-view images to personalize the text-to-image\ndiffusion model. To address the over-saturation problem, we propose to view\nscore distillation as a multi-objective optimization problem and introduce the\nBalanced Score Distillation algorithm, which offers a Pareto Optimal solution\nthat achieves both rich details and balanced saturation. Extensive experiments\nvalidate the outstanding capabilities of our PlacidDreamer. The code is\navailable at \\url{https://github.com/HansenHuang0823/PlacidDreamer}.\n", "date": "2024-07-19", "categories": []}
{"id": "2305.00510", "title": "Towards AI-Architecture Liberty: A Comprehensive Survey on Design and\n  Generation of Virtual Architecture by Deep Learning", "abstract": "  3D shape generation techniques leveraging deep learning have garnered\nsignificant interest from both the computer vision and architectural design\ncommunities, promising to enrich the content in the virtual environment.\nHowever, research on virtual architectural design remains limited, particularly\nregarding designer-AI collaboration and deep learning-assisted design. In our\nsurvey, we reviewed 149 related articles (81.2% of articles published between\n2019 and 2023) covering architectural design, 3D shape techniques, and virtual\nenvironments. Through scrutinizing the literature, we first identify the\nprinciples of virtual architecture and illuminate its current production\nchallenges, including datasets, multimodality, design intuition, and generative\nframeworks. We then introduce the latest approaches to designing and generating\nvirtual buildings leveraging 3D shape generation and summarize four\ncharacteristics of various approaches to virtual architecture. Based on our\nanalysis, we expound on four research agendas, including agency, communication,\nuser consideration, and integrating tools. Additionally, we highlight four\nimportant enablers of ubiquitous interaction with immersive systems in deep\nlearning-assisted architectural generation. Our work contributes to fostering\nunderstanding between designers and deep learning techniques, broadening access\nto designer-AI collaboration. We advocate for interdisciplinary efforts to\naddress this timely research topic, facilitating content designing and\ngeneration in the virtual environment.\n", "date": "2023-04-30", "categories": []}
{"id": "2407.13677", "title": "PASTA: Controllable Part-Aware Shape Generation with Autoregressive\n  Transformers", "abstract": "  The increased demand for tools that automate the 3D content creation process\nled to tremendous progress in deep generative models that can generate diverse\n3D objects of high fidelity. In this paper, we present PASTA, an autoregressive\ntransformer architecture for generating high quality 3D shapes. PASTA comprises\ntwo main components: An autoregressive transformer that generates objects as a\nsequence of cuboidal primitives and a blending network, implemented with a\ntransformer decoder that composes the sequences of cuboids and synthesizes high\nquality meshes for each object. Our model is trained in two stages: First we\ntrain our autoregressive generative model using only annotated cuboidal parts\nas supervision and next, we train our blending network using explicit 3D\nsupervision, in the form of watertight meshes. Evaluations on various ShapeNet\nobjects showcase the ability of our model to perform shape generation from\ndiverse inputs \\eg from scratch, from a partial object, from text and images,\nas well size-guided generation, by explicitly conditioning on a bounding box\nthat defines the object's boundaries. Moreover, as our model considers the\nunderlying part-based structure of a 3D object, we are able to select a\nspecific part and produce shapes with meaningful variations of this part. As\nevidenced by our experiments, our model generates 3D shapes that are both more\nrealistic and diverse than existing part-based and non part-based methods,\nwhile at the same time is simpler to implement and train.\n", "date": "2024-07-18", "categories": []}
{"id": "2312.02192", "title": "DiverseDream: Diverse Text-to-3D Synthesis with Augmented Text Embedding", "abstract": "  Text-to-3D synthesis has recently emerged as a new approach to sampling 3D\nmodels by adopting pretrained text-to-image models as guiding visual priors. An\nintriguing but underexplored problem with existing text-to-3D methods is that\n3D models obtained from the sampling-by-optimization procedure tend to have\nmode collapses, and hence poor diversity in their results. In this paper, we\nprovide an analysis and identify potential causes of such a limited diversity,\nwhich motivates us to devise a new method that considers the joint generation\nof different 3D models from the same text prompt. We propose to use augmented\ntext prompts via textual inversion of reference images to diversify the joint\ngeneration. We show that our method leads to improved diversity in text-to-3D\nsynthesis qualitatively and quantitatively. Project page:\nhttps://diversedream.github.io\n", "date": "2023-12-02", "categories": []}
{"id": "2407.09822", "title": "VividDreamer: Invariant Score Distillation For Hyper-Realistic\n  Text-to-3D Generation", "abstract": "  This paper presents Invariant Score Distillation (ISD), a novel method for\nhigh-fidelity text-to-3D generation. ISD aims to tackle the over-saturation and\nover-smoothing problems in Score Distillation Sampling (SDS). In this paper,\nSDS is decoupled into a weighted sum of two components: the reconstruction term\nand the classifier-free guidance term. We experimentally found that\nover-saturation stems from the large classifier-free guidance scale and\nover-smoothing comes from the reconstruction term. To overcome these problems,\nISD utilizes an invariant score term derived from DDIM sampling to replace the\nreconstruction term in SDS. This operation allows the utilization of a medium\nclassifier-free guidance scale and mitigates the reconstruction-related errors,\nthus preventing the over-smoothing and over-saturation of results. Extensive\nexperiments demonstrate that our method greatly enhances SDS and produces\nrealistic 3D objects through single-stage optimization.\n", "date": "2024-07-13", "categories": []}
{"id": "2407.12267", "title": "Generating 3D House Wireframes with Semantics", "abstract": "  We present a new approach for generating 3D house wireframes with semantic\nenrichment using an autoregressive model. Unlike conventional generative models\nthat independently process vertices, edges, and faces, our approach employs a\nunified wire-based representation for improved coherence in learning 3D\nwireframe structures. By re-ordering wire sequences based on semantic meanings,\nwe facilitate seamless semantic integration during sequence generation. Our\ntwo-phase technique merges a graph-based autoencoder with a transformer-based\ndecoder to learn latent geometric tokens and generate semantic-aware\nwireframes. Through iterative prediction and decoding during inference, our\nmodel produces detailed wireframes that can be easily segmented into distinct\ncomponents, such as walls, roofs, and rooms, reflecting the semantic essence of\nthe shape. Empirical results on a comprehensive house dataset validate the\nsuperior accuracy, novelty, and semantic fidelity of our model compared to\nexisting generative models. More results and details can be found on\nhttps://vcc.tech/research/2024/3DWire.\n", "date": "2024-07-17", "categories": []}
{"id": "2407.12739", "title": "GroundUp: Rapid Sketch-Based 3D City Massing", "abstract": "  We propose GroundUp, the first sketch-based ideation tool for 3D city massing\nof urban areas. We focus on early-stage urban design, where sketching is a\ncommon tool and the design starts from balancing building volumes (masses) and\nopen spaces. With Human-Centered AI in mind, we aim to help architects quickly\nrevise their ideas by easily switching between 2D sketches and 3D models,\nallowing for smoother iteration and sharing of ideas. Inspired by feedback from\narchitects and existing workflows, our system takes as a first input a user\nsketch of multiple buildings in a top-down view. The user then draws a\nperspective sketch of the envisioned site. Our method is designed to exploit\nthe complementarity of information in the two sketches and allows users to\nquickly preview and adjust the inferred 3D shapes. Our model has two main\ncomponents. First, we propose a novel sketch-to-depth prediction network for\nperspective sketches that exploits top-down sketch shapes. Second, we use depth\ncues derived from the perspective sketch as a condition to our diffusion model,\nwhich ultimately completes the geometry in a top-down view. Thus, our final 3D\ngeometry is represented as a heightfield, allowing users to construct the city\n`from the ground up'.\n", "date": "2024-07-17", "categories": []}
{"id": "2403.12957", "title": "GVGEN: Text-to-3D Generation with Volumetric Representation", "abstract": "  In recent years, 3D Gaussian splatting has emerged as a powerful technique\nfor 3D reconstruction and generation, known for its fast and high-quality\nrendering capabilities. To address these shortcomings, this paper introduces a\nnovel diffusion-based framework, GVGEN, designed to efficiently generate 3D\nGaussian representations from text input. We propose two innovative\ntechniques:(1) Structured Volumetric Representation. We first arrange\ndisorganized 3D Gaussian points as a structured form GaussianVolume. This\ntransformation allows the capture of intricate texture details within a volume\ncomposed of a fixed number of Gaussians. To better optimize the representation\nof these details, we propose a unique pruning and densifying method named the\nCandidate Pool Strategy, enhancing detail fidelity through selective\noptimization. (2) Coarse-to-fine Generation Pipeline. To simplify the\ngeneration of GaussianVolume and empower the model to generate instances with\ndetailed 3D geometry, we propose a coarse-to-fine pipeline. It initially\nconstructs a basic geometric structure, followed by the prediction of complete\nGaussian attributes. Our framework, GVGEN, demonstrates superior performance in\nqualitative and quantitative assessments compared to existing 3D generation\nmethods. Simultaneously, it maintains a fast generation speed ($\\sim$7\nseconds), effectively striking a balance between quality and efficiency. Our\nproject page is: https://gvgen.github.io/\n", "date": "2024-03-19", "categories": []}
{"id": "2312.08754", "title": "UniDream: Unifying Diffusion Priors for Relightable Text-to-3D\n  Generation", "abstract": "  Recent advancements in text-to-3D generation technology have significantly\nadvanced the conversion of textual descriptions into imaginative\nwell-geometrical and finely textured 3D objects. Despite these developments, a\nprevalent limitation arises from the use of RGB data in diffusion or\nreconstruction models, which often results in models with inherent lighting and\nshadows effects that detract from their realism, thereby limiting their\nusability in applications that demand accurate relighting capabilities. To\nbridge this gap, we present UniDream, a text-to-3D generation framework by\nincorporating unified diffusion priors. Our approach consists of three main\ncomponents: (1) a dual-phase training process to get albedo-normal aligned\nmulti-view diffusion and reconstruction models, (2) a progressive generation\nprocedure for geometry and albedo-textures based on Score Distillation Sample\n(SDS) using the trained reconstruction and diffusion models, and (3) an\ninnovative application of SDS for finalizing PBR generation while keeping a\nfixed albedo based on Stable Diffusion model. Extensive evaluations demonstrate\nthat UniDream surpasses existing methods in generating 3D objects with clearer\nalbedo textures, smoother surfaces, enhanced realism, and superior relighting\ncapabilities.\n", "date": "2023-12-14", "categories": []}
{"id": "2403.17541", "title": "WordRobe: Text-Guided Generation of Textured 3D Garments", "abstract": "  In this paper, we tackle a new and challenging problem of text-driven\ngeneration of 3D garments with high-quality textures. We propose \"WordRobe\", a\nnovel framework for the generation of unposed & textured 3D garment meshes from\nuser-friendly text prompts. We achieve this by first learning a latent\nrepresentation of 3D garments using a novel coarse-to-fine training strategy\nand a loss for latent disentanglement, promoting better latent interpolation.\nSubsequently, we align the garment latent space to the CLIP embedding space in\na weakly supervised manner, enabling text-driven 3D garment generation and\nediting. For appearance modeling, we leverage the zero-shot generation\ncapability of ControlNet to synthesize view-consistent texture maps in a single\nfeed-forward inference step, thereby drastically decreasing the generation time\nas compared to existing methods. We demonstrate superior performance over\ncurrent SOTAs for learning 3D garment latent space, garment interpolation, and\ntext-driven texture synthesis, supported by quantitative evaluation and\nqualitative user study. The unposed 3D garment meshes generated using WordRobe\ncan be directly fed to standard cloth simulation & animation pipelines without\nany post-processing.\n", "date": "2024-03-26", "categories": []}
{"id": "2310.06744", "title": "HiFi-123: Towards High-fidelity One Image to 3D Content Generation", "abstract": "  Recent advances in diffusion models have enabled 3D generation from a single\nimage. However, current methods often produce suboptimal results for novel\nviews, with blurred textures and deviations from the reference image, limiting\ntheir practical applications. In this paper, we introduce HiFi-123, a method\ndesigned for high-fidelity and multi-view consistent 3D generation. Our\ncontributions are twofold: First, we propose a Reference-Guided Novel View\nEnhancement (RGNV) technique that significantly improves the fidelity of\ndiffusion-based zero-shot novel view synthesis methods. Second, capitalizing on\nthe RGNV, we present a novel Reference-Guided State Distillation (RGSD) loss.\nWhen incorporated into the optimization-based image-to-3D pipeline, our method\nsignificantly improves 3D generation quality, achieving state-of-the-art\nperformance. Comprehensive evaluations demonstrate the effectiveness of our\napproach over existing methods, both qualitatively and quantitatively. Video\nresults are available on the project page.\n", "date": "2023-10-10", "categories": []}
{"id": "2311.12090", "title": "FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud\n  Generation", "abstract": "  We propose FrePolad: frequency-rectified point latent diffusion, a point\ncloud generation pipeline integrating a variational autoencoder (VAE) with a\ndenoising diffusion probabilistic model (DDPM) for the latent distribution.\nFrePolad simultaneously achieves high quality, diversity, and flexibility in\npoint cloud cardinality for generation tasks while maintaining high\ncomputational efficiency. The improvement in generation quality and diversity\nis achieved through (1) a novel frequency rectification via spherical harmonics\ndesigned to retain high-frequency content while learning the point cloud\ndistribution; and (2) a latent DDPM to learn the regularized yet complex latent\ndistribution. In addition, FrePolad supports variable point cloud cardinality\nby formulating the sampling of points as conditional distributions over a\nlatent shape distribution. Finally, the low-dimensional latent space encoded by\nthe VAE contributes to FrePolad's fast and scalable sampling. Our quantitative\nand qualitative results demonstrate FrePolad's state-of-the-art performance in\nterms of quality, diversity, and computational efficiency. Project page:\nhttps://chenliang-zhou.github.io/FrePolad/.\n", "date": "2023-11-20", "categories": []}
{"id": "2403.18241", "title": "NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion,\n  Reconstruction, and Generation", "abstract": "  3D shape generation aims to produce innovative 3D content adhering to\nspecific conditions and constraints. Existing methods often decompose 3D shapes\ninto a sequence of localized components, treating each element in isolation\nwithout considering spatial consistency. As a result, these approaches exhibit\nlimited versatility in 3D data representation and shape generation, hindering\ntheir ability to generate highly diverse 3D shapes that comply with the\nspecified constraints. In this paper, we introduce a novel spatial-aware 3D\nshape generation framework that leverages 2D plane representations for enhanced\n3D shape modeling. To ensure spatial coherence and reduce memory usage, we\nincorporate a hybrid shape representation technique that directly learns a\ncontinuous signed distance field representation of the 3D shape using\northogonal 2D planes. Additionally, we meticulously enforce spatial\ncorrespondences across distinct planes using a transformer-based autoencoder\nstructure, promoting the preservation of spatial relationships in the generated\n3D shapes. This yields an algorithm that consistently outperforms\nstate-of-the-art 3D shape generation methods on various tasks, including\nunconditional shape generation, multi-modal shape completion, single-view\nreconstruction, and text-to-shape synthesis. Our project page is available at\nhttps://weizheliu.github.io/NeuSDFusion/ .\n", "date": "2024-03-27", "categories": []}
{"id": "2406.07516", "title": "Instant 3D Human Avatar Generation using Image Diffusion Models", "abstract": "  We present AvatarPopUp, a method for fast, high quality 3D human avatar\ngeneration from different input modalities, such as images and text prompts and\nwith control over the generated pose and shape. The common theme is the use of\ndiffusion-based image generation networks that are specialized for each\nparticular task, followed by a 3D lifting network. We purposefully decouple the\ngeneration from the 3D modeling which allow us to leverage powerful image\nsynthesis priors, trained on billions of text-image pairs. We fine-tune latent\ndiffusion networks with additional image conditioning for image generation and\nback-view prediction, and to support qualitatively different multiple 3D\nhypotheses. Our partial fine-tuning approach allows to adapt the networks for\neach task without inducing catastrophic forgetting. In our experiments, we\ndemonstrate that our method produces accurate, high-quality 3D avatars with\ndiverse appearance that respect the multimodal text, image, and body control\nsignals. Our approach can produce a 3D model in as few as 2 seconds, a four\norders of magnitude speedup wrt the vast majority of existing methods, most of\nwhich solve only a subset of our tasks, and with fewer controls. AvatarPopUp\nenables applications that require the controlled 3D generation of human avatars\nat scale. The project website can be found at\nhttps://www.nikoskolot.com/avatarpopup/.\n", "date": "2024-06-11", "categories": []}
{"id": "2407.06516", "title": "VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle\n  Asset Generation in Autonomous Driving", "abstract": "  Generating 3D vehicle assets from in-the-wild observations is crucial to\nautonomous driving. Existing image-to-3D methods cannot well address this\nproblem because they learn generation merely from image RGB information without\na deeper understanding of in-the-wild vehicles (such as car models,\nmanufacturers, etc.). This leads to their poor zero-shot prediction capability\nto handle real-world observations with occlusion or tricky viewing angles. To\nsolve this problem, in this work, we propose VQA-Diff, a novel framework that\nleverages in-the-wild vehicle images to create photorealistic 3D vehicle assets\nfor autonomous driving. VQA-Diff exploits the real-world knowledge inherited\nfrom the Large Language Model in the Visual Question Answering (VQA) model for\nrobust zero-shot prediction and the rich image prior knowledge in the Diffusion\nmodel for structure and appearance generation. In particular, we utilize a\nmulti-expert Diffusion Models strategy to generate the structure information\nand employ a subject-driven structure-controlled generation mechanism to model\nappearance information. As a result, without the necessity to learn from a\nlarge-scale image-to-3D vehicle dataset collected from the real world, VQA-Diff\nstill has a robust zero-shot image-to-novel-view generation ability. We conduct\nexperiments on various datasets, including Pascal 3D+, Waymo, and Objaverse, to\ndemonstrate that VQA-Diff outperforms existing state-of-the-art methods both\nqualitatively and quantitatively.\n", "date": "2024-07-09", "categories": []}
{"id": "2407.06938", "title": "RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models", "abstract": "  We present RodinHD, which can generate high-fidelity 3D avatars from a\nportrait image. Existing methods fail to capture intricate details such as\nhairstyles which we tackle in this paper. We first identify an overlooked\nproblem of catastrophic forgetting that arises when fitting triplanes\nsequentially on many avatars, caused by the MLP decoder sharing scheme. To\novercome this issue, we raise a novel data scheduling strategy and a weight\nconsolidation regularization term, which improves the decoder's capability of\nrendering sharper details. Additionally, we optimize the guiding effect of the\nportrait image by computing a finer-grained hierarchical representation that\ncaptures rich 2D texture cues, and injecting them to the 3D diffusion model at\nmultiple layers via cross-attention. When trained on 46K avatars with a noise\nschedule optimized for triplanes, the resulting model can generate 3D avatars\nwith notably better details than previous methods and can generalize to\nin-the-wild portrait input.\n", "date": "2024-07-09", "categories": []}
{"id": "2402.17214", "title": "CharacterGen: Efficient 3D Character Generation from Single Images with\n  Multi-View Pose Canonicalization", "abstract": "  In the field of digital content creation, generating high-quality 3D\ncharacters from single images is challenging, especially given the complexities\nof various body poses and the issues of self-occlusion and pose ambiguity. In\nthis paper, we present CharacterGen, a framework developed to efficiently\ngenerate 3D characters. CharacterGen introduces a streamlined generation\npipeline along with an image-conditioned multi-view diffusion model. This model\neffectively calibrates input poses to a canonical form while retaining key\nattributes of the input image, thereby addressing the challenges posed by\ndiverse poses. A transformer-based, generalizable sparse-view reconstruction\nmodel is the other core component of our approach, facilitating the creation of\ndetailed 3D models from multi-view images. We also adopt a\ntexture-back-projection strategy to produce high-quality texture maps.\nAdditionally, we have curated a dataset of anime characters, rendered in\nmultiple poses and views, to train and evaluate our model. Our approach has\nbeen thoroughly evaluated through quantitative and qualitative experiments,\nshowing its proficiency in generating 3D characters with high-quality shapes\nand textures, ready for downstream applications such as rigging and animation.\n", "date": "2024-02-27", "categories": []}
{"id": "2407.06191", "title": "Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side\n  Images", "abstract": "  Recent advances in 3D AIGC have shown promise in directly creating 3D objects\nfrom text and images, offering significant cost savings in animation and\nproduct design. However, detailed edit and customization of 3D assets remains a\nlong-standing challenge. Specifically, 3D Generation methods lack the ability\nto follow finely detailed instructions as precisely as their 2D image creation\ncounterparts. Imagine you can get a toy through 3D AIGC but with undesired\naccessories and dressing. To tackle this challenge, we propose a novel pipeline\ncalled Tailor3D, which swiftly creates customized 3D assets from editable\ndual-side images. We aim to emulate a tailor's ability to locally change\nobjects or perform overall style transfer. Unlike creating 3D assets from\nmultiple views, using dual-side images eliminates conflicts on overlapping\nareas that occur when editing individual views. Specifically, it begins by\nediting the front view, then generates the back view of the object through\nmulti-view diffusion. Afterward, it proceeds to edit the back views. Finally, a\nDual-sided LRM is proposed to seamlessly stitch together the front and back 3D\nfeatures, akin to a tailor sewing together the front and back of a garment. The\nDual-sided LRM rectifies imperfect consistencies between the front and back\nviews, enhancing editing capabilities and reducing memory burdens while\nseamlessly integrating them into a unified 3D representation with the LoRA\nTriplane Transformer. Experimental results demonstrate Tailor3D's effectiveness\nacross various 3D generation and editing tasks, including 3D generative fill\nand style transfer. It provides a user-friendly, efficient solution for editing\n3D assets, with each editing step taking only seconds to complete.\n", "date": "2024-07-08", "categories": []}
{"id": "2407.02599", "title": "Meta 3D Gen", "abstract": "  We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for\ntext-to-3D asset generation. 3DGen offers 3D asset creation with high prompt\nfidelity and high-quality 3D shapes and textures in under a minute. It supports\nphysically-based rendering (PBR), necessary for 3D asset relighting in\nreal-world applications. Additionally, 3DGen supports generative retexturing of\npreviously generated (or artist-created) 3D shapes using additional textual\ninputs provided by the user. 3DGen integrates key technical components, Meta 3D\nAssetGen and Meta 3D TextureGen, that we developed for text-to-3D and\ntext-to-texture generation, respectively. By combining their strengths, 3DGen\nrepresents 3D objects simultaneously in three ways: in view space, in\nvolumetric space, and in UV (or texture) space. The integration of these two\ntechniques achieves a win rate of 68% with respect to the single-stage model.\nWe compare 3DGen to numerous industry baselines, and show that it outperforms\nthem in terms of prompt fidelity and visual quality for complex textual\nprompts, while being significantly faster.\n", "date": "2024-07-02", "categories": []}
{"id": "2404.01241", "title": "StructLDM: Structured Latent Diffusion for 3D Human Generation", "abstract": "  Recent 3D human generative models have achieved remarkable progress by\nlearning 3D-aware GANs from 2D images. However, existing 3D human generative\nmethods model humans in a compact 1D latent space, ignoring the articulated\nstructure and semantics of human body topology. In this paper, we explore more\nexpressive and higher-dimensional latent space for 3D human modeling and\npropose StructLDM, a diffusion-based unconditional 3D human generative model,\nwhich is learned from 2D images. StructLDM solves the challenges imposed due to\nthe high-dimensional growth of latent space with three key designs: 1) A\nsemantic structured latent space defined on the dense surface manifold of a\nstatistical human body template. 2) A structured 3D-aware auto-decoder that\nfactorizes the global latent space into several semantic body parts\nparameterized by a set of conditional structured local NeRFs anchored to the\nbody template, which embeds the properties learned from the 2D training data\nand can be decoded to render view-consistent humans under different poses and\nclothing styles. 3) A structured latent diffusion model for generative human\nappearance sampling. Extensive experiments validate StructLDM's\nstate-of-the-art generation performance and illustrate the expressiveness of\nthe structured latent space over the well-adopted 1D latent space. Notably,\nStructLDM enables different levels of controllable 3D human generation and\nediting, including pose/view/shape control, and high-level tasks including\ncompositional generations, part-aware clothing editing, 3D virtual try-on, etc.\nOur project page is at: https://taohuumd.github.io/projects/StructLDM/.\n", "date": "2024-04-01", "categories": []}
{"id": "2407.02445", "title": "Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry,\n  Texture, and PBR Materials", "abstract": "  We present Meta 3D AssetGen (AssetGen), a significant advancement in\ntext-to-3D generation which produces faithful, high-quality meshes with texture\nand material control. Compared to works that bake shading in the 3D object's\nappearance, AssetGen outputs physically-based rendering (PBR) materials,\nsupporting realistic relighting. AssetGen generates first several views of the\nobject with factored shaded and albedo appearance channels, and then\nreconstructs colours, metalness and roughness in 3D, using a deferred shading\nloss for efficient supervision. It also uses a sign-distance function to\nrepresent 3D shape more reliably and introduces a corresponding loss for direct\nshape supervision. This is implemented using fused kernels for high memory\nefficiency. After mesh extraction, a texture refinement transformer operating\nin UV space significantly improves sharpness and details. AssetGen achieves 17%\nimprovement in Chamfer Distance and 40% in LPIPS over the best concurrent work\nfor few-view reconstruction, and a human preference of 72% over the best\nindustry competitors of comparable speed, including those that support PBR.\nProject page with generated assets: https://assetgen.github.io\n", "date": "2024-07-02", "categories": []}
{"id": "2406.16695", "title": "Geometry-Aware Score Distillation via 3D Consistent Noising and Gradient\n  Consistency Modeling", "abstract": "  Score distillation sampling (SDS), the methodology in which the score from\npretrained 2D diffusion models is distilled into 3D representation, has\nrecently brought significant advancements in text-to-3D generation task.\nHowever, this approach is still confronted with critical geometric\ninconsistency problems such as the Janus problem. Starting from a hypothesis\nthat such inconsistency problems may be induced by multiview inconsistencies\nbetween 2D scores predicted from various viewpoints, we introduce GSD, a simple\nand general plug-and-play framework for incorporating 3D consistency and\ntherefore geometry awareness into the SDS process. Our methodology is composed\nof three components: 3D consistent noising, designed to produce 3D consistent\nnoise maps that perfectly follow the standard Gaussian distribution,\ngeometry-based gradient warping for identifying correspondences between\npredicted gradients of different viewpoints, and novel gradient consistency\nloss to optimize the scene geometry toward producing more consistent gradients.\nWe demonstrate that our method significantly improves performance, successfully\naddressing the geometric inconsistency problems in text-to-3D generation task\nwith minimal computation cost and being compatible with existing score\ndistillation-based models. Our project page is available at\nhttps://ku-cvlab.github.io/GSD/.\n", "date": "2024-06-24", "categories": []}
{"id": "2406.18462", "title": "GaussianDreamerPro: Text to Manipulable 3D Gaussians with Highly\n  Enhanced Quality", "abstract": "  Recently, 3D Gaussian splatting (3D-GS) has achieved great success in\nreconstructing and rendering real-world scenes. To transfer the high rendering\nquality to generation tasks, a series of research works attempt to generate\n3D-Gaussian assets from text. However, the generated assets have not achieved\nthe same quality as those in reconstruction tasks. We observe that Gaussians\ntend to grow without control as the generation process may cause indeterminacy.\nAiming at highly enhancing the generation quality, we propose a novel framework\nnamed GaussianDreamerPro. The main idea is to bind Gaussians to reasonable\ngeometry, which evolves over the whole generation process. Along different\nstages of our framework, both the geometry and appearance can be enriched\nprogressively. The final output asset is constructed with 3D Gaussians bound to\nmesh, which shows significantly enhanced details and quality compared with\nprevious methods. Notably, the generated asset can also be seamlessly\nintegrated into downstream manipulation pipelines, e.g. animation, composition,\nand simulation etc., greatly promoting its potential in wide applications.\nDemos are available at https://taoranyi.com/gaussiandreamerpro/.\n", "date": "2024-06-26", "categories": []}
{"id": "2312.03806", "title": "XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies", "abstract": "  We present XCube (abbreviated as $\\mathcal{X}^3$), a novel generative model\nfor high-resolution sparse 3D voxel grids with arbitrary attributes. Our model\ncan generate millions of voxels with a finest effective resolution of up to\n$1024^3$ in a feed-forward fashion without time-consuming test-time\noptimization. To achieve this, we employ a hierarchical voxel latent diffusion\nmodel which generates progressively higher resolution grids in a coarse-to-fine\nmanner using a custom framework built on the highly efficient VDB data\nstructure. Apart from generating high-resolution objects, we demonstrate the\neffectiveness of XCube on large outdoor scenes at scales of 100m$\\times$100m\nwith a voxel size as small as 10cm. We observe clear qualitative and\nquantitative improvements over past approaches. In addition to unconditional\ngeneration, we show that our model can be used to solve a variety of tasks such\nas user-guided editing, scene completion from a single scan, and text-to-3D.\nThe source code and more results can be found at\nhttps://research.nvidia.com/labs/toronto-ai/xcube/.\n", "date": "2023-12-06", "categories": []}
{"id": "2406.16273", "title": "YouDream: Generating Anatomically Controllable Consistent Text-to-3D\n  Animals", "abstract": "  3D generation guided by text-to-image diffusion models enables the creation\nof visually compelling assets. However previous methods explore generation\nbased on image or text. The boundaries of creativity are limited by what can be\nexpressed through words or the images that can be sourced. We present YouDream,\na method to generate high-quality anatomically controllable animals. YouDream\nis guided using a text-to-image diffusion model controlled by 2D views of a 3D\npose prior. Our method generates 3D animals that are not possible to create\nusing previous text-to-3D generative methods. Additionally, our method is\ncapable of preserving anatomic consistency in the generated animals, an area\nwhere prior text-to-3D approaches often struggle. Moreover, we design a fully\nautomated pipeline for generating commonly found animals. To circumvent the\nneed for human intervention to create a 3D pose, we propose a multi-agent LLM\nthat adapts poses from a limited library of animal 3D poses to represent the\ndesired animal. A user study conducted on the outcomes of YouDream demonstrates\nthe preference of the animal models generated by our method over others.\nTurntable results and code are released at https://youdream3d.github.io/\n", "date": "2024-06-24", "categories": []}
{"id": "2406.14964", "title": "VividDreamer: Towards High-Fidelity and Efficient Text-to-3D Generation", "abstract": "  Text-to-3D generation aims to create 3D assets from text-to-image diffusion\nmodels. However, existing methods face an inherent bottleneck in generation\nquality because the widely-used objectives such as Score Distillation Sampling\n(SDS) inappropriately omit U-Net jacobians for swift generation, leading to\nsignificant bias compared to the \"true\" gradient obtained by full denoising\nsampling. This bias brings inconsistent updating direction, resulting in\nimplausible 3D generation e.g., color deviation, Janus problem, and\nsemantically inconsistent details). In this work, we propose Pose-dependent\nConsistency Distillation Sampling (PCDS), a novel yet efficient objective for\ndiffusion-based 3D generation tasks. Specifically, PCDS builds the\npose-dependent consistency function within diffusion trajectories, allowing to\napproximate true gradients through minimal sampling steps (1-3). Compared to\nSDS, PCDS can acquire a more accurate updating direction with the same sampling\ntime (1 sampling step), while enabling few-step (2-3) sampling to trade compute\nfor higher generation quality. For efficient generation, we propose a\ncoarse-to-fine optimization strategy, which first utilizes 1-step PCDS to\ncreate the basic structure of 3D objects, and then gradually increases PCDS\nsteps to generate fine-grained details. Extensive experiments demonstrate that\nour approach outperforms the state-of-the-art in generation quality and\ntraining efficiency, conspicuously alleviating the implausible 3D generation\nissues caused by the deviated updating direction. Moreover, it can be simply\napplied to many 3D generative applications to yield impressive 3D assets,\nplease see our project page: https://narcissusex.github.io/VividDreamer.\n", "date": "2024-06-21", "categories": []}
{"id": "2406.13897", "title": "CLAY: A Controllable Large-scale Generative Model for Creating\n  High-quality 3D Assets", "abstract": "  In the realm of digital creativity, our potential to craft intricate 3D\nworlds from imagination is often hampered by the limitations of existing\ndigital tools, which demand extensive expertise and efforts. To narrow this\ndisparity, we introduce CLAY, a 3D geometry and material generator designed to\neffortlessly transform human imagination into intricate 3D digital structures.\nCLAY supports classic text or image inputs as well as 3D-aware controls from\ndiverse primitives (multi-view images, voxels, bounding boxes, point clouds,\nimplicit representations, etc). At its core is a large-scale generative model\ncomposed of a multi-resolution Variational Autoencoder (VAE) and a minimalistic\nlatent Diffusion Transformer (DiT), to extract rich 3D priors directly from a\ndiverse range of 3D geometries. Specifically, it adopts neural fields to\nrepresent continuous and complete surfaces and uses a geometry generative\nmodule with pure transformer blocks in latent space. We present a progressive\ntraining scheme to train CLAY on an ultra large 3D model dataset obtained\nthrough a carefully designed processing pipeline, resulting in a 3D native\ngeometry generator with 1.5 billion parameters. For appearance generation, CLAY\nsets out to produce physically-based rendering (PBR) textures by employing a\nmulti-view material diffusion model that can generate 2K resolution textures\nwith diffuse, roughness, and metallic modalities. We demonstrate using CLAY for\na range of controllable 3D asset creations, from sketchy conceptual designs to\nproduction ready assets with intricate details. Even first time users can\neasily use CLAY to bring their vivid 3D imaginations to life, unleashing\nunlimited creativity.\n", "date": "2024-05-30", "categories": []}
{"id": "2405.20853", "title": "MeshXL: Neural Coordinate Field for Generative 3D Foundation Models", "abstract": "  The polygon mesh representation of 3D data exhibits great flexibility, fast\nrendering speed, and storage efficiency, which is widely preferred in various\napplications. However, given its unstructured graph representation, the direct\ngeneration of high-fidelity 3D meshes is challenging. Fortunately, with a\npre-defined ordering strategy, 3D meshes can be represented as sequences, and\nthe generation process can be seamlessly treated as an auto-regressive problem.\nIn this paper, we validate the Neural Coordinate Field (NeurCF), an explicit\ncoordinate representation with implicit neural embeddings, is a\nsimple-yet-effective representation for large-scale sequential mesh modeling.\nAfter that, we present MeshXL, a family of generative pre-trained\nauto-regressive models, which addresses the process of 3D mesh generation with\nmodern large language model approaches. Extensive experiments show that MeshXL\nis able to generate high-quality 3D meshes, and can also serve as foundation\nmodels for various down-stream applications.\n", "date": "2024-05-31", "categories": []}
{"id": "2406.04254", "title": "GeoGen: Geometry-Aware Generative Modeling via Signed Distance Functions", "abstract": "  We introduce a new generative approach for synthesizing 3D geometry and\nimages from single-view collections. Most existing approaches predict\nvolumetric density to render multi-view consistent images. By employing\nvolumetric rendering using neural radiance fields, they inherit a key\nlimitation: the generated geometry is noisy and unconstrained, limiting the\nquality and utility of the output meshes. To address this issue, we propose\nGeoGen, a new SDF-based 3D generative model trained in an end-to-end manner.\nInitially, we reinterpret the volumetric density as a Signed Distance Function\n(SDF). This allows us to introduce useful priors to generate valid meshes.\nHowever, those priors prevent the generative model from learning details,\nlimiting the applicability of the method to real-world scenarios. To alleviate\nthat problem, we make the transformation learnable and constrain the rendered\ndepth map to be consistent with the zero-level set of the SDF. Through the lens\nof adversarial training, we encourage the network to produce higher fidelity\ndetails on the output meshes. For evaluation, we introduce a synthetic dataset\nof human avatars captured from 360-degree camera angles, to overcome the\nchallenges presented by real-world datasets, which often lack 3D consistency\nand do not cover all camera angles. Our experiments on multiple datasets show\nthat GeoGen produces visually and quantitatively better geometry than the\nprevious generative models based on neural radiance fields.\n", "date": "2024-06-06", "categories": []}
{"id": "2406.09850", "title": "GradeADreamer: Enhanced Text-to-3D Generation Using Gaussian Splatting\n  and Multi-View Diffusion", "abstract": "  Text-to-3D generation has shown promising results, yet common challenges such\nas the Multi-face Janus problem and extended generation time for high-quality\nassets. In this paper, we address these issues by introducing a novel\nthree-stage training pipeline called GradeADreamer. This pipeline is capable of\nproducing high-quality assets with a total generation time of under 30 minutes\nusing only a single RTX 3090 GPU. Our proposed method employs a Multi-view\nDiffusion Model, MVDream, to generate Gaussian Splats as a prior, followed by\nrefining geometry and texture using StableDiffusion. Experimental results\ndemonstrate that our approach significantly mitigates the Multi-face Janus\nproblem and achieves the highest average user preference ranking compared to\nprevious state-of-the-art methods. The project code is available at\nhttps://github.com/trapoom555/GradeADreamer.\n", "date": "2024-06-14", "categories": []}
{"id": "2401.09050", "title": "Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation\n  with Deterministic Sampling Prior", "abstract": "  Score distillation sampling (SDS) and its variants have greatly boosted the\ndevelopment of text-to-3D generation, but are vulnerable to geometry collapse\nand poor textures yet. To solve this issue, we first deeply analyze the SDS and\nfind that its distillation sampling process indeed corresponds to the\ntrajectory sampling of a stochastic differential equation (SDE): SDS samples\nalong an SDE trajectory to yield a less noisy sample which then serves as a\nguidance to optimize a 3D model. However, the randomness in SDE sampling often\nleads to a diverse and unpredictable sample which is not always less noisy, and\nthus is not a consistently correct guidance, explaining the vulnerability of\nSDS. Since for any SDE, there always exists an ordinary differential equation\n(ODE) whose trajectory sampling can deterministically and consistently converge\nto the desired target point as the SDE, we propose a novel and effective\n\"Consistent3D\" method that explores the ODE deterministic sampling prior for\ntext-to-3D generation. Specifically, at each training iteration, given a\nrendered image by a 3D model, we first estimate its desired 3D score function\nby a pre-trained 2D diffusion model, and build an ODE for trajectory sampling.\nNext, we design a consistency distillation sampling loss which samples along\nthe ODE trajectory to generate two adjacent samples and uses the less noisy\nsample to guide another more noisy one for distilling the deterministic prior\ninto the 3D model. Experimental results show the efficacy of our Consistent3D\nin generating high-fidelity and diverse 3D objects and large-scale scenes, as\nshown in Fig. 1. The codes are available at\nhttps://github.com/sail-sg/Consistent3D.\n", "date": "2024-01-17", "categories": []}
{"id": "2406.07742", "title": "C3DAG: Controlled 3D Animal Generation using 3D pose guidance", "abstract": "  Recent advancements in text-to-3D generation have demonstrated the ability to\ngenerate high quality 3D assets. However while generating animals these methods\nunderperform, often portraying inaccurate anatomy and geometry. Towards\nameliorating this defect, we present C3DAG, a novel pose-Controlled text-to-3D\nAnimal Generation framework which generates a high quality 3D animal consistent\nwith a given pose. We also introduce an automatic 3D shape creator tool, that\nallows dynamic pose generation and modification via a web-based tool, and that\ngenerates a 3D balloon animal using simple geometries. A NeRF is then\ninitialized using this 3D shape using depth-controlled SDS. In the next stage,\nthe pre-trained NeRF is fine-tuned using quadruped-pose-controlled SDS. The\npipeline that we have developed not only produces geometrically and\nanatomically consistent results, but also renders highly controlled 3D animals,\nunlike prior methods which do not allow fine-grained pose control.\n", "date": "2024-06-11", "categories": []}
{"id": "2312.04043", "title": "Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes", "abstract": "  In this paper, we democratise 3D content creation, enabling precise\ngeneration of 3D shapes from abstract sketches while overcoming limitations\ntied to drawing skills. We introduce a novel part-level modelling and alignment\nframework that facilitates abstraction modelling and cross-modal\ncorrespondence. Leveraging the same part-level decoder, our approach seamlessly\nextends to sketch modelling by establishing correspondence between CLIPasso\nedgemaps and projected 3D part regions, eliminating the need for a dataset\npairing human sketches and 3D shapes. Additionally, our method introduces a\nseamless in-position editing process as a byproduct of cross-modal part-aligned\nmodelling. Operating in a low-dimensional implicit space, our approach\nsignificantly reduces computational demands and processing time.\n", "date": "2023-12-07", "categories": []}
{"id": "2406.04322", "title": "DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D\n  Data", "abstract": "  We present DIRECT-3D, a diffusion-based 3D generative model for creating\nhigh-quality 3D assets (represented by Neural Radiance Fields) from text\nprompts. Unlike recent 3D generative models that rely on clean and well-aligned\n3D data, limiting them to single or few-class generation, our model is directly\ntrained on extensive noisy and unaligned `in-the-wild' 3D assets, mitigating\nthe key challenge (i.e., data scarcity) in large-scale 3D generation. In\nparticular, DIRECT-3D is a tri-plane diffusion model that integrates two\ninnovations: 1) A novel learning framework where noisy data are filtered and\naligned automatically during the training process. Specifically, after an\ninitial warm-up phase using a small set of clean data, an iterative\noptimization is introduced in the diffusion process to explicitly estimate the\n3D pose of objects and select beneficial data based on conditional density. 2)\nAn efficient 3D representation that is achieved by disentangling object\ngeometry and color features with two separate conditional diffusion models that\nare optimized hierarchically. Given a prompt input, our model generates\nhigh-quality, high-resolution, realistic, and complex 3D objects with accurate\ngeometric details in seconds. We achieve state-of-the-art performance in both\nsingle-class generation and text-to-3D generation. We also demonstrate that\nDIRECT-3D can serve as a useful 3D geometric prior of objects, for example to\nalleviate the well-known Janus problem in 2D-lifting methods such as\nDreamFusion. The code and models are available for research purposes at:\nhttps://github.com/qihao067/direct3d.\n", "date": "2024-06-06", "categories": []}
{"id": "2406.05038", "title": "Efficient 3D Shape Generation via Diffusion Mamba with Bidirectional\n  SSMs", "abstract": "  Recent advancements in sequence modeling have led to the development of the\nMamba architecture, noted for its selective state space approach, offering a\npromising avenue for efficient long sequence handling. However, its application\nin 3D shape generation, particularly at high resolutions, remains\nunderexplored. Traditional diffusion transformers (DiT) with self-attention\nmechanisms, despite their potential, face scalability challenges due to the\ncubic complexity of attention operations as input length increases. This\ncomplexity becomes a significant hurdle when dealing with high-resolution voxel\nsizes. To address this challenge, we introduce a novel diffusion architecture\ntailored for 3D point clouds generation-Diffusion Mamba (DiM-3D). This\narchitecture forgoes traditional attention mechanisms, instead utilizing the\ninherent efficiency of the Mamba architecture to maintain linear complexity\nwith respect to sequence length. DiM-3D is characterized by fast inference\ntimes and substantially lower computational demands, quantified in reduced\nGflops, thereby addressing the key scalability issues of prior models. Our\nempirical results on the ShapeNet benchmark demonstrate that DiM-3D achieves\nstate-of-the-art performance in generating high-fidelity and diverse 3D shapes.\nAdditionally, DiM-3D shows superior capabilities in tasks like 3D point cloud\ncompletion. This not only proves the model's scalability but also underscores\nits efficiency in generating detailed, high-resolution voxels necessary for\nadvanced 3D shape modeling, particularly excelling in environments requiring\nhigh-resolution voxel sizes. Through these findings, we illustrate the\nexceptional scalability and efficiency of the Diffusion Mamba framework in 3D\nshape generation, setting a new standard for the field and paving the way for\nfuture explorations in high-resolution 3D modeling technologies.\n", "date": "2024-06-07", "categories": []}
{"id": "2404.14329", "title": "X-Ray: A Sequential 3D Representation For Generation", "abstract": "  We introduce X-Ray, a novel 3D sequential representation inspired by the\npenetrability of x-ray scans. X-Ray transforms a 3D object into a series of\nsurface frames at different layers, making it suitable for generating 3D models\nfrom images. Our method utilizes ray casting from the camera center to capture\ngeometric and textured details, including depth, normal, and color, across all\nintersected surfaces. This process efficiently condenses the whole 3D object\ninto a multi-frame video format, motivating the utilize of a network\narchitecture similar to those in video diffusion models. This design ensures an\nefficient 3D representation by focusing solely on surface information. Also, we\npropose a two-stage pipeline to generate 3D objects from X-Ray Diffusion Model\nand Upsampler. We demonstrate the practicality and adaptability of our X-Ray\nrepresentation by synthesizing the complete visible and hidden surfaces of a 3D\nobject from a single input image. Experimental results reveal the\nstate-of-the-art superiority of our representation in enhancing the accuracy of\n3D generation, paving the way for new 3D representation research and practical\napplications.\n", "date": "2024-04-22", "categories": []}
{"id": "2405.14832", "title": "Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion\n  Transformer", "abstract": "  Generating high-quality 3D assets from text and images has long been\nchallenging, primarily due to the absence of scalable 3D representations\ncapable of capturing intricate geometry distributions. In this work, we\nintroduce Direct3D, a native 3D generative model scalable to in-the-wild input\nimages, without requiring a multiview diffusion model or SDS optimization. Our\napproach comprises two primary components: a Direct 3D Variational Auto-Encoder\n(D3D-VAE) and a Direct 3D Diffusion Transformer (D3D-DiT). D3D-VAE efficiently\nencodes high-resolution 3D shapes into a compact and continuous latent triplane\nspace. Notably, our method directly supervises the decoded geometry using a\nsemi-continuous surface sampling strategy, diverging from previous methods\nrelying on rendered images as supervision signals. D3D-DiT models the\ndistribution of encoded 3D latents and is specifically designed to fuse\npositional information from the three feature maps of the triplane latent,\nenabling a native 3D generative model scalable to large-scale 3D datasets.\nAdditionally, we introduce an innovative image-to-3D generation pipeline\nincorporating semantic and pixel-level image conditions, allowing the model to\nproduce 3D shapes consistent with the provided conditional image input.\nExtensive experiments demonstrate the superiority of our large-scale\npre-trained Direct3D over previous image-to-3D approaches, achieving\nsignificantly better generation quality and generalization ability, thus\nestablishing a new state-of-the-art for 3D content creation. Project page:\nhttps://nju-3dv.github.io/projects/Direct3D/.\n", "date": "2024-05-23", "categories": []}
{"id": "2212.04981", "title": "LoopDraw: a Loop-Based Autoregressive Model for Shape Synthesis and\n  Editing", "abstract": "  There is no settled universal 3D representation for geometry with many\nalternatives such as point clouds, meshes, implicit functions, and voxels to\nname a few. In this work, we present a new, compelling alternative for\nrepresenting shapes using a sequence of cross-sectional closed loops. The loops\nacross all planes form an organizational hierarchy which we leverage for\nautoregressive shape synthesis and editing. Loops are a non-local description\nof the underlying shape, as simple loop manipulations (such as shifts) result\nin significant structural changes to the geometry. This is in contrast to\nmanipulating local primitives such as points in a point cloud or a triangle in\na triangle mesh. We further demonstrate that loops are intuitive and natural\nprimitive for analyzing and editing shapes, both computationally and for users.\n", "date": "2022-12-09", "categories": []}
{"id": "2405.16570", "title": "ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling", "abstract": "  We propose ID-to-3D, a method to generate identity- and text-guided 3D human\nheads with disentangled expressions, starting from even a single casually\ncaptured in-the-wild image of a subject. The foundation of our approach is\nanchored in compositionality, alongside the use of task-specific 2D diffusion\nmodels as priors for optimization. First, we extend a foundational model with a\nlightweight expression-aware and ID-aware architecture, and create 2D priors\nfor geometry and texture generation, via fine-tuning only 0.2% of its available\ntraining parameters. Then, we jointly leverage a neural parametric\nrepresentation for the expressions of each subject and a multi-stage generation\nof highly detailed geometry and albedo texture. This combination of strong face\nidentity embeddings and our neural representation enables accurate\nreconstruction of not only facial features but also accessories and hair and\ncan be meshed to provide render-ready assets for gaming and telepresence. Our\nresults achieve an unprecedented level of identity-consistent and high-quality\ntexture and geometry generation, generalizing to a ``world'' of unseen 3D\nidentities, without relying on large 3D captured datasets of human assets.\n", "date": "2024-05-26", "categories": []}
{"id": "2404.00987", "title": "FlexiDreamer: Single Image-to-3D Generation with FlexiCubes", "abstract": "  3D content generation has wide applications in various fields. One of its\ndominant paradigms is by sparse-view reconstruction using multi-view images\ngenerated by diffusion models. However, since directly reconstructing triangle\nmeshes from multi-view images is challenging, most methodologies opt to an\nimplicit representation (such as NeRF) during the sparse-view reconstruction\nand acquire the target mesh by a post-processing extraction. However, the\nimplicit representation takes extensive time to train and the post-extraction\nalso leads to undesirable visual artifacts. In this paper, we propose\nFlexiDreamer, a novel framework that directly reconstructs high-quality meshes\nfrom multi-view generated images. We utilize an advanced gradient-based mesh\noptimization, namely FlexiCubes, for multi-view mesh reconstruction, which\nenables us to generate 3D meshes in an end-to-end manner. To address the\nreconstruction artifacts owing to the inconsistencies from generated images, we\ndesign a hybrid positional encoding scheme to improve the reconstruction\ngeometry and an orientation-aware texture mapping to mitigate surface ghosting.\nTo further enhance the results, we respectively incorporate eikonal and smooth\nregularizations to reduce geometric holes and surface noise. Our approach can\ngenerate high-fidelity 3D meshes in the single image-to-3D downstream task with\napproximately 1 minute, significantly outperforming previous methods.\n", "date": "2024-04-01", "categories": []}
{"id": "2405.15914", "title": "ExactDreamer: High-Fidelity Text-to-3D Content Creation via Exact Score\n  Matching", "abstract": "  Text-to-3D content creation is a rapidly evolving research area. Given the\nscarcity of 3D data, current approaches often adapt pre-trained 2D diffusion\nmodels for 3D synthesis. Among these approaches, Score Distillation Sampling\n(SDS) has been widely adopted. However, the issue of over-smoothing poses a\nsignificant limitation on the high-fidelity generation of 3D models. To address\nthis challenge, LucidDreamer replaces the Denoising Diffusion Probabilistic\nModel (DDPM) in SDS with the Denoising Diffusion Implicit Model (DDIM) to\nconstruct Interval Score Matching (ISM). However, ISM inevitably inherits\ninconsistencies from DDIM, causing reconstruction errors during the DDIM\ninversion process. This results in poor performance in the detailed generation\nof 3D objects and loss of content. To alleviate these problems, we propose a\nnovel method named Exact Score Matching (ESM). Specifically, ESM leverages\nauxiliary variables to mathematically guarantee exact recovery in the DDIM\nreverse process. Furthermore, to effectively capture the dynamic changes of the\noriginal and auxiliary variables, the LoRA of a pre-trained diffusion model\nimplements these exact paths. Extensive experiments demonstrate the\neffectiveness of ESM in text-to-3D generation, particularly highlighting its\nsuperiority in detailed generation.\n", "date": "2024-05-24", "categories": []}
{"id": "2405.16890", "title": "PivotMesh: Generic 3D Mesh Generation via Pivot Vertices Guidance", "abstract": "  Generating compact and sharply detailed 3D meshes poses a significant\nchallenge for current 3D generative models. Different from extracting dense\nmeshes from neural representation, some recent works try to model the native\nmesh distribution (i.e., a set of triangles), which generates more compact\nresults as humans crafted. However, due to the complexity and variety of mesh\ntopology, these methods are typically limited to small datasets with specific\ncategories and are hard to extend. In this paper, we introduce a generic and\nscalable mesh generation framework PivotMesh, which makes an initial attempt to\nextend the native mesh generation to large-scale datasets. We employ a\ntransformer-based auto-encoder to encode meshes into discrete tokens and decode\nthem from face level to vertex level hierarchically. Subsequently, to model the\ncomplex typology, we first learn to generate pivot vertices as coarse mesh\nrepresentation and then generate the complete mesh tokens with the same\nauto-regressive Transformer. This reduces the difficulty compared with directly\nmodeling the mesh distribution and further improves the model controllability.\nPivotMesh demonstrates its versatility by effectively learning from both small\ndatasets like Shapenet, and large-scale datasets like Objaverse and\nObjaverse-xl. Extensive experiments indicate that PivotMesh can generate\ncompact and sharp 3D meshes across various categories, highlighting its great\npotential for native mesh modeling.\n", "date": "2024-05-27", "categories": []}
{"id": "2401.17053", "title": "BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane\n  Extrapolation", "abstract": "  We present BlockFusion, a diffusion-based model that generates 3D scenes as\nunit blocks and seamlessly incorporates new blocks to extend the scene.\nBlockFusion is trained using datasets of 3D blocks that are randomly cropped\nfrom complete 3D scene meshes. Through per-block fitting, all training blocks\nare converted into the hybrid neural fields: with a tri-plane containing the\ngeometry features, followed by a Multi-layer Perceptron (MLP) for decoding the\nsigned distance values. A variational auto-encoder is employed to compress the\ntri-planes into the latent tri-plane space, on which the denoising diffusion\nprocess is performed. Diffusion applied to the latent representations allows\nfor high-quality and diverse 3D scene generation. To expand a scene during\ngeneration, one needs only to append empty blocks to overlap with the current\nscene and extrapolate existing latent tri-planes to populate new blocks. The\nextrapolation is done by conditioning the generation process with the feature\nsamples from the overlapping tri-planes during the denoising iterations. Latent\ntri-plane extrapolation produces semantically and geometrically meaningful\ntransitions that harmoniously blend with the existing scene. A 2D layout\nconditioning mechanism is used to control the placement and arrangement of\nscene elements. Experimental results indicate that BlockFusion is capable of\ngenerating diverse, geometrically consistent and unbounded large 3D scenes with\nunprecedented high-quality shapes in both indoor and outdoor scenarios.\n", "date": "2024-01-30", "categories": []}
{"id": "2311.17082", "title": "DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling", "abstract": "  Recent methods such as Score Distillation Sampling (SDS) and Variational\nScore Distillation (VSD) using 2D diffusion models for text-to-3D generation\nhave demonstrated impressive generation quality. However, the long generation\ntime of such algorithms significantly degrades the user experience. To tackle\nthis problem, we propose DreamPropeller, a drop-in acceleration algorithm that\ncan be wrapped around any existing text-to-3D generation pipeline based on\nscore distillation. Our framework generalizes Picard iterations, a classical\nalgorithm for parallel sampling an ODE path, and can account for non-ODE paths\nsuch as momentum-based gradient updates and changes in dimensions during the\noptimization process as in many cases of 3D generation. We show that our\nalgorithm trades parallel compute for wallclock time and empirically achieves\nup to 4.7x speedup with a negligible drop in generation quality for all tested\nframeworks.\n", "date": "2023-11-28", "categories": []}
{"id": "2405.09874", "title": "Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode\n  Multi-view Latent Diffusion", "abstract": "  We present Dual3D, a novel text-to-3D generation framework that generates\nhigh-quality 3D assets from texts in only $1$ minute.The key component is a\ndual-mode multi-view latent diffusion model. Given the noisy multi-view\nlatents, the 2D mode can efficiently denoise them with a single latent\ndenoising network, while the 3D mode can generate a tri-plane neural surface\nfor consistent rendering-based denoising. Most modules for both modes are tuned\nfrom a pre-trained text-to-image latent diffusion model to circumvent the\nexpensive cost of training from scratch. To overcome the high rendering cost\nduring inference, we propose the dual-mode toggling inference strategy to use\nonly $1/10$ denoising steps with 3D mode, successfully generating a 3D asset in\njust $10$ seconds without sacrificing quality. The texture of the 3D asset can\nbe further enhanced by our efficient texture refinement process in a short\ntime. Extensive experiments demonstrate that our method delivers\nstate-of-the-art performance while significantly reducing generation time. Our\nproject page is available at https://dual3d.github.io\n", "date": "2024-05-16", "categories": []}
{"id": "2403.15383", "title": "ThemeStation: Generating Theme-Aware 3D Assets from Few Exemplars", "abstract": "  Real-world applications often require a large gallery of 3D assets that share\na consistent theme. While remarkable advances have been made in general 3D\ncontent creation from text or image, synthesizing customized 3D assets\nfollowing the shared theme of input 3D exemplars remains an open and\nchallenging problem. In this work, we present ThemeStation, a novel approach\nfor theme-aware 3D-to-3D generation. ThemeStation synthesizes customized 3D\nassets based on given few exemplars with two goals: 1) unity for generating 3D\nassets that thematically align with the given exemplars and 2) diversity for\ngenerating 3D assets with a high degree of variations. To this end, we design a\ntwo-stage framework that draws a concept image first, followed by a\nreference-informed 3D modeling stage. We propose a novel dual score\ndistillation (DSD) loss to jointly leverage priors from both the input\nexemplars and the synthesized concept image. Extensive experiments and user\nstudies confirm that ThemeStation surpasses prior works in producing diverse\ntheme-aware 3D models with impressive quality. ThemeStation also enables\nvarious applications such as controllable 3D-to-3D generation.\n", "date": "2024-03-22", "categories": []}
{"id": "2405.09431", "title": "A Survey On Text-to-3D Contents Generation In The Wild", "abstract": "  3D content creation plays a vital role in various applications, such as\ngaming, robotics simulation, and virtual reality. However, the process is\nlabor-intensive and time-consuming, requiring skilled designers to invest\nconsiderable effort in creating a single 3D asset. To address this challenge,\ntext-to-3D generation technologies have emerged as a promising solution for\nautomating 3D creation. Leveraging the success of large vision language models,\nthese techniques aim to generate 3D content based on textual descriptions.\nDespite recent advancements in this area, existing solutions still face\nsignificant limitations in terms of generation quality and efficiency. In this\nsurvey, we conduct an in-depth investigation of the latest text-to-3D creation\nmethods. We provide a comprehensive background on text-to-3D creation,\nincluding discussions on datasets employed in training and evaluation metrics\nused to assess the quality of generated 3D models. Then, we delve into the\nvarious 3D representations that serve as the foundation for the 3D generation\nprocess. Furthermore, we present a thorough comparison of the rapidly growing\nliterature on generative pipelines, categorizing them into feedforward\ngenerators, optimization-based generation, and view reconstruction approaches.\nBy examining the strengths and weaknesses of these methods, we aim to shed\nlight on their respective capabilities and limitations. Lastly, we point out\nseveral promising avenues for future research. With this survey, we hope to\ninspire researchers further to explore the potential of open-vocabulary\ntext-conditioned 3D content creation.\n", "date": "2024-05-15", "categories": []}
{"id": "2405.08054", "title": "Coin3D: Controllable and Interactive 3D Assets Generation with\n  Proxy-Guided Conditioning", "abstract": "  As humans, we aspire to create media content that is both freely willed and\nreadily controlled. Thanks to the prominent development of generative\ntechniques, we now can easily utilize 2D diffusion methods to synthesize images\ncontrolled by raw sketch or designated human poses, and even progressively\nedit/regenerate local regions with masked inpainting. However, similar\nworkflows in 3D modeling tasks are still unavailable due to the lack of\ncontrollability and efficiency in 3D generation. In this paper, we present a\nnovel controllable and interactive 3D assets modeling framework, named Coin3D.\nCoin3D allows users to control the 3D generation using a coarse geometry proxy\nassembled from basic shapes, and introduces an interactive generation workflow\nto support seamless local part editing while delivering responsive 3D object\npreviewing within a few seconds. To this end, we develop several techniques,\nincluding the 3D adapter that applies volumetric coarse shape control to the\ndiffusion model, proxy-bounded editing strategy for precise part editing,\nprogressive volume cache to support responsive preview, and volume-SDS to\nensure consistent mesh reconstruction. Extensive experiments of interactive\ngeneration and editing on diverse shape proxies demonstrate that our method\nachieves superior controllability and flexibility in the 3D assets generation\ntask.\n", "date": "2024-05-13", "categories": []}
{"id": "2405.08055", "title": "DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D\n  Generation", "abstract": "  Generating diverse and high-quality 3D assets automatically poses a\nfundamental yet challenging task in 3D computer vision. Despite extensive\nefforts in 3D generation, existing optimization-based approaches struggle to\nproduce large-scale 3D assets efficiently. Meanwhile, feed-forward methods\noften focus on generating only a single category or a few categories, limiting\ntheir generalizability. Therefore, we introduce a diffusion-based feed-forward\nframework to address these challenges with a single model. To handle the large\ndiversity and complexity in geometry and texture across categories efficiently,\nwe 1) adopt improved triplane to guarantee efficiency; 2) introduce the\n3D-aware transformer to aggregate the generalized 3D knowledge with specialized\n3D features; and 3) devise the 3D-aware encoder/decoder to enhance the\ngeneralized 3D knowledge. Building upon our 3D-aware Diffusion model with\nTransFormer, DiffTF, we propose a stronger version for 3D generation, i.e.,\nDiffTF++. It boils down to two parts: multi-view reconstruction loss and\ntriplane refinement. Specifically, we utilize multi-view reconstruction loss to\nfine-tune the diffusion model and triplane decoder, thereby avoiding the\nnegative influence caused by reconstruction errors and improving texture\nsynthesis. By eliminating the mismatch between the two stages, the generative\nperformance is enhanced, especially in texture. Additionally, a 3D-aware\nrefinement process is introduced to filter out artifacts and refine triplanes,\nresulting in the generation of more intricate and reasonable details. Extensive\nexperiments on ShapeNet and OmniObject3D convincingly demonstrate the\neffectiveness of our proposed modules and the state-of-the-art 3D object\ngeneration performance with large diversity, rich semantics, and high quality.\n", "date": "2024-05-13", "categories": []}
{"id": "2310.08529", "title": "GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging\n  2D and 3D Diffusion Models", "abstract": "  In recent times, the generation of 3D assets from text prompts has shown\nimpressive results. Both 2D and 3D diffusion models can help generate decent 3D\nobjects based on prompts. 3D diffusion models have good 3D consistency, but\ntheir quality and generalization are limited as trainable 3D data is expensive\nand hard to obtain. 2D diffusion models enjoy strong abilities of\ngeneralization and fine generation, but 3D consistency is hard to guarantee.\nThis paper attempts to bridge the power from the two types of diffusion models\nvia the recent explicit and efficient 3D Gaussian splatting representation. A\nfast 3D object generation framework, named as GaussianDreamer, is proposed,\nwhere the 3D diffusion model provides priors for initialization and the 2D\ndiffusion model enriches the geometry and appearance. Operations of noisy point\ngrowing and color perturbation are introduced to enhance the initialized\nGaussians. Our GaussianDreamer can generate a high-quality 3D instance or 3D\navatar within 15 minutes on one GPU, much faster than previous methods, while\nthe generated instances can be directly rendered in real time. Demos and code\nare available at https://taoranyi.com/gaussiandreamer/.\n", "date": "2023-10-12", "categories": []}
{"id": "2311.17834", "title": "Spice-E : Structural Priors in 3D Diffusion using Cross-Entity Attention", "abstract": "  We are witnessing rapid progress in automatically generating and manipulating\n3D assets due to the availability of pretrained text-image diffusion models.\nHowever, time-consuming optimization procedures are required for synthesizing\neach sample, hindering their potential for democratizing 3D content creation.\nConversely, 3D diffusion models now train on million-scale 3D datasets,\nyielding high-quality text-conditional 3D samples within seconds. In this work,\nwe present Spice-E - a neural network that adds structural guidance to 3D\ndiffusion models, extending their usage beyond text-conditional generation. At\nits core, our framework introduces a cross-entity attention mechanism that\nallows for multiple entities (in particular, paired input and guidance 3D\nshapes) to interact via their internal representations within the denoising\nnetwork. We utilize this mechanism for learning task-specific structural priors\nin 3D diffusion models from auxiliary guidance shapes. We show that our\napproach supports a variety of applications, including 3D stylization, semantic\nshape editing and text-conditional abstraction-to-3D, which transforms\nprimitive-based abstractions into highly-expressive shapes. Extensive\nexperiments demonstrate that Spice-E achieves SOTA performance over these tasks\nwhile often being considerably faster than alternative methods. Importantly,\nthis is accomplished without tailoring our approach for any specific task.\n", "date": "2023-11-29", "categories": []}
{"id": "2312.07539", "title": "HeadArtist: Text-conditioned 3D Head Generation with Self Score\n  Distillation", "abstract": "  This work presents HeadArtist for 3D head generation from text descriptions.\nWith a landmark-guided ControlNet serving as the generative prior, we come up\nwith an efficient pipeline that optimizes a parameterized 3D head model under\nthe supervision of the prior distillation itself. We call such a process self\nscore distillation (SSD). In detail, given a sampled camera pose, we first\nrender an image and its corresponding landmarks from the head model, and add\nsome particular level of noise onto the image. The noisy image, landmarks, and\ntext condition are then fed into the frozen ControlNet twice for noise\nprediction. Two different classifier-free guidance (CFG) weights are applied\nduring these two predictions, and the prediction difference offers a direction\non how the rendered image can better match the text of interest. Experimental\nresults suggest that our approach delivers high-quality 3D head sculptures with\nadequate geometry and photorealistic appearance, significantly outperforming\nstate-ofthe-art methods. We also show that the same pipeline well supports\nediting the generated heads, including both geometry deformation and appearance\nchange.\n", "date": "2023-12-12", "categories": []}
{"id": "2403.02234", "title": "3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors", "abstract": "  We present a two-stage text-to-3D generation system, namely 3DTopia, which\ngenerates high-quality general 3D assets within 5 minutes using hybrid\ndiffusion priors. The first stage samples from a 3D diffusion prior directly\nlearned from 3D data. Specifically, it is powered by a text-conditioned\ntri-plane latent diffusion model, which quickly generates coarse 3D samples for\nfast prototyping. The second stage utilizes 2D diffusion priors to further\nrefine the texture of coarse 3D models from the first stage. The refinement\nconsists of both latent and pixel space optimization for high-quality texture\ngeneration. To facilitate the training of the proposed system, we clean and\ncaption the largest open-source 3D dataset, Objaverse, by combining the power\nof vision language models and large language models. Experiment results are\nreported qualitatively and quantitatively to show the performance of the\nproposed system. Our codes and models are available at\nhttps://github.com/3DTopia/3DTopia\n", "date": "2024-03-04", "categories": []}
{"id": "2306.12422", "title": "DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D\n  Generation", "abstract": "  Text-to-image diffusion models pre-trained on billions of image-text pairs\nhave recently enabled 3D content creation by optimizing a randomly initialized\ndifferentiable 3D representation with score distillation. However, the\noptimization process suffers slow convergence and the resultant 3D models often\nexhibit two limitations: (a) quality concerns such as missing attributes and\ndistorted shape and texture; (b) extremely low diversity comparing to\ntext-guided image synthesis. In this paper, we show that the conflict between\nthe 3D optimization process and uniform timestep sampling in score distillation\nis the main reason for these limitations. To resolve this conflict, we propose\nto prioritize timestep sampling with monotonically non-increasing functions,\nwhich aligns the 3D optimization process with the sampling process of diffusion\nmodel. Extensive experiments show that our simple redesign significantly\nimproves 3D content creation with faster convergence, better quality and\ndiversity.\n", "date": "2023-06-21", "categories": []}
{"id": "2404.04562", "title": "Diffusion Time-step Curriculum for One Image to 3D Generation", "abstract": "  Score distillation sampling~(SDS) has been widely adopted to overcome the\nabsence of unseen views in reconstructing 3D objects from a \\textbf{single}\nimage. It leverages pre-trained 2D diffusion models as teacher to guide the\nreconstruction of student 3D models. Despite their remarkable success,\nSDS-based methods often encounter geometric artifacts and texture saturation.\nWe find out the crux is the overlooked indiscriminate treatment of diffusion\ntime-steps during optimization: it unreasonably treats the student-teacher\nknowledge distillation to be equal at all time-steps and thus entangles\ncoarse-grained and fine-grained modeling. Therefore, we propose the Diffusion\nTime-step Curriculum one-image-to-3D pipeline (DTC123), which involves both the\nteacher and student models collaborating with the time-step curriculum in a\ncoarse-to-fine manner. Extensive experiments on NeRF4, RealFusion15, GSO and\nLevel50 benchmark demonstrate that DTC123 can produce multi-view consistent,\nhigh-quality, and diverse 3D assets. Codes and more generation demos will be\nreleased in https://github.com/yxymessi/DTC123.\n", "date": "2024-04-06", "categories": []}
{"id": "2402.02972", "title": "Retrieval-Augmented Score Distillation for Text-to-3D Generation", "abstract": "  Text-to-3D generation has achieved significant success by incorporating\npowerful 2D diffusion models, but insufficient 3D prior knowledge also leads to\nthe inconsistency of 3D geometry. Recently, since large-scale multi-view\ndatasets have been released, fine-tuning the diffusion model on the multi-view\ndatasets becomes a mainstream to solve the 3D inconsistency problem. However,\nit has confronted with fundamental difficulties regarding the limited quality\nand diversity of 3D data, compared with 2D data. To sidestep these trade-offs,\nwe explore a retrieval-augmented approach tailored for score distillation,\ndubbed ReDream. We postulate that both expressiveness of 2D diffusion models\nand geometric consistency of 3D assets can be fully leveraged by employing the\nsemantically relevant assets directly within the optimization process. To this\nend, we introduce novel framework for retrieval-based quality enhancement in\ntext-to-3D generation. We leverage the retrieved asset to incorporate its\ngeometric prior in the variational objective and adapt the diffusion model's 2D\nprior toward view consistency, achieving drastic improvements in both geometry\nand fidelity of generated scenes. We conduct extensive experiments to\ndemonstrate that ReDream exhibits superior quality with increased geometric\nconsistency. Project page is available at https://ku-cvlab.github.io/ReDream/.\n", "date": "2024-02-05", "categories": []}
{"id": "2403.00372", "title": "HyperSDFusion: Bridging Hierarchical Structures in Language and Geometry\n  for Enhanced 3D Text2Shape Generation", "abstract": "  3D shape generation from text is a fundamental task in 3D representation\nlearning. The text-shape pairs exhibit a hierarchical structure, where a\ngeneral text like ``chair\" covers all 3D shapes of the chair, while more\ndetailed prompts refer to more specific shapes. Furthermore, both text and 3D\nshapes are inherently hierarchical structures. However, existing Text2Shape\nmethods, such as SDFusion, do not exploit that. In this work, we propose\nHyperSDFusion, a dual-branch diffusion model that generates 3D shapes from a\ngiven text. Since hyperbolic space is suitable for handling hierarchical data,\nwe propose to learn the hierarchical representations of text and 3D shapes in\nhyperbolic space. First, we introduce a hyperbolic text-image encoder to learn\nthe sequential and multi-modal hierarchical features of text in hyperbolic\nspace. In addition, we design a hyperbolic text-graph convolution module to\nlearn the hierarchical features of text in hyperbolic space. In order to fully\nutilize these text features, we introduce a dual-branch structure to embed text\nfeatures in 3D feature space. At last, to endow the generated 3D shapes with a\nhierarchical structure, we devise a hyperbolic hierarchical loss. Our method is\nthe first to explore the hyperbolic hierarchical representation for\ntext-to-shape generation. Experimental results on the existing text-to-shape\npaired dataset, Text2Shape, achieved state-of-the-art results. We release our\nimplementation under HyperSDFusion.github.io.\n", "date": "2024-03-01", "categories": []}
{"id": "2311.08403", "title": "Instant3D: Instant Text-to-3D Generation", "abstract": "  Text-to-3D generation has attracted much attention from the computer vision\ncommunity. Existing methods mainly optimize a neural field from scratch for\neach text prompt, relying on heavy and repetitive training cost which impedes\ntheir practical deployment. In this paper, we propose a novel framework for\nfast text-to-3D generation, dubbed Instant3D. Once trained, Instant3D is able\nto create a 3D object for an unseen text prompt in less than one second with a\nsingle run of a feedforward network. We achieve this remarkable speed by\ndevising a new network that directly constructs a 3D triplane from a text\nprompt. The core innovation of our Instant3D lies in our exploration of\nstrategies to effectively inject text conditions into the network. In\nparticular, we propose to combine three key mechanisms: cross-attention, style\ninjection, and token-to-plane transformation, which collectively ensure precise\nalignment of the output with the input text. Furthermore, we propose a simple\nyet effective activation function, the scaled-sigmoid, to replace the original\nsigmoid function, which speeds up the training convergence by more than ten\ntimes. Finally, to address the Janus (multi-head) problem in 3D generation, we\npropose an adaptive Perp-Neg algorithm that can dynamically adjust its concept\nnegation scales according to the severity of the Janus problem during training,\neffectively reducing the multi-head effect. Extensive experiments on a wide\nvariety of benchmark datasets demonstrate that the proposed algorithm performs\nfavorably against the state-of-the-art methods both qualitatively and\nquantitatively, while achieving significantly better efficiency. The code,\ndata, and models are available at https://github.com/ming1993li/Instant3DCodes.\n", "date": "2023-11-14", "categories": []}
{"id": "2404.18065", "title": "Grounded Compositional and Diverse Text-to-3D with Pretrained Multi-View\n  Diffusion Model", "abstract": "  In this paper, we propose an effective two-stage approach named\nGrounded-Dreamer to generate 3D assets that can accurately follow complex,\ncompositional text prompts while achieving high fidelity by using a pre-trained\nmulti-view diffusion model. Multi-view diffusion models, such as MVDream, have\nshown to generate high-fidelity 3D assets using score distillation sampling\n(SDS). However, applied naively, these methods often fail to comprehend\ncompositional text prompts, and may often entirely omit certain subjects or\nparts. To address this issue, we first advocate leveraging text-guided 4-view\nimages as the bottleneck in the text-to-3D pipeline. We then introduce an\nattention refocusing mechanism to encourage text-aligned 4-view image\ngeneration, without the necessity to re-train the multi-view diffusion model or\ncraft a high-quality compositional 3D dataset. We further propose a hybrid\noptimization strategy to encourage synergy between the SDS loss and the sparse\nRGB reference images. Our method consistently outperforms previous\nstate-of-the-art (SOTA) methods in generating compositional 3D assets,\nexcelling in both quality and accuracy, and enabling diverse 3D from the same\ntext prompt.\n", "date": "2024-04-28", "categories": []}
{"id": "2404.17419", "title": "Multi-view Image Prompted Multi-view Diffusion for Improved 3D\n  Generation", "abstract": "  Using image as prompts for 3D generation demonstrate particularly strong\nperformances compared to using text prompts alone, for images provide a more\nintuitive guidance for the 3D generation process. In this work, we delve into\nthe potential of using multiple image prompts, instead of a single image\nprompt, for 3D generation. Specifically, we build on ImageDream, a novel\nimage-prompt multi-view diffusion model, to support multi-view images as the\ninput prompt. Our method, dubbed MultiImageDream, reveals that transitioning\nfrom a single-image prompt to multiple-image prompts enhances the performance\nof multi-view and 3D object generation according to various quantitative\nevaluation metrics and qualitative assessments. This advancement is achieved\nwithout the necessity of fine-tuning the pre-trained ImageDream multi-view\ndiffusion model.\n", "date": "2024-04-26", "categories": []}
{"id": "2404.16510", "title": "Interactive3D: Create What You Want by Interactive 3D Generation", "abstract": "  3D object generation has undergone significant advancements, yielding\nhigh-quality results. However, fall short of achieving precise user control,\noften yielding results that do not align with user expectations, thus limiting\ntheir applicability. User-envisioning 3D object generation faces significant\nchallenges in realizing its concepts using current generative models due to\nlimited interaction capabilities. Existing methods mainly offer two approaches:\n(i) interpreting textual instructions with constrained controllability, or (ii)\nreconstructing 3D objects from 2D images. Both of them limit customization to\nthe confines of the 2D reference and potentially introduce undesirable\nartifacts during the 3D lifting process, restricting the scope for direct and\nversatile 3D modifications. In this work, we introduce Interactive3D, an\ninnovative framework for interactive 3D generation that grants users precise\ncontrol over the generative process through extensive 3D interaction\ncapabilities. Interactive3D is constructed in two cascading stages, utilizing\ndistinct 3D representations. The first stage employs Gaussian Splatting for\ndirect user interaction, allowing modifications and guidance of the generative\ndirection at any intermediate step through (i) Adding and Removing components,\n(ii) Deformable and Rigid Dragging, (iii) Geometric Transformations, and (iv)\nSemantic Editing. Subsequently, the Gaussian splats are transformed into\nInstantNGP. We introduce a novel (v) Interactive Hash Refinement module to\nfurther add details and extract the geometry in the second stage. Our\nexperiments demonstrate that Interactive3D markedly improves the\ncontrollability and quality of 3D generation. Our project webpage is available\nat \\url{https://interactive-3d.github.io/}.\n", "date": "2024-04-25", "categories": []}
{"id": "2312.09222", "title": "Mosaic-SDF for 3D Generative Models", "abstract": "  Current diffusion or flow-based generative models for 3D shapes divide to\ntwo: distilling pre-trained 2D image diffusion models, and training directly on\n3D shapes. When training a diffusion or flow models on 3D shapes a crucial\ndesign choice is the shape representation. An effective shape representation\nneeds to adhere three design principles: it should allow an efficient\nconversion of large 3D datasets to the representation form; it should provide a\ngood tradeoff of approximation power versus number of parameters; and it should\nhave a simple tensorial form that is compatible with existing powerful neural\narchitectures. While standard 3D shape representations such as volumetric grids\nand point clouds do not adhere to all these principles simultaneously, we\nadvocate in this paper a new representation that does. We introduce Mosaic-SDF\n(M-SDF): a simple 3D shape representation that approximates the Signed Distance\nFunction (SDF) of a given shape by using a set of local grids spread near the\nshape's boundary. The M-SDF representation is fast to compute for each shape\nindividually making it readily parallelizable; it is parameter efficient as it\nonly covers the space around the shape's boundary; and it has a simple matrix\nform, compatible with Transformer-based architectures. We demonstrate the\nefficacy of the M-SDF representation by using it to train a 3D generative flow\nmodel including class-conditioned generation with the 3D Warehouse dataset, and\ntext-to-3D generation using a dataset of about 600k caption-shape pairs.\n", "date": "2023-12-14", "categories": []}
{"id": "2312.09069", "title": "PI3D: Efficient Text-to-3D Generation with Pseudo-Image Diffusion", "abstract": "  Diffusion models trained on large-scale text-image datasets have demonstrated\na strong capability of controllable high-quality image generation from\narbitrary text prompts. However, the generation quality and generalization\nability of 3D diffusion models is hindered by the scarcity of high-quality and\nlarge-scale 3D datasets. In this paper, we present PI3D, a framework that fully\nleverages the pre-trained text-to-image diffusion models' ability to generate\nhigh-quality 3D shapes from text prompts in minutes. The core idea is to\nconnect the 2D and 3D domains by representing a 3D shape as a set of Pseudo RGB\nImages. We fine-tune an existing text-to-image diffusion model to produce such\npseudo-images using a small number of text-3D pairs. Surprisingly, we find that\nit can already generate meaningful and consistent 3D shapes given complex text\ndescriptions. We further take the generated shapes as the starting point for a\nlightweight iterative refinement using score distillation sampling to achieve\nhigh-quality generation under a low budget. PI3D generates a single 3D shape\nfrom text in only 3 minutes and the quality is validated to outperform existing\n3D generative models by a large margin.\n", "date": "2023-12-14", "categories": []}
{"id": "2403.06973", "title": "Bayesian Diffusion Models for 3D Shape Reconstruction", "abstract": "  We present Bayesian Diffusion Models (BDM), a prediction algorithm that\nperforms effective Bayesian inference by tightly coupling the top-down (prior)\ninformation with the bottom-up (data-driven) procedure via joint diffusion\nprocesses. We show the effectiveness of BDM on the 3D shape reconstruction\ntask. Compared to prototypical deep learning data-driven approaches trained on\npaired (supervised) data-labels (e.g. image-point clouds) datasets, our BDM\nbrings in rich prior information from standalone labels (e.g. point clouds) to\nimprove the bottom-up 3D reconstruction. As opposed to the standard Bayesian\nframeworks where explicit prior and likelihood are required for the inference,\nBDM performs seamless information fusion via coupled diffusion processes with\nlearned gradient computation networks. The specialty of our BDM lies in its\ncapability to engage the active and effective information exchange and fusion\nof the top-down and bottom-up processes where each itself is a diffusion\nprocess. We demonstrate state-of-the-art results on both synthetic and\nreal-world benchmarks for 3D shape reconstruction.\n", "date": "2024-03-11", "categories": []}
{"id": "2404.00815", "title": "Towards Realistic Scene Generation with LiDAR Diffusion Models", "abstract": "  Diffusion models (DMs) excel in photo-realistic image synthesis, but their\nadaptation to LiDAR scene generation poses a substantial hurdle. This is\nprimarily because DMs operating in the point space struggle to preserve the\ncurve-like patterns and 3D geometry of LiDAR scenes, which consumes much of\ntheir representation power. In this paper, we propose LiDAR Diffusion Models\n(LiDMs) to generate LiDAR-realistic scenes from a latent space tailored to\ncapture the realism of LiDAR scenes by incorporating geometric priors into the\nlearning pipeline. Our method targets three major desiderata: pattern realism,\ngeometry realism, and object realism. Specifically, we introduce curve-wise\ncompression to simulate real-world LiDAR patterns, point-wise coordinate\nsupervision to learn scene geometry, and patch-wise encoding for a full 3D\nobject context. With these three core designs, our method achieves competitive\nperformance on unconditional LiDAR generation in 64-beam scenario and state of\nthe art on conditional LiDAR generation, while maintaining high efficiency\ncompared to point-based DMs (up to 107$\\times$ faster). Furthermore, by\ncompressing LiDAR scenes into a latent space, we enable the controllability of\nDMs with various conditions such as semantic maps, camera views, and text\nprompts.\n", "date": "2024-03-31", "categories": []}
{"id": "2308.16512", "title": "MVDream: Multi-view Diffusion for 3D Generation", "abstract": "  We introduce MVDream, a diffusion model that is able to generate consistent\nmulti-view images from a given text prompt. Learning from both 2D and 3D data,\na multi-view diffusion model can achieve the generalizability of 2D diffusion\nmodels and the consistency of 3D renderings. We demonstrate that such a\nmulti-view diffusion model is implicitly a generalizable 3D prior agnostic to\n3D representations. It can be applied to 3D generation via Score Distillation\nSampling, significantly enhancing the consistency and stability of existing\n2D-lifting methods. It can also learn new concepts from a few 2D examples, akin\nto DreamBooth, but for 3D generation.\n", "date": "2023-08-31", "categories": []}
{"id": "2404.10394", "title": "Portrait3D: Text-Guided High-Quality 3D Portrait Generation Using\n  Pyramid Representation and GANs Prior", "abstract": "  Existing neural rendering-based text-to-3D-portrait generation methods\ntypically make use of human geometry prior and diffusion models to obtain\nguidance. However, relying solely on geometry information introduces issues\nsuch as the Janus problem, over-saturation, and over-smoothing. We present\nPortrait3D, a novel neural rendering-based framework with a novel joint\ngeometry-appearance prior to achieve text-to-3D-portrait generation that\novercomes the aforementioned issues. To accomplish this, we train a 3D portrait\ngenerator, 3DPortraitGAN-Pyramid, as a robust prior. This generator is capable\nof producing 360{\\deg} canonical 3D portraits, serving as a starting point for\nthe subsequent diffusion-based generation process. To mitigate the \"grid-like\"\nartifact caused by the high-frequency information in the feature-map-based 3D\nrepresentation commonly used by most 3D-aware GANs, we integrate a novel\npyramid tri-grid 3D representation into 3DPortraitGAN-Pyramid. To generate 3D\nportraits from text, we first project a randomly generated image aligned with\nthe given prompt into the pre-trained 3DPortraitGAN-Pyramid's latent space. The\nresulting latent code is then used to synthesize a pyramid tri-grid. Beginning\nwith the obtained pyramid tri-grid, we use score distillation sampling to\ndistill the diffusion model's knowledge into the pyramid tri-grid. Following\nthat, we utilize the diffusion model to refine the rendered images of the 3D\nportrait and then use these refined images as training data to further optimize\nthe pyramid tri-grid, effectively eliminating issues with unrealistic color and\nunnatural artifacts. Our experimental results show that Portrait3D can produce\nrealistic, high-quality, and canonical 3D portraits that align with the prompt.\n", "date": "2024-04-16", "categories": []}
{"id": "2404.07191", "title": "InstantMesh: Efficient 3D Mesh Generation from a Single Image with\n  Sparse-view Large Reconstruction Models", "abstract": "  We present InstantMesh, a feed-forward framework for instant 3D mesh\ngeneration from a single image, featuring state-of-the-art generation quality\nand significant training scalability. By synergizing the strengths of an\noff-the-shelf multiview diffusion model and a sparse-view reconstruction model\nbased on the LRM architecture, InstantMesh is able to create diverse 3D assets\nwithin 10 seconds. To enhance the training efficiency and exploit more\ngeometric supervisions, e.g, depths and normals, we integrate a differentiable\niso-surface extraction module into our framework and directly optimize on the\nmesh representation. Experimental results on public datasets demonstrate that\nInstantMesh significantly outperforms other latest image-to-3D baselines, both\nqualitatively and quantitatively. We release all the code, weights, and demo of\nInstantMesh, with the intention that it can make substantial contributions to\nthe community of 3D generative AI and empower both researchers and content\ncreators.\n", "date": "2024-04-10", "categories": []}
{"id": "2312.16837", "title": "DiffusionGAN3D: Boosting Text-guided 3D Generation and Domain Adaptation\n  by Combining 3D GANs and Diffusion Priors", "abstract": "  Text-guided domain adaptation and generation of 3D-aware portraits find many\napplications in various fields. However, due to the lack of training data and\nthe challenges in handling the high variety of geometry and appearance, the\nexisting methods for these tasks suffer from issues like inflexibility,\ninstability, and low fidelity. In this paper, we propose a novel framework\nDiffusionGAN3D, which boosts text-guided 3D domain adaptation and generation by\ncombining 3D GANs and diffusion priors. Specifically, we integrate the\npre-trained 3D generative models (e.g., EG3D) and text-to-image diffusion\nmodels. The former provides a strong foundation for stable and high-quality\navatar generation from text. And the diffusion models in turn offer powerful\npriors and guide the 3D generator finetuning with informative direction to\nachieve flexible and efficient text-guided domain adaptation. To enhance the\ndiversity in domain adaptation and the generation capability in text-to-avatar,\nwe introduce the relative distance loss and case-specific learnable triplane\nrespectively. Besides, we design a progressive texture refinement module to\nimprove the texture quality for both tasks above. Extensive experiments\ndemonstrate that the proposed framework achieves excellent results in both\ndomain adaptation and text-to-avatar tasks, outperforming existing methods in\nterms of generation quality and efficiency. The project homepage is at\nhttps://younglbw.github.io/DiffusionGAN3D-homepage/.\n", "date": "2023-12-28", "categories": []}
{"id": "2402.16994", "title": "GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis", "abstract": "  We introduce GEM3D -- a new deep, topology-aware generative model of 3D\nshapes. The key ingredient of our method is a neural skeleton-based\nrepresentation encoding information on both shape topology and geometry.\nThrough a denoising diffusion probabilistic model, our method first generates\nskeleton-based representations following the Medial Axis Transform (MAT), then\ngenerates surfaces through a skeleton-driven neural implicit formulation. The\nneural implicit takes into account the topological and geometric information\nstored in the generated skeleton representations to yield surfaces that are\nmore topologically and geometrically accurate compared to previous neural field\nformulations. We discuss applications of our method in shape synthesis and\npoint cloud reconstruction tasks, and evaluate our method both qualitatively\nand quantitatively. We demonstrate significantly more faithful surface\nreconstruction and diverse shape generation results compared to the\nstate-of-the-art, also involving challenging scenarios of reconstructing and\nsynthesizing structurally complex, high-genus shape surfaces from Thingi10K and\nShapeNet.\n", "date": "2024-02-26", "categories": []}
{"id": "2404.06851", "title": "UDiFF: Generating Conditional Unsigned Distance Fields with Optimal\n  Wavelet Diffusion", "abstract": "  Diffusion models have shown remarkable results for image generation, editing\nand inpainting. Recent works explore diffusion models for 3D shape generation\nwith neural implicit functions, i.e., signed distance function and occupancy\nfunction. However, they are limited to shapes with closed surfaces, which\nprevents them from generating diverse 3D real-world contents containing open\nsurfaces. In this work, we present UDiFF, a 3D diffusion model for unsigned\ndistance fields (UDFs) which is capable to generate textured 3D shapes with\nopen surfaces from text conditions or unconditionally. Our key idea is to\ngenerate UDFs in spatial-frequency domain with an optimal wavelet\ntransformation, which produces a compact representation space for UDF\ngeneration. Specifically, instead of selecting an appropriate wavelet\ntransformation which requires expensive manual efforts and still leads to large\ninformation loss, we propose a data-driven approach to learn the optimal\nwavelet transformation for UDFs. We evaluate UDiFF to show our advantages by\nnumerical and visual comparisons with the latest methods on widely used\nbenchmarks. Page: https://weiqi-zhang.github.io/UDiFF.\n", "date": "2024-04-10", "categories": []}
{"id": "2404.06091", "title": "Hash3D: Training-free Acceleration for 3D Generation", "abstract": "  The evolution of 3D generative modeling has been notably propelled by the\nadoption of 2D diffusion models. Despite this progress, the cumbersome\noptimization process per se presents a critical hurdle to efficiency. In this\npaper, we introduce Hash3D, a universal acceleration for 3D generation without\nmodel training. Central to Hash3D is the insight that feature-map redundancy is\nprevalent in images rendered from camera positions and diffusion time-steps in\nclose proximity. By effectively hashing and reusing these feature maps across\nneighboring timesteps and camera angles, Hash3D substantially prevents\nredundant calculations, thus accelerating the diffusion model's inference in 3D\ngeneration tasks. We achieve this through an adaptive grid-based hashing.\nSurprisingly, this feature-sharing mechanism not only speed up the generation\nbut also enhances the smoothness and view consistency of the synthesized 3D\nobjects. Our experiments covering 5 text-to-3D and 3 image-to-3D models,\ndemonstrate Hash3D's versatility to speed up optimization, enhancing efficiency\nby 1.3 to 4 times. Additionally, Hash3D's integration with 3D Gaussian\nsplatting largely speeds up 3D model creation, reducing text-to-3D processing\nto about 10 minutes and image-to-3D conversion to roughly 30 seconds. The\nproject page is at https://adamdad.github.io/hash3D/.\n", "date": "2024-04-09", "categories": []}
{"id": "2404.01843", "title": "Sketch3D: Style-Consistent Guidance for Sketch-to-3D Generation", "abstract": "  Recently, image-to-3D approaches have achieved significant results with a\nnatural image as input. However, it is not always possible to access these\nenriched color input samples in practical applications, where only sketches are\navailable. Existing sketch-to-3D researches suffer from limitations in broad\napplications due to the challenges of lacking color information and multi-view\ncontent. To overcome them, this paper proposes a novel generation paradigm\nSketch3D to generate realistic 3D assets with shape aligned with the input\nsketch and color matching the textual description. Concretely, Sketch3D first\ninstantiates the given sketch in the reference image through the\nshape-preserving generation process. Second, the reference image is leveraged\nto deduce a coarse 3D Gaussian prior, and multi-view style-consistent guidance\nimages are generated based on the renderings of the 3D Gaussians. Finally,\nthree strategies are designed to optimize 3D Gaussians, i.e., structural\noptimization via a distribution transfer mechanism, color optimization with a\nstraightforward MSE loss and sketch similarity optimization with a CLIP-based\ngeometric similarity loss. Extensive visual comparisons and quantitative\nanalysis illustrate the advantage of our Sketch3D in generating realistic 3D\nassets while preserving consistency with the input.\n", "date": "2024-04-02", "categories": []}
{"id": "2403.19773", "title": "ShapeFusion: A 3D diffusion model for localized shape editing", "abstract": "  In the realm of 3D computer vision, parametric models have emerged as a\nground-breaking methodology for the creation of realistic and expressive 3D\navatars. Traditionally, they rely on Principal Component Analysis (PCA), given\nits ability to decompose data to an orthonormal space that maximally captures\nshape variations. However, due to the orthogonality constraints and the global\nnature of PCA's decomposition, these models struggle to perform localized and\ndisentangled editing of 3D shapes, which severely affects their use in\napplications requiring fine control such as face sculpting. In this paper, we\nleverage diffusion models to enable diverse and fully localized edits on 3D\nmeshes, while completely preserving the un-edited regions. We propose an\neffective diffusion masking training strategy that, by design, facilitates\nlocalized manipulation of any shape region, without being limited to predefined\nregions or to sparse sets of predefined control vertices. Following our\nframework, a user can explicitly set their manipulation region of choice and\ndefine an arbitrary set of vertices as handles to edit a 3D mesh. Compared to\nthe current state-of-the-art our method leads to more interpretable shape\nmanipulations than methods relying on latent code state, greater localization\nand generation diversity while offering faster inference than optimization\nbased approaches. Project page: https://rolpotamias.github.io/Shapefusion/\n", "date": "2024-03-28", "categories": []}
{"id": "2404.03042", "title": "AWOL: Analysis WithOut synthesis using Language", "abstract": "  Many classical parametric 3D shape models exist, but creating novel shapes\nwith such models requires expert knowledge of their parameters. For example,\nimagine creating a specific type of tree using procedural graphics or a new\nkind of animal from a statistical shape model. Our key idea is to leverage\nlanguage to control such existing models to produce novel shapes. This involves\nlearning a mapping between the latent space of a vision-language model and the\nparameter space of the 3D model, which we do using a small set of shape and\ntext pairs. Our hypothesis is that mapping from language to parameters allows\nus to generate parameters for objects that were never seen during training. If\nthe mapping between language and parameters is sufficiently smooth, then\ninterpolation or generalization in language should translate appropriately into\nnovel 3D shapes. We test our approach with two very different types of\nparametric shape models (quadrupeds and arboreal trees). We use a learned\nstatistical shape model of quadrupeds and show that we can use text to generate\nnew animals not present during training. In particular, we demonstrate\nstate-of-the-art shape estimation of 3D dogs. This work also constitutes the\nfirst language-driven method for generating 3D trees. Finally, embedding images\nin the CLIP latent space enables us to generate animals and trees directly from\nimages.\n", "date": "2024-04-03", "categories": []}
{"id": "2404.03566", "title": "PointInfinity: Resolution-Invariant Point Diffusion Models", "abstract": "  We present PointInfinity, an efficient family of point cloud diffusion\nmodels. Our core idea is to use a transformer-based architecture with a\nfixed-size, resolution-invariant latent representation. This enables efficient\ntraining with low-resolution point clouds, while allowing high-resolution point\nclouds to be generated during inference. More importantly, we show that scaling\nthe test-time resolution beyond the training resolution improves the fidelity\nof generated point clouds and surfaces. We analyze this phenomenon and draw a\nlink to classifier-free guidance commonly used in diffusion models,\ndemonstrating that both allow trading off fidelity and variability during\ninference. Experiments on CO3D show that PointInfinity can efficiently generate\nhigh-resolution point clouds (up to 131k points, 31 times more than Point-E)\nwith state-of-the-art quality.\n", "date": "2024-04-04", "categories": []}
{"id": "2403.00939", "title": "G3DR: Generative 3D Reconstruction in ImageNet", "abstract": "  We introduce a novel 3D generative method, Generative 3D Reconstruction\n(G3DR) in ImageNet, capable of generating diverse and high-quality 3D objects\nfrom single images, addressing the limitations of existing methods. At the\nheart of our framework is a novel depth regularization technique that enables\nthe generation of scenes with high-geometric fidelity. G3DR also leverages a\npretrained language-vision model, such as CLIP, to enable reconstruction in\nnovel views and improve the visual realism of generations. Additionally, G3DR\ndesigns a simple but effective sampling procedure to further improve the\nquality of generations. G3DR offers diverse and efficient 3D asset generation\nbased on class or text conditioning. Despite its simplicity, G3DR is able to\nbeat state-of-theart methods, improving over them by up to 22% in perceptual\nmetrics and 90% in geometry scores, while needing only half of the training\ntime. Code is available at https://github.com/preddy5/G3DR\n", "date": "2024-03-01", "categories": []}
{"id": "2309.16585", "title": "Text-to-3D using Gaussian Splatting", "abstract": "  Automatic text-to-3D generation that combines Score Distillation Sampling\n(SDS) with the optimization of volume rendering has achieved remarkable\nprogress in synthesizing realistic 3D objects. Yet most existing text-to-3D\nmethods by SDS and volume rendering suffer from inaccurate geometry, e.g., the\nJanus issue, since it is hard to explicitly integrate 3D priors into implicit\n3D representations. Besides, it is usually time-consuming for them to generate\nelaborate 3D models with rich colors. In response, this paper proposes GSGEN, a\nnovel method that adopts Gaussian Splatting, a recent state-of-the-art\nrepresentation, to text-to-3D generation. GSGEN aims at generating high-quality\n3D objects and addressing existing shortcomings by exploiting the explicit\nnature of Gaussian Splatting that enables the incorporation of 3D prior.\nSpecifically, our method adopts a progressive optimization strategy, which\nincludes a geometry optimization stage and an appearance refinement stage. In\ngeometry optimization, a coarse representation is established under 3D point\ncloud diffusion prior along with the ordinary 2D SDS optimization, ensuring a\nsensible and 3D-consistent rough shape. Subsequently, the obtained Gaussians\nundergo an iterative appearance refinement to enrich texture details. In this\nstage, we increase the number of Gaussians by compactness-based densification\nto enhance continuity and improve fidelity. With these designs, our approach\ncan generate 3D assets with delicate details and accurate geometry. Extensive\nevaluations demonstrate the effectiveness of our method, especially for\ncapturing high-frequency components. Our code is available at\nhttps://github.com/gsgen3d/gsgen\n", "date": "2023-09-28", "categories": []}
{"id": "2401.10786", "title": "Sat2Scene: 3D Urban Scene Generation from Satellite Images with\n  Diffusion", "abstract": "  Directly generating scenes from satellite imagery offers exciting\npossibilities for integration into applications like games and map services.\nHowever, challenges arise from significant view changes and scene scale.\nPrevious efforts mainly focused on image or video generation, lacking\nexploration into the adaptability of scene generation for arbitrary views.\nExisting 3D generation works either operate at the object level or are\ndifficult to utilize the geometry obtained from satellite imagery. To overcome\nthese limitations, we propose a novel architecture for direct 3D scene\ngeneration by introducing diffusion models into 3D sparse representations and\ncombining them with neural rendering techniques. Specifically, our approach\ngenerates texture colors at the point level for a given geometry using a 3D\ndiffusion model first, which is then transformed into a scene representation in\na feed-forward manner. The representation can be utilized to render arbitrary\nviews which would excel in both single-frame quality and inter-frame\nconsistency. Experiments in two city-scale datasets show that our model\ndemonstrates proficiency in generating photo-realistic street-view image\nsequences and cross-view urban scenes from satellite imagery.\n", "date": "2024-01-19", "categories": []}
{"id": "2309.16653", "title": "DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content\n  Creation", "abstract": "  Recent advances in 3D content creation mostly leverage optimization-based 3D\ngeneration via score distillation sampling (SDS). Though promising results have\nbeen exhibited, these methods often suffer from slow per-sample optimization,\nlimiting their practical usage. In this paper, we propose DreamGaussian, a\nnovel 3D content generation framework that achieves both efficiency and quality\nsimultaneously. Our key insight is to design a generative 3D Gaussian Splatting\nmodel with companioned mesh extraction and texture refinement in UV space. In\ncontrast to the occupancy pruning used in Neural Radiance Fields, we\ndemonstrate that the progressive densification of 3D Gaussians converges\nsignificantly faster for 3D generative tasks. To further enhance the texture\nquality and facilitate downstream applications, we introduce an efficient\nalgorithm to convert 3D Gaussians into textured meshes and apply a fine-tuning\nstage to refine the details. Extensive experiments demonstrate the superior\nefficiency and competitive generation quality of our proposed approach.\nNotably, DreamGaussian produces high-quality textured meshes in just 2 minutes\nfrom a single-view image, achieving approximately 10 times acceleration\ncompared to existing methods.\n", "date": "2023-09-28", "categories": []}
{"id": "2402.17464", "title": "Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing", "abstract": "  Generative 3D part assembly involves understanding part relationships and\npredicting their 6-DoF poses for assembling a realistic 3D shape. Prior work\noften focus on the geometry of individual parts, neglecting part-whole\nhierarchies of objects. Leveraging two key observations: 1) super-part poses\nprovide strong hints about part poses, and 2) predicting super-part poses is\neasier due to fewer superparts, we propose a part-whole-hierarchy message\npassing network for efficient 3D part assembly. We first introduce super-parts\nby grouping geometrically similar parts without any semantic labels. Then we\nemploy a part-whole hierarchical encoder, wherein a super-part encoder predicts\nlatent super-part poses based on input parts. Subsequently, we transform the\npoint cloud using the latent poses, feeding it to the part encoder for\naggregating super-part information and reasoning about part relationships to\npredict all part poses. In training, only ground-truth part poses are required.\nDuring inference, the predicted latent poses of super-parts enhance\ninterpretability. Experimental results on the PartNet dataset show that our\nmethod achieves state-of-the-art performance in part and connectivity accuracy\nand enables an interpretable hierarchical part assembly. Code is available at\nhttps://github.com/pkudba/3DHPA.\n", "date": "2024-02-27", "categories": []}
{"id": "2402.12225", "title": "Pushing Auto-regressive Models for 3D Shape Generation at Capacity and\n  Scalability", "abstract": "  Auto-regressive models have achieved impressive results in 2D image\ngeneration by modeling joint distributions in grid space. In this paper, we\nextend auto-regressive models to 3D domains, and seek a stronger ability of 3D\nshape generation by improving auto-regressive models at capacity and\nscalability simultaneously. Firstly, we leverage an ensemble of publicly\navailable 3D datasets to facilitate the training of large-scale models. It\nconsists of a comprehensive collection of approximately 900,000 objects, with\nmultiple properties of meshes, points, voxels, rendered images, and text\ncaptions. This diverse labeled dataset, termed Objaverse-Mix, empowers our\nmodel to learn from a wide range of object variations. However, directly\napplying 3D auto-regression encounters critical challenges of high\ncomputational demands on volumetric grids and ambiguous auto-regressive order\nalong grid dimensions, resulting in inferior quality of 3D shapes. To this end,\nwe then present a novel framework Argus3D in terms of capacity. Concretely, our\napproach introduces discrete representation learning based on a latent vector\ninstead of volumetric grids, which not only reduces computational costs but\nalso preserves essential geometric details by learning the joint distributions\nin a more tractable order. The capacity of conditional generation can thus be\nrealized by simply concatenating various conditioning inputs to the latent\nvector, such as point clouds, categories, images, and texts. In addition,\nthanks to the simplicity of our model architecture, we naturally scale up our\napproach to a larger model with an impressive 3.6 billion parameters, further\nenhancing the quality of versatile 3D generation. Extensive experiments on four\ngeneration tasks demonstrate that Argus3D can synthesize diverse and faithful\nshapes across multiple categories, achieving remarkable performance.\n", "date": "2024-02-19", "categories": []}
{"id": "2403.17237", "title": "DreamPolisher: Towards High-Quality Text-to-3D Generation via Geometric\n  Diffusion", "abstract": "  We present DreamPolisher, a novel Gaussian Splatting based method with\ngeometric guidance, tailored to learn cross-view consistency and intricate\ndetail from textual descriptions. While recent progress on text-to-3D\ngeneration methods have been promising, prevailing methods often fail to ensure\nview-consistency and textural richness. This problem becomes particularly\nnoticeable for methods that work with text input alone. To address this, we\npropose a two-stage Gaussian Splatting based approach that enforces geometric\nconsistency among views. Initially, a coarse 3D generation undergoes refinement\nvia geometric optimization. Subsequently, we use a ControlNet driven refiner\ncoupled with the geometric consistency term to improve both texture fidelity\nand overall consistency of the generated 3D asset. Empirical evaluations across\ndiverse textual prompts spanning various object categories demonstrate the\nefficacy of DreamPolisher in generating consistent and realistic 3D objects,\naligning closely with the semantics of the textual instructions.\n", "date": "2024-03-25", "categories": []}
{"id": "2310.15168", "title": "Ghost on the Shell: An Expressive Representation of General 3D Shapes", "abstract": "  The creation of photorealistic virtual worlds requires the accurate modeling\nof 3D surface geometry for a wide range of objects. For this, meshes are\nappealing since they 1) enable fast physics-based rendering with realistic\nmaterial and lighting, 2) support physical simulation, and 3) are\nmemory-efficient for modern graphics pipelines. Recent work on reconstructing\nand statistically modeling 3D shape, however, has critiqued meshes as being\ntopologically inflexible. To capture a wide range of object shapes, any 3D\nrepresentation must be able to model solid, watertight, shapes as well as thin,\nopen, surfaces. Recent work has focused on the former, and methods for\nreconstructing open surfaces do not support fast reconstruction with material\nand lighting or unconditional generative modelling. Inspired by the observation\nthat open surfaces can be seen as islands floating on watertight surfaces, we\nparameterize open surfaces by defining a manifold signed distance field on\nwatertight templates. With this parameterization, we further develop a\ngrid-based and differentiable representation that parameterizes both watertight\nand non-watertight meshes of arbitrary topology. Our new representation, called\nGhost-on-the-Shell (G-Shell), enables two important applications:\ndifferentiable rasterization-based reconstruction from multiview images and\ngenerative modelling of non-watertight meshes. We empirically demonstrate that\nG-Shell achieves state-of-the-art performance on non-watertight mesh\nreconstruction and generation tasks, while also performing effectively for\nwatertight meshes.\n", "date": "2023-10-23", "categories": []}
{"id": "2403.17001", "title": "VP3D: Unleashing 2D Visual Prompt for Text-to-3D Generation", "abstract": "  Recent innovations on text-to-3D generation have featured Score Distillation\nSampling (SDS), which enables the zero-shot learning of implicit 3D models\n(NeRF) by directly distilling prior knowledge from 2D diffusion models.\nHowever, current SDS-based models still struggle with intricate text prompts\nand commonly result in distorted 3D models with unrealistic textures or\ncross-view inconsistency issues. In this work, we introduce a novel Visual\nPrompt-guided text-to-3D diffusion model (VP3D) that explicitly unleashes the\nvisual appearance knowledge in 2D visual prompt to boost text-to-3D generation.\nInstead of solely supervising SDS with text prompt, VP3D first capitalizes on\n2D diffusion model to generate a high-quality image from input text, which\nsubsequently acts as visual prompt to strengthen SDS optimization with explicit\nvisual appearance. Meanwhile, we couple the SDS optimization with additional\ndifferentiable reward function that encourages rendering images of 3D models to\nbetter visually align with 2D visual prompt and semantically match with text\nprompt. Through extensive experiments, we show that the 2D Visual Prompt in our\nVP3D significantly eases the learning of visual appearance of 3D models and\nthus leads to higher visual fidelity with more detailed textures. It is also\nappealing in view that when replacing the self-generating visual prompt with a\ngiven reference image, VP3D is able to trigger a new task of stylized\ntext-to-3D generation. Our project page is available at\nhttps://vp3d-cvpr24.github.io.\n", "date": "2024-03-25", "categories": []}
{"id": "2403.15385", "title": "LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis", "abstract": "  Recent text-to-3D generation approaches produce impressive 3D results but\nrequire time-consuming optimization that can take up to an hour per prompt.\nAmortized methods like ATT3D optimize multiple prompts simultaneously to\nimprove efficiency, enabling fast text-to-3D synthesis. However, they cannot\ncapture high-frequency geometry and texture details and struggle to scale to\nlarge prompt sets, so they generalize poorly. We introduce LATTE3D, addressing\nthese limitations to achieve fast, high-quality generation on a significantly\nlarger prompt set. Key to our method is 1) building a scalable architecture and\n2) leveraging 3D data during optimization through 3D-aware diffusion priors,\nshape regularization, and model initialization to achieve robustness to diverse\nand complex training prompts. LATTE3D amortizes both neural field and textured\nsurface generation to produce highly detailed textured meshes in a single\nforward pass. LATTE3D generates 3D objects in 400ms, and can be further\nenhanced with fast test-time optimization.\n", "date": "2024-03-22", "categories": []}
{"id": "2308.13223", "title": "EfficientDreamer: High-Fidelity and Robust 3D Creation via\n  Orthogonal-view Diffusion Prior", "abstract": "  While image diffusion models have made significant progress in text-driven 3D\ncontent creation, they often fail to accurately capture the intended meaning of\ntext prompts, especially for view information. This limitation leads to the\nJanus problem, where multi-faced 3D models are generated under the guidance of\nsuch diffusion models. In this paper, we propose a robust high-quality 3D\ncontent generation pipeline by exploiting orthogonal-view image guidance.\nFirst, we introduce a novel 2D diffusion model that generates an image\nconsisting of four orthogonal-view sub-images based on the given text prompt.\nThen, the 3D content is created using this diffusion model. Notably, the\ngenerated orthogonal-view image provides strong geometric structure priors and\nthus improves 3D consistency. As a result, it effectively resolves the Janus\nproblem and significantly enhances the quality of 3D content creation.\nAdditionally, we present a 3D synthesis fusion network that can further improve\nthe details of the generated 3D contents. Both quantitative and qualitative\nevaluations demonstrate that our method surpasses previous text-to-3D\ntechniques. Project page: https://efficientdreamer.github.io.\n", "date": "2023-08-25", "categories": []}
{"id": "2311.15980", "title": "Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion", "abstract": "  Recent advances in generative AI have unveiled significant potential for the\ncreation of 3D content. However, current methods either apply a pre-trained 2D\ndiffusion model with the time-consuming score distillation sampling (SDS), or a\ndirect 3D diffusion model trained on limited 3D data losing generation\ndiversity. In this work, we approach the problem by employing a multi-view 2.5D\ndiffusion fine-tuned from a pre-trained 2D diffusion model. The multi-view 2.5D\ndiffusion directly models the structural distribution of 3D data, while still\nmaintaining the strong generalization ability of the original 2D diffusion\nmodel, filling the gap between 2D diffusion-based and direct 3D diffusion-based\nmethods for 3D content generation. During inference, multi-view normal maps are\ngenerated using the 2.5D diffusion, and a novel differentiable rasterization\nscheme is introduced to fuse the almost consistent multi-view normal maps into\na consistent 3D model. We further design a normal-conditioned multi-view image\ngeneration module for fast appearance generation given the 3D geometry. Our\nmethod is a one-pass diffusion process and does not require any SDS\noptimization as post-processing. We demonstrate through extensive experiments\nthat, our direct 2.5D generation with the specially-designed fusion scheme can\nachieve diverse, mode-seeking-free, and high-fidelity 3D content generation in\nonly 10 seconds. Project page: https://nju-3dv.github.io/projects/direct25.\n", "date": "2023-11-27", "categories": []}
{"id": "2303.12236", "title": "SALAD: Part-Level Latent Diffusion for 3D Shape Generation and\n  Manipulation", "abstract": "  We present a cascaded diffusion model based on a part-level implicit 3D\nrepresentation. Our model achieves state-of-the-art generation quality and also\nenables part-level shape editing and manipulation without any additional\ntraining in conditional setup. Diffusion models have demonstrated impressive\ncapabilities in data generation as well as zero-shot completion and editing via\na guided reverse process. Recent research on 3D diffusion models has focused on\nimproving their generation capabilities with various data representations,\nwhile the absence of structural information has limited their capability in\ncompletion and editing tasks. We thus propose our novel diffusion model using a\npart-level implicit representation. To effectively learn diffusion with\nhigh-dimensional embedding vectors of parts, we propose a cascaded framework,\nlearning diffusion first on a low-dimensional subspace encoding extrinsic\nparameters of parts and then on the other high-dimensional subspace encoding\nintrinsic attributes. In the experiments, we demonstrate the outperformance of\nour method compared with the previous ones both in generation and part-level\ncompletion and manipulation tasks.\n", "date": "2023-03-21", "categories": []}
{"id": "2312.09570", "title": "CAGE: Controllable Articulation GEneration", "abstract": "  We address the challenge of generating 3D articulated objects in a\ncontrollable fashion. Currently, modeling articulated 3D objects is either\nachieved through laborious manual authoring, or using methods from prior work\nthat are hard to scale and control directly. We leverage the interplay between\npart shape, connectivity, and motion using a denoising diffusion-based method\nwith attention modules designed to extract correlations between part\nattributes. Our method takes an object category label and a part connectivity\ngraph as input and generates an object's geometry and motion parameters. The\ngenerated objects conform to user-specified constraints on the object category,\npart shape, and part articulation. Our experiments show that our method\noutperforms the state-of-the-art in articulated object generation, producing\nmore realistic objects while conforming better to user constraints.\n  Video Summary at: http://youtu.be/cH_rbKbyTpE\n", "date": "2023-12-15", "categories": []}
{"id": "2401.00711", "title": "Text2Avatar: Text to 3D Human Avatar Generation with Codebook-Driven\n  Body Controllable Attribute", "abstract": "  Generating 3D human models directly from text helps reduce the cost and time\nof character modeling. However, achieving multi-attribute controllable and\nrealistic 3D human avatar generation is still challenging due to feature\ncoupling and the scarcity of realistic 3D human avatar datasets. To address\nthese issues, we propose Text2Avatar, which can generate realistic-style 3D\navatars based on the coupled text prompts. Text2Avatar leverages a discrete\ncodebook as an intermediate feature to establish a connection between text and\navatars, enabling the disentanglement of features. Furthermore, to alleviate\nthe scarcity of realistic style 3D human avatar data, we utilize a pre-trained\nunconditional 3D human avatar generation model to obtain a large amount of 3D\navatar pseudo data, which allows Text2Avatar to achieve realistic style\ngeneration. Experimental results demonstrate that our method can generate\nrealistic 3D avatars from coupled textual data, which is challenging for other\nexisting methods in this field.\n", "date": "2024-01-01", "categories": []}
{"id": "2403.13289", "title": "Text-to-3D Shape Generation", "abstract": "  Recent years have seen an explosion of work and interest in text-to-3D shape\ngeneration. Much of the progress is driven by advances in 3D representations,\nlarge-scale pretraining and representation learning for text and image data\nenabling generative AI models, and differentiable rendering. Computational\nsystems that can perform text-to-3D shape generation have captivated the\npopular imagination as they enable non-expert users to easily create 3D content\ndirectly from text. However, there are still many limitations and challenges\nremaining in this problem space. In this state-of-the-art report, we provide a\nsurvey of the underlying technology and methods enabling text-to-3D shape\ngeneration to summarize the background literature. We then derive a systematic\ncategorization of recent work on text-to-3D shape generation based on the type\nof supervision data required. Finally, we discuss limitations of the existing\ncategories of methods, and delineate promising directions for future work.\n", "date": "2024-03-20", "categories": []}
{"id": "2403.13524", "title": "Compress3D: a Compressed Latent Space for 3D Generation from a Single\n  Image", "abstract": "  3D generation has witnessed significant advancements, yet efficiently\nproducing high-quality 3D assets from a single image remains challenging. In\nthis paper, we present a triplane autoencoder, which encodes 3D models into a\ncompact triplane latent space to effectively compress both the 3D geometry and\ntexture information. Within the autoencoder framework, we introduce a 3D-aware\ncross-attention mechanism, which utilizes low-resolution latent representations\nto query features from a high-resolution 3D feature volume, thereby enhancing\nthe representation capacity of the latent space. Subsequently, we train a\ndiffusion model on this refined latent space. In contrast to solely relying on\nimage embedding for 3D generation, our proposed method advocates for the\nsimultaneous utilization of both image embedding and shape embedding as\nconditions. Specifically, the shape embedding is estimated via a diffusion\nprior model conditioned on the image embedding. Through comprehensive\nexperiments, we demonstrate that our method outperforms state-of-the-art\nalgorithms, achieving superior performance while requiring less training data\nand time. Our approach enables the generation of high-quality 3D assets in\nmerely 7 seconds on a single A100 GPU.\n", "date": "2024-03-20", "categories": []}
{"id": "2402.01166", "title": "A Comprehensive Survey on 3D Content Generation", "abstract": "  Recent years have witnessed remarkable advances in artificial intelligence\ngenerated content(AIGC), with diverse input modalities, e.g., text, image,\nvideo, audio and 3D. The 3D is the most close visual modality to real-world 3D\nenvironment and carries enormous knowledge. The 3D content generation shows\nboth academic and practical values while also presenting formidable technical\nchallenges. This review aims to consolidate developments within the burgeoning\ndomain of 3D content generation. Specifically, a new taxonomy is proposed that\ncategorizes existing approaches into three types: 3D native generative methods,\n2D prior-based 3D generative methods, and hybrid 3D generative methods. The\nsurvey covers approximately 60 papers spanning the major techniques. Besides,\nwe discuss limitations of current 3D content generation techniques, and point\nout open challenges as well as promising directions for future work.\nAccompanied with this survey, we have established a project website where the\nresources on 3D content generation research are provided. The project page is\navailable at https://github.com/hitcslj/Awesome-AIGC-3D.\n", "date": "2024-02-02", "categories": []}
{"id": "2403.12032", "title": "Generic 3D Diffusion Adapter Using Controlled Multi-View Editing", "abstract": "  Open-domain 3D object synthesis has been lagging behind image synthesis due\nto limited data and higher computational complexity. To bridge this gap, recent\nworks have investigated multi-view diffusion but often fall short in either 3D\nconsistency, visual quality, or efficiency. This paper proposes MVEdit, which\nfunctions as a 3D counterpart of SDEdit, employing ancestral sampling to\njointly denoise multi-view images and output high-quality textured meshes.\nBuilt on off-the-shelf 2D diffusion models, MVEdit achieves 3D consistency\nthrough a training-free 3D Adapter, which lifts the 2D views of the last\ntimestep into a coherent 3D representation, then conditions the 2D views of the\nnext timestep using rendered views, without uncompromising visual quality. With\nan inference time of only 2-5 minutes, this framework achieves better trade-off\nbetween quality and speed than score distillation. MVEdit is highly versatile\nand extendable, with a wide range of applications including text/image-to-3D\ngeneration, 3D-to-3D editing, and high-quality texture synthesis. In\nparticular, evaluations demonstrate state-of-the-art performance in both\nimage-to-3D and text-guided texture generation tasks. Additionally, we\nintroduce a method for fine-tuning 2D latent diffusion models on small 3D\ndatasets with limited resources, enabling fast low-resolution text-to-3D\ninitialization.\n", "date": "2024-03-18", "categories": []}
{"id": "2403.12409", "title": "ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware\n  Diffusion Guidance", "abstract": "  Generating high-quality 3D assets from a given image is highly desirable in\nvarious applications such as AR/VR. Recent advances in single-image 3D\ngeneration explore feed-forward models that learn to infer the 3D model of an\nobject without optimization. Though promising results have been achieved in\nsingle object generation, these methods often struggle to model complex 3D\nassets that inherently contain multiple objects. In this work, we present\nComboVerse, a 3D generation framework that produces high-quality 3D assets with\ncomplex compositions by learning to combine multiple models. 1) We first\nperform an in-depth analysis of this ``multi-object gap'' from both model and\ndata perspectives. 2) Next, with reconstructed 3D models of different objects,\nwe seek to adjust their sizes, rotation angles, and locations to create a 3D\nasset that matches the given image. 3) To automate this process, we apply\nspatially-aware score distillation sampling (SSDS) from pretrained diffusion\nmodels to guide the positioning of objects. Our proposed framework emphasizes\nspatial alignment of objects, compared with standard score distillation\nsampling, and thus achieves more accurate results. Extensive experiments\nvalidate ComboVerse achieves clear improvements over existing methods in\ngenerating compositional 3D assets.\n", "date": "2024-03-19", "categories": []}
{"id": "2403.12438", "title": "Precise-Physics Driven Text-to-3D Generation", "abstract": "  Text-to-3D generation has shown great promise in generating novel 3D content\nbased on given text prompts. However, existing generative methods mostly focus\non geometric or visual plausibility while ignoring precise physics perception\nfor the generated 3D shapes. This greatly hinders the practicality of generated\n3D shapes in real-world applications. In this work, we propose Phy3DGen, a\nprecise-physics-driven text-to-3D generation method. By analyzing the solid\nmechanics of generated 3D shapes, we reveal that the 3D shapes generated by\nexisting text-to-3D generation methods are impractical for real-world\napplications as the generated 3D shapes do not conform to the laws of physics.\nTo this end, we leverage 3D diffusion models to provide 3D shape priors and\ndesign a data-driven differentiable physics layer to optimize 3D shape priors\nwith solid mechanics. This allows us to optimize geometry efficiently and learn\nprecise physics information about 3D shapes at the same time. Experimental\nresults demonstrate that our method can consider both geometric plausibility\nand precise physics perception, further bridging 3D virtual modeling and\nprecise physical worlds.\n", "date": "2024-03-19", "categories": []}
{"id": "2310.11784", "title": "Progressive3D: Progressively Local Editing for Text-to-3D Content\n  Creation with Complex Semantic Prompts", "abstract": "  Recent text-to-3D generation methods achieve impressive 3D content creation\ncapacity thanks to the advances in image diffusion models and optimizing\nstrategies. However, current methods struggle to generate correct 3D content\nfor a complex prompt in semantics, i.e., a prompt describing multiple\ninteracted objects binding with different attributes. In this work, we propose\na general framework named Progressive3D, which decomposes the entire generation\ninto a series of locally progressive editing steps to create precise 3D content\nfor complex prompts, and we constrain the content change to only occur in\nregions determined by user-defined region prompts in each editing step.\nFurthermore, we propose an overlapped semantic component suppression technique\nto encourage the optimization process to focus more on the semantic differences\nbetween prompts. Extensive experiments demonstrate that the proposed\nProgressive3D framework generates precise 3D content for prompts with complex\nsemantics and is general for various text-to-3D methods driven by different 3D\nrepresentations.\n", "date": "2023-10-18", "categories": []}
{"id": "2403.11990", "title": "GetMesh: A Controllable Model for High-quality Mesh Generation and\n  Manipulation", "abstract": "  Mesh is a fundamental representation of 3D assets in various industrial\napplications, and is widely supported by professional softwares. However, due\nto its irregular structure, mesh creation and manipulation is often\ntime-consuming and labor-intensive. In this paper, we propose a highly\ncontrollable generative model, GetMesh, for mesh generation and manipulation\nacross different categories. By taking a varying number of points as the latent\nrepresentation, and re-organizing them as triplane representation, GetMesh\ngenerates meshes with rich and sharp details, outperforming both\nsingle-category and multi-category counterparts. Moreover, it also enables\nfine-grained control over the generation process that previous mesh generative\nmodels cannot achieve, where changing global/local mesh topologies,\nadding/removing mesh parts, and combining mesh parts across categories can be\nintuitively, efficiently, and robustly accomplished by adjusting the number,\npositions or features of latent points. Project page is\nhttps://getmesh.github.io.\n", "date": "2024-03-18", "categories": []}
{"id": "2403.12008", "title": "SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image\n  using Latent Video Diffusion", "abstract": "  We present Stable Video 3D (SV3D) -- a latent video diffusion model for\nhigh-resolution, image-to-multi-view generation of orbital videos around a 3D\nobject. Recent work on 3D generation propose techniques to adapt 2D generative\nmodels for novel view synthesis (NVS) and 3D optimization. However, these\nmethods have several disadvantages due to either limited views or inconsistent\nNVS, thereby affecting the performance of 3D object generation. In this work,\nwe propose SV3D that adapts image-to-video diffusion model for novel multi-view\nsynthesis and 3D generation, thereby leveraging the generalization and\nmulti-view consistency of the video models, while further adding explicit\ncamera control for NVS. We also propose improved 3D optimization techniques to\nuse SV3D and its NVS outputs for image-to-3D generation. Extensive experimental\nresults on multiple datasets with 2D and 3D metrics as well as user study\ndemonstrate SV3D's state-of-the-art performance on NVS as well as 3D\nreconstruction compared to prior works.\n", "date": "2024-03-18", "categories": []}
{"id": "2403.10395", "title": "Isotropic3D: Image-to-3D Generation Based on a Single CLIP Embedding", "abstract": "  Encouraged by the growing availability of pre-trained 2D diffusion models,\nimage-to-3D generation by leveraging Score Distillation Sampling (SDS) is\nmaking remarkable progress. Most existing methods combine novel-view lifting\nfrom 2D diffusion models which usually take the reference image as a condition\nwhile applying hard L2 image supervision at the reference view. Yet heavily\nadhering to the image is prone to corrupting the inductive knowledge of the 2D\ndiffusion model leading to flat or distorted 3D generation frequently. In this\nwork, we reexamine image-to-3D in a novel perspective and present Isotropic3D,\nan image-to-3D generation pipeline that takes only an image CLIP embedding as\ninput. Isotropic3D allows the optimization to be isotropic w.r.t. the azimuth\nangle by solely resting on the SDS loss. The core of our framework lies in a\ntwo-stage diffusion model fine-tuning. Firstly, we fine-tune a text-to-3D\ndiffusion model by substituting its text encoder with an image encoder, by\nwhich the model preliminarily acquires image-to-image capabilities. Secondly,\nwe perform fine-tuning using our Explicit Multi-view Attention (EMA) which\ncombines noisy multi-view images with the noise-free reference image as an\nexplicit condition. CLIP embedding is sent to the diffusion model throughout\nthe whole process while reference images are discarded once after fine-tuning.\nAs a result, with a single image CLIP embedding, Isotropic3D is capable of\ngenerating multi-view mutually consistent images and also a 3D model with more\nsymmetrical and neat content, well-proportioned geometry, rich colored texture,\nand less distortion compared with existing image-to-3D methods while still\npreserving the similarity to the reference image to a large extent. The project\npage is available at https://isotropic3d.github.io/. The code and models are\navailable at https://github.com/pkunliu/Isotropic3D.\n", "date": "2024-03-15", "categories": []}
{"id": "2309.17175", "title": "TextField3D: Towards Enhancing Open-Vocabulary 3D Generation with Noisy\n  Text Fields", "abstract": "  Recent works learn 3D representation explicitly under text-3D guidance.\nHowever, limited text-3D data restricts the vocabulary scale and text control\nof generations. Generators may easily fall into a stereotype concept for\ncertain text prompts, thus losing open-vocabulary generation ability. To tackle\nthis issue, we introduce a conditional 3D generative model, namely TextField3D.\nSpecifically, rather than using the text prompts as input directly, we suggest\nto inject dynamic noise into the latent space of given text prompts, i.e.,\nNoisy Text Fields (NTFs). In this way, limited 3D data can be mapped to the\nappropriate range of textual latent space that is expanded by NTFs. To this\nend, an NTFGen module is proposed to model general text latent code in noisy\nfields. Meanwhile, an NTFBind module is proposed to align view-invariant image\nlatent code to noisy fields, further supporting image-conditional 3D\ngeneration. To guide the conditional generation in both geometry and texture,\nmulti-modal discrimination is constructed with a text-3D discriminator and a\ntext-2.5D discriminator. Compared to previous methods, TextField3D includes\nthree merits: 1) large vocabulary, 2) text consistency, and 3) low latency.\nExtensive experiments demonstrate that our method achieves a potential\nopen-vocabulary 3D generation capability.\n", "date": "2023-09-29", "categories": []}
{"id": "2311.17061", "title": "HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting", "abstract": "  Realistic 3D human generation from text prompts is a desirable yet\nchallenging task. Existing methods optimize 3D representations like mesh or\nneural fields via score distillation sampling (SDS), which suffers from\ninadequate fine details or excessive training time. In this paper, we propose\nan efficient yet effective framework, HumanGaussian, that generates\nhigh-quality 3D humans with fine-grained geometry and realistic appearance. Our\nkey insight is that 3D Gaussian Splatting is an efficient renderer with\nperiodic Gaussian shrinkage or growing, where such adaptive density control can\nbe naturally guided by intrinsic human structures. Specifically, 1) we first\npropose a Structure-Aware SDS that simultaneously optimizes human appearance\nand geometry. The multi-modal score function from both RGB and depth space is\nleveraged to distill the Gaussian densification and pruning process. 2)\nMoreover, we devise an Annealed Negative Prompt Guidance by decomposing SDS\ninto a noisier generative score and a cleaner classifier score, which well\naddresses the over-saturation issue. The floating artifacts are further\neliminated based on Gaussian size in a prune-only phase to enhance generation\nsmoothness. Extensive experiments demonstrate the superior efficiency and\ncompetitive quality of our framework, rendering vivid 3D humans under diverse\nscenarios. Project Page: https://alvinliu0.github.io/projects/HumanGaussian\n", "date": "2023-11-28", "categories": []}
{"id": "2403.08902", "title": "Envision3D: One Image to 3D with Anchor Views Interpolation", "abstract": "  We present Envision3D, a novel method for efficiently generating high-quality\n3D content from a single image. Recent methods that extract 3D content from\nmulti-view images generated by diffusion models show great potential. However,\nit is still challenging for diffusion models to generate dense multi-view\nconsistent images, which is crucial for the quality of 3D content extraction.\nTo address this issue, we propose a novel cascade diffusion framework, which\ndecomposes the challenging dense views generation task into two tractable\nstages, namely anchor views generation and anchor views interpolation. In the\nfirst stage, we train the image diffusion model to generate global consistent\nanchor views conditioning on image-normal pairs. Subsequently, leveraging our\nvideo diffusion model fine-tuned on consecutive multi-view images, we conduct\ninterpolation on the previous anchor views to generate extra dense views. This\nframework yields dense, multi-view consistent images, providing comprehensive\n3D information. To further enhance the overall generation quality, we introduce\na coarse-to-fine sampling strategy for the reconstruction algorithm to robustly\nextract textured meshes from the generated dense images. Extensive experiments\ndemonstrate that our method is capable of generating high-quality 3D content in\nterms of texture and geometry, surpassing previous image-to-3D baseline\nmethods.\n", "date": "2024-03-13", "categories": []}
{"id": "2403.09140", "title": "Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D\n  Prior", "abstract": "  Recent works on text-to-3d generation show that using only 2D diffusion\nsupervision for 3D generation tends to produce results with inconsistent\nappearances (e.g., faces on the back view) and inaccurate shapes (e.g., animals\nwith extra legs). Existing methods mainly address this issue by retraining\ndiffusion models with images rendered from 3D data to ensure multi-view\nconsistency while struggling to balance 2D generation quality with 3D\nconsistency. In this paper, we present a new framework Sculpt3D that equips the\ncurrent pipeline with explicit injection of 3D priors from retrieved reference\nobjects without re-training the 2D diffusion model. Specifically, we\ndemonstrate that high-quality and diverse 3D geometry can be guaranteed by\nkeypoints supervision through a sparse ray sampling approach. Moreover, to\nensure accurate appearances of different views, we further modulate the output\nof the 2D diffusion model to the correct patterns of the template views without\naltering the generated object's style. These two decoupled designs effectively\nharness 3D information from reference objects to generate 3D objects while\npreserving the generation quality of the 2D diffusion model. Extensive\nexperiments show our method can largely improve the multi-view consistency\nwhile retaining fidelity and diversity. Our project page is available at:\nhttps://stellarcheng.github.io/Sculpt3D/.\n", "date": "2024-03-14", "categories": []}
{"id": "2403.09625", "title": "Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation", "abstract": "  Recent years have witnessed the strong power of 3D generation models, which\noffer a new level of creative flexibility by allowing users to guide the 3D\ncontent generation process through a single image or natural language. However,\nit remains challenging for existing 3D generation methods to create\nsubject-driven 3D content across diverse prompts. In this paper, we introduce a\nnovel 3D customization method, dubbed Make-Your-3D that can personalize\nhigh-fidelity and consistent 3D content from only a single image of a subject\nwith text description within 5 minutes. Our key insight is to harmonize the\ndistributions of a multi-view diffusion model and an identity-specific 2D\ngenerative model, aligning them with the distribution of the desired 3D\nsubject. Specifically, we design a co-evolution framework to reduce the\nvariance of distributions, where each model undergoes a process of learning\nfrom the other through identity-aware optimization and subject-prior\noptimization, respectively. Extensive experiments demonstrate that our method\ncan produce high-quality, consistent, and subject-specific 3D content with\ntext-driven modifications that are unseen in subject image.\n", "date": "2024-03-14", "categories": []}
{"id": "2312.06439", "title": "DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior", "abstract": "  3D generation has raised great attention in recent years. With the success of\ntext-to-image diffusion models, the 2D-lifting technique becomes a promising\nroute to controllable 3D generation. However, these methods tend to present\ninconsistent geometry, which is also known as the Janus problem. We observe\nthat the problem is caused mainly by two aspects, i.e., viewpoint bias in 2D\ndiffusion models and overfitting of the optimization objective. To address it,\nwe propose a two-stage 2D-lifting framework, namely DreamControl, which\noptimizes coarse NeRF scenes as 3D self-prior and then generates fine-grained\nobjects with control-based score distillation. Specifically, adaptive viewpoint\nsampling and boundary integrity metric are proposed to ensure the consistency\nof generated priors. The priors are then regarded as input conditions to\nmaintain reasonable geometries, in which conditional LoRA and weighted score\nare further proposed to optimize detailed textures. DreamControl can generate\nhigh-quality 3D content in terms of both geometry consistency and texture\nfidelity. Moreover, our control-based optimization guidance is applicable to\nmore downstream tasks, including user-guided generation and 3D animation. The\nproject page is available at https://github.com/tyhuang0428/DreamControl.\n", "date": "2023-12-11", "categories": []}
{"id": "2305.18766", "title": "HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion\n  Guidance", "abstract": "  The advancements in automatic text-to-3D generation have been remarkable.\nMost existing methods use pre-trained text-to-image diffusion models to\noptimize 3D representations like Neural Radiance Fields (NeRFs) via\nlatent-space denoising score matching. Yet, these methods often result in\nartifacts and inconsistencies across different views due to their suboptimal\noptimization approaches and limited understanding of 3D geometry. Moreover, the\ninherent constraints of NeRFs in rendering crisp geometry and stable textures\nusually lead to a two-stage optimization to attain high-resolution details.\nThis work proposes holistic sampling and smoothing approaches to achieve\nhigh-quality text-to-3D generation, all in a single-stage optimization. We\ncompute denoising scores in the text-to-image diffusion model's latent and\nimage spaces. Instead of randomly sampling timesteps (also referred to as noise\nlevels in denoising score matching), we introduce a novel timestep annealing\napproach that progressively reduces the sampled timestep throughout\noptimization. To generate high-quality renderings in a single-stage\noptimization, we propose regularization for the variance of z-coordinates along\nNeRF rays. To address texture flickering issues in NeRFs, we introduce a kernel\nsmoothing technique that refines importance sampling weights coarse-to-fine,\nensuring accurate and thorough sampling in high-density regions. Extensive\nexperiments demonstrate the superiority of our method over previous approaches,\nenabling the generation of highly detailed and view-consistent 3D assets\nthrough a single-stage training process.\n", "date": "2023-05-30", "categories": []}
{"id": "2403.06317", "title": "An End-to-End Deep Learning Generative Framework for Refinable Shape\n  Matching and Generation", "abstract": "  Generative modelling for shapes is a prerequisite for In-Silico Clinical\nTrials (ISCTs), which aim to cost-effectively validate medical device\ninterventions using synthetic anatomical shapes, often represented as 3D\nsurface meshes. However, constructing AI models to generate shapes closely\nresembling the real mesh samples is challenging due to variable vertex counts,\nconnectivities, and the lack of dense vertex-wise correspondences across the\ntraining data. Employing graph representations for meshes, we develop a novel\nunsupervised geometric deep-learning model to establish refinable shape\ncorrespondences in a latent space, construct a population-derived atlas and\ngenerate realistic synthetic shapes. We additionally extend our proposed base\nmodel to a joint shape generative-clustering multi-atlas framework to\nincorporate further variability and preserve more details in the generated\nshapes. Experimental results using liver and left-ventricular models\ndemonstrate the approach's applicability to computational medicine,\nhighlighting its suitability for ISCTs through a comparative analysis.\n", "date": "2024-03-10", "categories": []}
{"id": "2403.06470", "title": "3D-aware Image Generation and Editing with Multi-modal Conditions", "abstract": "  3D-consistent image generation from a single 2D semantic label is an\nimportant and challenging research topic in computer graphics and computer\nvision. Although some related works have made great progress in this field,\nmost of the existing methods suffer from poor disentanglement performance of\nshape and appearance, and lack multi-modal control. In this paper, we propose a\nnovel end-to-end 3D-aware image generation and editing model incorporating\nmultiple types of conditional inputs, including pure noise, text and reference\nimage. On the one hand, we dive into the latent space of 3D Generative\nAdversarial Networks (GANs) and propose a novel disentanglement strategy to\nseparate appearance features from shape features during the generation process.\nOn the other hand, we propose a unified framework for flexible image generation\nand editing tasks with multi-modal conditions. Our method can generate diverse\nimages with distinct noises, edit the attribute through a text description and\nconduct style transfer by giving a reference RGB image. Extensive experiments\ndemonstrate that the proposed method outperforms alternative approaches both\nqualitatively and quantitatively on image generation and editing.\n", "date": "2024-03-11", "categories": []}
{"id": "2403.06738", "title": "V3D: Video Diffusion Models are Effective 3D Generators", "abstract": "  Automatic 3D generation has recently attracted widespread attention. Recent\nmethods have greatly accelerated the generation speed, but usually produce\nless-detailed objects due to limited model capacity or 3D data. Motivated by\nrecent advancements in video diffusion models, we introduce V3D, which\nleverages the world simulation capacity of pre-trained video diffusion models\nto facilitate 3D generation. To fully unleash the potential of video diffusion\nto perceive the 3D world, we further introduce geometrical consistency prior\nand extend the video diffusion model to a multi-view consistent 3D generator.\nBenefiting from this, the state-of-the-art video diffusion model could be\nfine-tuned to generate 360degree orbit frames surrounding an object given a\nsingle image. With our tailored reconstruction pipelines, we can generate\nhigh-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method\ncan be extended to scene-level novel view synthesis, achieving precise control\nover the camera path with sparse input views. Extensive experiments demonstrate\nthe superior performance of the proposed approach, especially in terms of\ngeneration quality and multi-view consistency. Our code is available at\nhttps://github.com/heheyas/V3D\n", "date": "2024-03-11", "categories": []}
{"id": "2403.05034", "title": "CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction\n  Model", "abstract": "  Feed-forward 3D generative models like the Large Reconstruction Model (LRM)\nhave demonstrated exceptional generation speed. However, the transformer-based\nmethods do not leverage the geometric priors of the triplane component in their\narchitecture, often leading to sub-optimal quality given the limited size of 3D\ndata and slow training. In this work, we present the Convolutional\nReconstruction Model (CRM), a high-fidelity feed-forward single image-to-3D\ngenerative model. Recognizing the limitations posed by sparse 3D data, we\nhighlight the necessity of integrating geometric priors into network design.\nCRM builds on the key observation that the visualization of triplane exhibits\nspatial correspondence of six orthographic images. First, it generates six\northographic view images from a single input image, then feeds these images\ninto a convolutional U-Net, leveraging its strong pixel-level alignment\ncapabilities and significant bandwidth to create a high-resolution triplane.\nCRM further employs Flexicubes as geometric representation, facilitating direct\nend-to-end optimization on textured meshes. Overall, our model delivers a\nhigh-fidelity textured mesh from an image in just 10 seconds, without any\ntest-time optimization.\n", "date": "2024-03-08", "categories": []}
{"id": "2402.17115", "title": "CharNeRF: 3D Character Generation from Concept Art", "abstract": "  3D modeling holds significant importance in the realms of AR/VR and gaming,\nallowing for both artistic creativity and practical applications. However, the\nprocess is often time-consuming and demands a high level of skill. In this\npaper, we present a novel approach to create volumetric representations of 3D\ncharacters from consistent turnaround concept art, which serves as the standard\ninput in the 3D modeling industry. While Neural Radiance Field (NeRF) has been\na game-changer in image-based 3D reconstruction, to the best of our knowledge,\nthere is no known research that optimizes the pipeline for concept art. To\nharness the potential of concept art, with its defined body poses and specific\nview angles, we propose encoding it as priors for our model. We train the\nnetwork to make use of these priors for various 3D points through a learnable\nview-direction-attended multi-head self-attention layer. Additionally, we\ndemonstrate that a combination of ray sampling and surface sampling enhances\nthe inference capabilities of our network. Our model is able to generate\nhigh-quality 360-degree views of characters. Subsequently, we provide a simple\nguideline to better leverage our model to extract the 3D mesh. It is important\nto note that our model's inferencing capabilities are influenced by the\ntraining data's characteristics, primarily focusing on characters with a single\nhead, two arms, and two legs. Nevertheless, our methodology remains versatile\nand adaptable to concept art from diverse subject matters, without imposing any\nspecific assumptions on the data.\n", "date": "2024-02-27", "categories": []}
{"id": "2402.17292", "title": "DivAvatar: Diverse 3D Avatar Generation with a Single Prompt", "abstract": "  Text-to-Avatar generation has recently made significant strides due to\nadvancements in diffusion models. However, most existing work remains\nconstrained by limited diversity, producing avatars with subtle differences in\nappearance for a given text prompt. We design DivAvatar, a novel framework that\ngenerates diverse avatars, empowering 3D creatives with a multitude of distinct\nand richly varied 3D avatars from a single text prompt. Different from most\nexisting work that exploits scene-specific 3D representations such as NeRF,\nDivAvatar finetunes a 3D generative model (i.e., EVA3D), allowing diverse\navatar generation from simply noise sampling in inference time. DivAvatar has\ntwo key designs that help achieve generation diversity and visual quality. The\nfirst is a noise sampling technique during training phase which is critical in\ngenerating diverse appearances. The second is a semantic-aware zoom mechanism\nand a novel depth loss, the former producing appearances of high textual\nfidelity by separate fine-tuning of specific body parts and the latter\nimproving geometry quality greatly by smoothing the generated mesh in the\nfeatures space. Extensive experiments show that DivAvatar is highly versatile\nin generating avatars of diverse appearances.\n", "date": "2024-02-27", "categories": []}
{"id": "2211.09869", "title": "RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and\n  Generation", "abstract": "  Diffusion models currently achieve state-of-the-art performance for both\nconditional and unconditional image generation. However, so far, image\ndiffusion models do not support tasks required for 3D understanding, such as\nview-consistent 3D generation or single-view object reconstruction. In this\npaper, we present RenderDiffusion, the first diffusion model for 3D generation\nand inference, trained using only monocular 2D supervision. Central to our\nmethod is a novel image denoising architecture that generates and renders an\nintermediate three-dimensional representation of a scene in each denoising\nstep. This enforces a strong inductive structure within the diffusion process,\nproviding a 3D consistent representation while only requiring 2D supervision.\nThe resulting 3D representation can be rendered from any view. We evaluate\nRenderDiffusion on FFHQ, AFHQ, ShapeNet and CLEVR datasets, showing competitive\nperformance for generation of 3D scenes and inference of 3D scenes from 2D\nimages. Additionally, our diffusion-based approach allows us to use 2D\ninpainting to edit 3D scenes.\n", "date": "2022-11-17", "categories": []}
{"id": "2305.15399", "title": "Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape", "abstract": "  Synthesizing novel 3D models that resemble the input example has long been\npursued by graphics artists and machine learning researchers. In this paper, we\npresent Sin3DM, a diffusion model that learns the internal patch distribution\nfrom a single 3D textured shape and generates high-quality variations with fine\ngeometry and texture details. Training a diffusion model directly in 3D would\ninduce large memory and computational cost. Therefore, we first compress the\ninput into a lower-dimensional latent space and then train a diffusion model on\nit. Specifically, we encode the input 3D textured shape into triplane feature\nmaps that represent the signed distance and texture fields of the input. The\ndenoising network of our diffusion model has a limited receptive field to avoid\noverfitting, and uses triplane-aware 2D convolution blocks to improve the\nresult quality. Aside from randomly generating new samples, our model also\nfacilitates applications such as retargeting, outpainting and local editing.\nThrough extensive qualitative and quantitative evaluation, we show that our\nmethod outperforms prior methods in generation quality of 3D shapes.\n", "date": "2023-05-24", "categories": []}
{"id": "2306.06088", "title": "SENS: Part-Aware Sketch-based Implicit Neural Shape Modeling", "abstract": "  We present SENS, a novel method for generating and editing 3D models from\nhand-drawn sketches, including those of abstract nature. Our method allows\nusers to quickly and easily sketch a shape, and then maps the sketch into the\nlatent space of a part-aware neural implicit shape architecture. SENS analyzes\nthe sketch and encodes its parts into ViT patch encoding, subsequently feeding\nthem into a transformer decoder that converts them to shape embeddings suitable\nfor editing 3D neural implicit shapes. SENS provides intuitive sketch-based\ngeneration and editing, and also succeeds in capturing the intent of the user's\nsketch to generate a variety of novel and expressive 3D shapes, even from\nabstract and imprecise sketches. Additionally, SENS supports refinement via\npart reconstruction, allowing for nuanced adjustments and artifact removal. It\nalso offers part-based modeling capabilities, enabling the combination of\nfeatures from multiple sketches to create more complex and customized 3D\nshapes. We demonstrate the effectiveness of our model compared to the\nstate-of-the-art using objective metric evaluation criteria and a user study,\nboth indicating strong performance on sketches with a medium level of\nabstraction. Furthermore, we showcase our method's intuitive sketch-based shape\nediting capabilities, and validate it through a usability study.\n", "date": "2023-06-09", "categories": []}
{"id": "2402.09052", "title": "L3GO: Language Agents with Chain-of-3D-Thoughts for Generating\n  Unconventional Objects", "abstract": "  Diffusion-based image generation models such as DALL-E 3 and Stable\nDiffusion-XL demonstrate remarkable capabilities in generating images with\nrealistic and unique compositions. Yet, these models are not robust in\nprecisely reasoning about physical and spatial configurations of objects,\nespecially when instructed with unconventional, thereby out-of-distribution\ndescriptions, such as \"a chair with five legs\". In this paper, we propose a\nlanguage agent with chain-of-3D-thoughts (L3GO), an inference-time approach\nthat can reason about part-based 3D mesh generation of unconventional objects\nthat current data-driven diffusion models struggle with. More concretely, we\nuse large language models as agents to compose a desired object via\ntrial-and-error within the 3D simulation environment. To facilitate our\ninvestigation, we develop a new benchmark, Unconventionally Feasible Objects\n(UFO), as well as SimpleBlenv, a wrapper environment built on top of Blender\nwhere language agents can build and compose atomic building blocks via API\ncalls. Human and automatic GPT-4V evaluations show that our approach surpasses\nthe standard GPT-4 and other language agents (e.g., ReAct and Reflexion) for 3D\nmesh generation on ShapeNet. Moreover, when tested on our UFO benchmark, our\napproach outperforms other state-of-the-art text-to-2D image and text-to-3D\nmodels based on human evaluation.\n", "date": "2024-02-14", "categories": []}
{"id": "2402.08682", "title": "IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality\n  3D Generation", "abstract": "  Most text-to-3D generators build upon off-the-shelf text-to-image models\ntrained on billions of images. They use variants of Score Distillation Sampling\n(SDS), which is slow, somewhat unstable, and prone to artifacts. A mitigation\nis to fine-tune the 2D generator to be multi-view aware, which can help\ndistillation or can be combined with reconstruction networks to output 3D\nobjects directly. In this paper, we further explore the design space of\ntext-to-3D models. We significantly improve multi-view generation by\nconsidering video instead of image generators. Combined with a 3D\nreconstruction algorithm which, by using Gaussian splatting, can optimize a\nrobust image-based loss, we directly produce high-quality 3D outputs from the\ngenerated views. Our new method, IM-3D, reduces the number of evaluations of\nthe 2D generator network 10-100x, resulting in a much more efficient pipeline,\nbetter quality, fewer geometric inconsistencies, and higher yield of usable 3D\nassets.\n", "date": "2024-02-13", "categories": []}
{"id": "2402.05803", "title": "AvatarMMC: 3D Head Avatar Generation and Editing with Multi-Modal\n  Conditioning", "abstract": "  We introduce an approach for 3D head avatar generation and editing with\nmulti-modal conditioning based on a 3D Generative Adversarial Network (GAN) and\na Latent Diffusion Model (LDM). 3D GANs can generate high-quality head avatars\ngiven a single or no condition. However, it is challenging to generate samples\nthat adhere to multiple conditions of different modalities. On the other hand,\nLDMs excel at learning complex conditional distributions. To this end, we\npropose to exploit the conditioning capabilities of LDMs to enable multi-modal\ncontrol over the latent space of a pre-trained 3D GAN. Our method can generate\nand edit 3D head avatars given a mixture of control signals such as RGB input,\nsegmentation masks, and global attributes. This provides better control over\nthe generation and editing of synthetic avatars both globally and locally.\nExperiments show that our proposed approach outperforms a solely GAN-based\napproach both qualitatively and quantitatively on generation and editing tasks.\nTo the best of our knowledge, our approach is the first to introduce\nmulti-modal conditioning to 3D avatar generation and editing.\n\\\\href{avatarmmc-sig24.github.io}{Project Page}\n", "date": "2024-02-08", "categories": []}
{"id": "2312.09305", "title": "Stable Score Distillation for High-Quality 3D Generation", "abstract": "  Although Score Distillation Sampling (SDS) has exhibited remarkable\nperformance in conditional 3D content generation, a comprehensive understanding\nof its formulation is still lacking, hindering the development of 3D\ngeneration. In this work, we decompose SDS as a combination of three functional\ncomponents, namely mode-seeking, mode-disengaging and variance-reducing terms,\nanalyzing the properties of each. We show that problems such as over-smoothness\nand implausibility result from the intrinsic deficiency of the first two terms\nand propose a more advanced variance-reducing term than that introduced by SDS.\nBased on the analysis, we propose a simple yet effective approach named Stable\nScore Distillation (SSD) which strategically orchestrates each term for\nhigh-quality 3D generation and can be readily incorporated to various 3D\ngeneration frameworks and 3D representations. Extensive experiments validate\nthe efficacy of our approach, demonstrating its ability to generate\nhigh-fidelity 3D content without succumbing to issues such as over-smoothness.\n", "date": "2023-12-14", "categories": []}
{"id": "2402.05054", "title": "LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content\n  Creation", "abstract": "  3D content creation has achieved significant progress in terms of both\nquality and speed. Although current feed-forward models can produce 3D objects\nin seconds, their resolution is constrained by the intensive computation\nrequired during training. In this paper, we introduce Large Multi-View Gaussian\nModel (LGM), a novel framework designed to generate high-resolution 3D models\nfrom text prompts or single-view images. Our key insights are two-fold: 1) 3D\nRepresentation: We propose multi-view Gaussian features as an efficient yet\npowerful representation, which can then be fused together for differentiable\nrendering. 2) 3D Backbone: We present an asymmetric U-Net as a high-throughput\nbackbone operating on multi-view images, which can be produced from text or\nsingle-view image input by leveraging multi-view diffusion models. Extensive\nexperiments demonstrate the high fidelity and efficiency of our approach.\nNotably, we maintain the fast speed to generate 3D objects within 5 seconds\nwhile boosting the training resolution to 512, thereby achieving\nhigh-resolution 3D content generation.\n", "date": "2024-02-07", "categories": []}
{"id": "2303.07937", "title": "Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D\n  Generation", "abstract": "  Text-to-3D generation has shown rapid progress in recent days with the advent\nof score distillation, a methodology of using pretrained text-to-2D diffusion\nmodels to optimize neural radiance field (NeRF) in the zero-shot setting.\nHowever, the lack of 3D awareness in the 2D diffusion models destabilizes score\ndistillation-based methods from reconstructing a plausible 3D scene. To address\nthis issue, we propose 3DFuse, a novel framework that incorporates 3D awareness\ninto pretrained 2D diffusion models, enhancing the robustness and 3D\nconsistency of score distillation-based methods. We realize this by first\nconstructing a coarse 3D structure of a given text prompt and then utilizing\nprojected, view-specific depth map as a condition for the diffusion model.\nAdditionally, we introduce a training strategy that enables the 2D diffusion\nmodel learns to handle the errors and sparsity within the coarse 3D structure\nfor robust generation, as well as a method for ensuring semantic consistency\nthroughout all viewpoints of the scene. Our framework surpasses the limitations\nof prior arts, and has significant implications for 3D consistent generation of\n2D diffusion models.\n", "date": "2023-03-14", "categories": []}
{"id": "2402.00225", "title": "Geometry aware 3D generation from in-the-wild images in ImageNet", "abstract": "  Generating accurate 3D models is a challenging problem that traditionally\nrequires explicit learning from 3D datasets using supervised learning. Although\nrecent advances have shown promise in learning 3D models from 2D images, these\nmethods often rely on well-structured datasets with multi-view images of each\ninstance or camera pose information. Furthermore, these datasets usually\ncontain clean backgrounds with simple shapes, making them expensive to acquire\nand hard to generalize, which limits the applicability of these methods. To\novercome these limitations, we propose a method for reconstructing 3D geometry\nfrom the diverse and unstructured Imagenet dataset without camera pose\ninformation. We use an efficient triplane representation to learn 3D models\nfrom 2D images and modify the architecture of the generator backbone based on\nStyleGAN2 to adapt to the highly diverse dataset. To prevent mode collapse and\nimprove the training stability on diverse data, we propose to use multi-view\ndiscrimination. The trained generator can produce class-conditional 3D models\nas well as renderings from arbitrary viewpoints. The class-conditional\ngeneration results demonstrate significant improvement over the current\nstate-of-the-art method. Additionally, using PTI, we can efficiently\nreconstruct the whole 3D geometry from single-view images.\n", "date": "2024-01-31", "categories": []}
{"id": "2402.01241", "title": "Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D\n  Diffusion?", "abstract": "  Recent advancements in deep generative models, particularly with the\napplication of CLIP (Contrastive Language Image Pretraining) to Denoising\nDiffusion Probabilistic Models (DDPMs), have demonstrated remarkable\neffectiveness in text to image generation. The well structured embedding space\nof CLIP has also been extended to image to shape generation with DDPMs,\nyielding notable results. Despite these successes, some fundamental questions\narise: Does CLIP ensure the best results in shape generation from images? Can\nwe leverage conditioning to bring explicit 3D knowledge into the generative\nprocess and obtain better quality? This study introduces CISP (Contrastive\nImage Shape Pre training), designed to enhance 3D shape synthesis guided by 2D\nimages. CISP aims to enrich the CLIP framework by aligning 2D images with 3D\nshapes in a shared embedding space, specifically capturing 3D characteristics\npotentially overlooked by CLIP's text image focus. Our comprehensive analysis\nassesses CISP's guidance performance against CLIP guided models, focusing on\ngeneration quality, diversity, and coherence of the produced shapes with the\nconditioning image. We find that, while matching CLIP in generation quality and\ndiversity, CISP substantially improves coherence with input images,\nunderscoring the value of incorporating 3D knowledge into generative models.\nThese findings suggest a promising direction for advancing the synthesis of 3D\nvisual content by integrating multimodal systems with 3D representations.\n", "date": "2024-02-02", "categories": []}
{"id": "2401.17603", "title": "Topology-Aware Latent Diffusion for 3D Shape Generation", "abstract": "  We introduce a new generative model that combines latent diffusion with\npersistent homology to create 3D shapes with high diversity, with a special\nemphasis on their topological characteristics. Our method involves representing\n3D shapes as implicit fields, then employing persistent homology to extract\ntopological features, including Betti numbers and persistence diagrams. The\nshape generation process consists of two steps. Initially, we employ a\ntransformer-based autoencoding module to embed the implicit representation of\neach 3D shape into a set of latent vectors. Subsequently, we navigate through\nthe learned latent space via a diffusion model. By strategically incorporating\ntopological features into the diffusion process, our generative module is able\nto produce a richer variety of 3D shapes with different topological structures.\nFurthermore, our framework is flexible, supporting generation tasks constrained\nby a variety of inputs, including sparse and partial point clouds, as well as\nsketches. By modifying the persistence diagrams, we can alter the topology of\nthe shapes generated from these input modalities.\n", "date": "2024-01-31", "categories": []}
{"id": "2401.17807", "title": "Advances in 3D Generation: A Survey", "abstract": "  Generating 3D models lies at the core of computer graphics and has been the\nfocus of decades of research. With the emergence of advanced neural\nrepresentations and generative models, the field of 3D content generation is\ndeveloping rapidly, enabling the creation of increasingly high-quality and\ndiverse 3D models. The rapid growth of this field makes it difficult to stay\nabreast of all recent developments. In this survey, we aim to introduce the\nfundamental methodologies of 3D generation methods and establish a structured\nroadmap, encompassing 3D representation, generation methods, datasets, and\ncorresponding applications. Specifically, we introduce the 3D representations\nthat serve as the backbone for 3D generation. Furthermore, we provide a\ncomprehensive overview of the rapidly growing literature on generation methods,\ncategorized by the type of algorithmic paradigms, including feedforward\ngeneration, optimization-based generation, procedural generation, and\ngenerative novel view synthesis. Lastly, we discuss available datasets,\napplications, and open challenges. We hope this survey will help readers\nexplore this exciting topic and foster further advancements in the field of 3D\ncontent generation.\n", "date": "2024-01-31", "categories": []}
{"id": "2401.14257", "title": "Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation", "abstract": "  Recently, text-to-3D approaches have achieved high-fidelity 3D content\ngeneration using text description. However, the generated objects are\nstochastic and lack fine-grained control. Sketches provide a cheap approach to\nintroduce such fine-grained control. Nevertheless, it is challenging to achieve\nflexible control from these sketches due to their abstraction and ambiguity. In\nthis paper, we present a multi-view sketch-guided text-to-3D generation\nframework (namely, Sketch2NeRF) to add sketch control to 3D generation.\nSpecifically, our method leverages pretrained 2D diffusion models (e.g., Stable\nDiffusion and ControlNet) to supervise the optimization of a 3D scene\nrepresented by a neural radiance field (NeRF). We propose a novel synchronized\ngeneration and reconstruction method to effectively optimize the NeRF. In the\nexperiments, we collected two kinds of multi-view sketch datasets to evaluate\nthe proposed method. We demonstrate that our method can synthesize 3D\nconsistent contents with fine-grained sketch control while being high-fidelity\nto text prompts. Extensive results show that our method achieves\nstate-of-the-art performance in terms of sketch similarity and text alignment.\n", "date": "2024-01-25", "categories": []}
{"id": "2310.12474", "title": "Enhancing High-Resolution 3D Generation through Pixel-wise Gradient\n  Clipping", "abstract": "  High-resolution 3D object generation remains a challenging task primarily due\nto the limited availability of comprehensive annotated training data. Recent\nadvancements have aimed to overcome this constraint by harnessing image\ngenerative models, pretrained on extensive curated web datasets, using\nknowledge transfer techniques like Score Distillation Sampling (SDS).\nEfficiently addressing the requirements of high-resolution rendering often\nnecessitates the adoption of latent representation-based models, such as the\nLatent Diffusion Model (LDM). In this framework, a significant challenge\narises: To compute gradients for individual image pixels, it is necessary to\nbackpropagate gradients from the designated latent space through the frozen\ncomponents of the image model, such as the VAE encoder used within LDM.\nHowever, this gradient propagation pathway has never been optimized, remaining\nuncontrolled during training. We find that the unregulated gradients adversely\naffect the 3D model's capacity in acquiring texture-related information from\nthe image generative model, leading to poor quality appearance synthesis. To\naddress this overarching challenge, we propose an innovative operation termed\nPixel-wise Gradient Clipping (PGC) designed for seamless integration into\nexisting 3D generative models, thereby enhancing their synthesis quality.\nSpecifically, we control the magnitude of stochastic gradients by clipping the\npixel-wise gradients efficiently, while preserving crucial texture-related\ngradient directions. Despite this simplicity and minimal extra cost, extensive\nexperiments demonstrate the efficacy of our PGC in enhancing the performance of\nexisting 3D generative models for high-resolution object rendering.\n", "date": "2023-10-19", "categories": []}
{"id": "2401.06437", "title": "3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp\n  Features and Parametric Control?", "abstract": "  Recent advancements in implicit 3D representations and generative models have\nmarkedly propelled the field of 3D object generation forward. However, it\nremains a significant challenge to accurately model geometries with defined\nsharp features under parametric controls, which is crucial in fields like\nindustrial design and manufacturing. To bridge this gap, we introduce a\nframework that employs Large Language Models (LLMs) to generate text-driven 3D\nshapes, manipulating 3D software via program synthesis. We present 3D-PreMise,\na dataset specifically tailored for 3D parametric modeling of industrial\nshapes, designed to explore state-of-the-art LLMs within our proposed pipeline.\nOur work reveals effective generation strategies and delves into the\nself-correction capabilities of LLMs using a visual interface. Our work\nhighlights both the potential and limitations of LLMs in 3D parametric modeling\nfor industrial applications.\n", "date": "2024-01-12", "categories": []}
{"id": "2401.07727", "title": "HexaGen3D: StableDiffusion is just one step away from Fast and Diverse\n  Text-to-3D Generation", "abstract": "  Despite the latest remarkable advances in generative modeling, efficient\ngeneration of high-quality 3D assets from textual prompts remains a difficult\ntask. A key challenge lies in data scarcity: the most extensive 3D datasets\nencompass merely millions of assets, while their 2D counterparts contain\nbillions of text-image pairs. To address this, we propose a novel approach\nwhich harnesses the power of large, pretrained 2D diffusion models. More\nspecifically, our approach, HexaGen3D, fine-tunes a pretrained text-to-image\nmodel to jointly predict 6 orthographic projections and the corresponding\nlatent triplane. We then decode these latents to generate a textured mesh.\nHexaGen3D does not require per-sample optimization, and can infer high-quality\nand diverse objects from textual prompts in 7 seconds, offering significantly\nbetter quality-to-latency trade-offs when comparing to existing approaches.\nFurthermore, HexaGen3D demonstrates strong generalization to new objects or\ncompositions.\n", "date": "2024-01-15", "categories": []}
{"id": "2312.13941", "title": "Controllable 3D Face Generation with Conditional Style Code Diffusion", "abstract": "  Generating photorealistic 3D faces from given conditions is a challenging\ntask. Existing methods often rely on time-consuming one-by-one optimization\napproaches, which are not efficient for modeling the same distribution content,\ne.g., faces. Additionally, an ideal controllable 3D face generation model\nshould consider both facial attributes and expressions. Thus we propose a novel\napproach called TEx-Face(TExt & Expression-to-Face) that addresses these\nchallenges by dividing the task into three components, i.e., 3D GAN Inversion,\nConditional Style Code Diffusion, and 3D Face Decoding. For 3D GAN inversion,\nwe introduce two methods which aim to enhance the representation of style codes\nand alleviate 3D inconsistencies. Furthermore, we design a style code denoiser\nto incorporate multiple conditions into the style code and propose a data\naugmentation strategy to address the issue of insufficient paired\nvisual-language data. Extensive experiments conducted on FFHQ, CelebA-HQ, and\nCelebA-Dialog demonstrate the promising performance of our TEx-Face in\nachieving the efficient and controllable generation of photorealistic 3D faces.\nThe code will be available at https://github.com/sxl142/TEx-Face.\n", "date": "2023-12-21", "categories": []}
{"id": "2306.10830", "title": "3D VR Sketch Guided 3D Shape Prototyping and Exploration", "abstract": "  3D shape modeling is labor-intensive, time-consuming, and requires years of\nexpertise. To facilitate 3D shape modeling, we propose a 3D shape generation\nnetwork that takes a 3D VR sketch as a condition. We assume that sketches are\ncreated by novices without art training and aim to reconstruct geometrically\nrealistic 3D shapes of a given category. To handle potential sketch ambiguity,\nour method creates multiple 3D shapes that align with the original sketch's\nstructure. We carefully design our method, training the model step-by-step and\nleveraging multi-modal 3D shape representation to support training with limited\ntraining data. To guarantee the realism of generated 3D shapes we leverage the\nnormalizing flow that models the distribution of the latent space of 3D shapes.\nTo encourage the fidelity of the generated 3D shapes to an input sketch, we\npropose a dedicated loss that we deploy at different stages of the training\nprocess. The code is available at https://github.com/Rowl1ng/3Dsketch2shape.\n", "date": "2023-06-19", "categories": []}
{"id": "2401.04099", "title": "AGG: Amortized Generative 3D Gaussians for Single Image to 3D", "abstract": "  Given the growing need for automatic 3D content creation pipelines, various\n3D representations have been studied to generate 3D objects from a single\nimage. Due to its superior rendering efficiency, 3D Gaussian splatting-based\nmodels have recently excelled in both 3D reconstruction and generation. 3D\nGaussian splatting approaches for image to 3D generation are often\noptimization-based, requiring many computationally expensive score-distillation\nsteps. To overcome these challenges, we introduce an Amortized Generative 3D\nGaussian framework (AGG) that instantly produces 3D Gaussians from a single\nimage, eliminating the need for per-instance optimization. Utilizing an\nintermediate hybrid representation, AGG decomposes the generation of 3D\nGaussian locations and other appearance attributes for joint optimization.\nMoreover, we propose a cascaded pipeline that first generates a coarse\nrepresentation of the 3D data and later upsamples it with a 3D Gaussian\nsuper-resolution module. Our method is evaluated against existing\noptimization-based 3D Gaussian frameworks and sampling-based pipelines\nutilizing other 3D representations, where AGG showcases competitive generation\nabilities both qualitatively and quantitatively while being several orders of\nmagnitude faster. Project page: https://ir1d.github.io/AGG/\n", "date": "2024-01-08", "categories": []}
{"id": "2401.02620", "title": "Progress and Prospects in 3D Generative AI: A Technical Overview\n  including 3D human", "abstract": "  While AI-generated text and 2D images continue to expand its territory, 3D\ngeneration has gradually emerged as a trend that cannot be ignored. Since the\nyear 2023 an abundant amount of research papers has emerged in the domain of 3D\ngeneration. This growth encompasses not just the creation of 3D objects, but\nalso the rapid development of 3D character and motion generation. Several key\nfactors contribute to this progress. The enhanced fidelity in stable diffusion,\ncoupled with control methods that ensure multi-view consistency, and realistic\nhuman models like SMPL-X, contribute synergistically to the production of 3D\nmodels with remarkable consistency and near-realistic appearances. The\nadvancements in neural network-based 3D storing and rendering models, such as\nNeural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have\naccelerated the efficiency and realism of neural rendered models. Furthermore,\nthe multimodality capabilities of large language models have enabled language\ninputs to transcend into human motion outputs. This paper aims to provide a\ncomprehensive overview and summary of the relevant papers published mostly\nduring the latter half year of 2023. It will begin by discussing the AI\ngenerated object models in 3D, followed by the generated 3D human models, and\nfinally, the generated 3D human motions, culminating in a conclusive summary\nand a vision for the future.\n", "date": "2024-01-05", "categories": []}
{"id": "2401.02937", "title": "Locally Adaptive Neural 3D Morphable Models", "abstract": "  We present the Locally Adaptive Morphable Model (LAMM), a highly flexible\nAuto-Encoder (AE) framework for learning to generate and manipulate 3D meshes.\nWe train our architecture following a simple self-supervised training scheme in\nwhich input displacements over a set of sparse control vertices are used to\noverwrite the encoded geometry in order to transform one training sample into\nanother. During inference, our model produces a dense output that adheres\nlocally to the specified sparse geometry while maintaining the overall\nappearance of the encoded object. This approach results in state-of-the-art\nperformance in both disentangling manipulated geometry and 3D mesh\nreconstruction. To the best of our knowledge LAMM is the first end-to-end\nframework that enables direct local control of 3D vertex geometry in a single\nforward pass. A very efficient computational graph allows our network to train\nwith only a fraction of the memory required by previous methods and run faster\nduring inference, generating 12k vertex meshes at $>$60fps on a single CPU\nthread. We further leverage local geometry control as a primitive for higher\nlevel editing operations and present a set of derivative capabilities such as\nswapping and sampling object parts. Code and pretrained models can be found at\nhttps://github.com/michaeltrs/LAMM.\n", "date": "2024-01-05", "categories": []}
{"id": "2312.13271", "title": "Repaint123: Fast and High-quality One Image to 3D Generation with\n  Progressive Controllable 2D Repainting", "abstract": "  Recent one image to 3D generation methods commonly adopt Score Distillation\nSampling (SDS). Despite the impressive results, there are multiple deficiencies\nincluding multi-view inconsistency, over-saturated and over-smoothed textures,\nas well as the slow generation speed. To address these deficiencies, we present\nRepaint123 to alleviate multi-view bias as well as texture degradation and\nspeed up the generation process. The core idea is to combine the powerful image\ngeneration capability of the 2D diffusion model and the texture alignment\nability of the repainting strategy for generating high-quality multi-view\nimages with consistency. We further propose visibility-aware adaptive\nrepainting strength for overlap regions to enhance the generated image quality\nin the repainting process. The generated high-quality and multi-view consistent\nimages enable the use of simple Mean Square Error (MSE) loss for fast 3D\ncontent generation. We conduct extensive experiments and show that our method\nhas a superior ability to generate high-quality 3D content with multi-view\nconsistency and fine textures in 2 minutes from scratch. Our project page is\navailable at https://pku-yuangroup.github.io/repaint123/.\n", "date": "2023-12-20", "categories": []}
{"id": "2306.01461", "title": "PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion\n  Models", "abstract": "  This paper presents PolyDiffuse, a novel structured reconstruction algorithm\nthat transforms visual sensor data into polygonal shapes with Diffusion Models\n(DM), an emerging machinery amid exploding generative AI, while formulating\nreconstruction as a generation process conditioned on sensor data. The task of\nstructured reconstruction poses two fundamental challenges to DM: 1) A\nstructured geometry is a ``set'' (e.g., a set of polygons for a floorplan\ngeometry), where a sample of $N$ elements has $N!$ different but equivalent\nrepresentations, making the denoising highly ambiguous; and 2) A\n``reconstruction'' task has a single solution, where an initial noise needs to\nbe chosen carefully, while any initial noise works for a generation task. Our\ntechnical contribution is the introduction of a Guided Set Diffusion Model\nwhere 1) the forward diffusion process learns guidance networks to control\nnoise injection so that one representation of a sample remains distinct from\nits other permutation variants, thus resolving denoising ambiguity; and 2) the\nreverse denoising process reconstructs polygonal shapes, initialized and\ndirected by the guidance networks, as a conditional generation process subject\nto the sensor data. We have evaluated our approach for reconstructing two types\nof polygonal shapes: floorplan as a set of polygons and HD map for autonomous\ncars as a set of polylines. Through extensive experiments on standard\nbenchmarks, we demonstrate that PolyDiffuse significantly advances the current\nstate of the art and enables broader practical applications.\n", "date": "2023-06-02", "categories": []}
{"id": "2308.12590", "title": "Self-supervised Learning of Implicit Shape Representation with Dense\n  Correspondence for Deformable Objects", "abstract": "  Learning 3D shape representation with dense correspondence for deformable\nobjects is a fundamental problem in computer vision. Existing approaches often\nneed additional annotations of specific semantic domain, e.g., skeleton poses\nfor human bodies or animals, which require extra annotation effort and suffer\nfrom error accumulation, and they are limited to specific domain. In this\npaper, we propose a novel self-supervised approach to learn neural implicit\nshape representation for deformable objects, which can represent shapes with a\ntemplate shape and dense correspondence in 3D. Our method does not require the\npriors of skeleton and skinning weight, and only requires a collection of\nshapes represented in signed distance fields. To handle the large deformation,\nwe constrain the learned template shape in the same latent space with the\ntraining shapes, design a new formulation of local rigid constraint that\nenforces rigid transformation in local region and addresses local reflection\nissue, and present a new hierarchical rigid constraint to reduce the ambiguity\ndue to the joint learning of template shape and correspondences. Extensive\nexperiments show that our model can represent shapes with large deformations.\nWe also show that our shape representation can support two typical\napplications, such as texture transfer and shape editing, with competitive\nperformance. The code and models are available at\nhttps://iscas3dv.github.io/deformshape\n", "date": "2023-08-24", "categories": []}
{"id": "2311.16918", "title": "RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail\n  Richness in Text-to-3D", "abstract": "  Lifting 2D diffusion for 3D generation is a challenging problem due to the\nlack of geometric prior and the complex entanglement of materials and lighting\nin natural images. Existing methods have shown promise by first creating the\ngeometry through score-distillation sampling (SDS) applied to rendered surface\nnormals, followed by appearance modeling. However, relying on a 2D RGB\ndiffusion model to optimize surface normals is suboptimal due to the\ndistribution discrepancy between natural images and normals maps, leading to\ninstability in optimization. In this paper, recognizing that the normal and\ndepth information effectively describe scene geometry and be automatically\nestimated from images, we propose to learn a generalizable Normal-Depth\ndiffusion model for 3D generation. We achieve this by training on the\nlarge-scale LAION dataset together with the generalizable image-to-depth and\nnormal prior models. In an attempt to alleviate the mixed illumination effects\nin the generated materials, we introduce an albedo diffusion model to impose\ndata-driven constraints on the albedo component. Our experiments show that when\nintegrated into existing text-to-3D pipelines, our models significantly enhance\nthe detail richness, achieving state-of-the-art results. Our project page is\nhttps://aigc3d.github.io/richdreamer/.\n", "date": "2023-11-28", "categories": []}
{"id": "2312.08889", "title": "SEEAvatar: Photorealistic Text-to-3D Avatar Generation with Constrained\n  Geometry and Appearance", "abstract": "  Powered by large-scale text-to-image generation models, text-to-3D avatar\ngeneration has made promising progress. However, most methods fail to produce\nphotorealistic results, limited by imprecise geometry and low-quality\nappearance. Towards more practical avatar generation, we present SEEAvatar, a\nmethod for generating photorealistic 3D avatars from text with SElf-Evolving\nconstraints for decoupled geometry and appearance. For geometry, we propose to\nconstrain the optimized avatar in a decent global shape with a template avatar.\nThe template avatar is initialized with human prior and can be updated by the\noptimized avatar periodically as an evolving template, which enables more\nflexible shape generation. Besides, the geometry is also constrained by the\nstatic human prior in local parts like face and hands to maintain the delicate\nstructures. For appearance generation, we use diffusion model enhanced by\nprompt engineering to guide a physically based rendering pipeline to generate\nrealistic textures. The lightness constraint is applied on the albedo texture\nto suppress incorrect lighting effect. Experiments show that our method\noutperforms previous methods on both global and local geometry and appearance\nquality by a large margin. Since our method can produce high-quality meshes and\ntextures, such assets can be directly applied in classic graphics pipeline for\nrealistic rendering under any lighting condition. Project page at:\nhttps://yoxu515.github.io/SEEAvatar/.\n", "date": "2023-12-13", "categories": []}
{"id": "2312.15430", "title": "Make-A-Character: High Quality Text-to-3D Character Generation within\n  Minutes", "abstract": "  There is a growing demand for customized and expressive 3D characters with\nthe emergence of AI agents and Metaverse, but creating 3D characters using\ntraditional computer graphics tools is a complex and time-consuming task. To\naddress these challenges, we propose a user-friendly framework named\nMake-A-Character (Mach) to create lifelike 3D avatars from text descriptions.\nThe framework leverages the power of large language and vision models for\ntextual intention understanding and intermediate image generation, followed by\na series of human-oriented visual perception and 3D generation modules. Our\nsystem offers an intuitive approach for users to craft controllable, realistic,\nfully-realized 3D characters that meet their expectations within 2 minutes,\nwhile also enabling easy integration with existing CG pipeline for dynamic\nexpressiveness. For more information, please visit the project page at\nhttps://human3daigc.github.io/MACH/.\n", "date": "2023-12-24", "categories": []}
{"id": "2312.15980", "title": "HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D", "abstract": "  Recent progress in single-image 3D generation highlights the importance of\nmulti-view coherency, leveraging 3D priors from large-scale diffusion models\npretrained on Internet-scale images. However, the aspect of novel-view\ndiversity remains underexplored within the research landscape due to the\nambiguity in converting a 2D image into 3D content, where numerous potential\nshapes can emerge. Here, we aim to address this research gap by simultaneously\naddressing both consistency and diversity. Yet, striking a balance between\nthese two aspects poses a considerable challenge due to their inherent\ntrade-offs. This work introduces HarmonyView, a simple yet effective diffusion\nsampling technique adept at decomposing two intricate aspects in single-image\n3D generation: consistency and diversity. This approach paves the way for a\nmore nuanced exploration of the two critical dimensions within the sampling\nprocess. Moreover, we propose a new evaluation metric based on CLIP image and\ntext encoders to comprehensively assess the diversity of the generated views,\nwhich closely aligns with human evaluators' judgments. In experiments,\nHarmonyView achieves a harmonious balance, demonstrating a win-win scenario in\nboth consistency and diversity.\n", "date": "2023-12-26", "categories": []}
{"id": "2312.03763", "title": "Gaussian3Diff: 3D Gaussian Diffusion for 3D Full Head Synthesis and\n  Editing", "abstract": "  We present a novel framework for generating photorealistic 3D human head and\nsubsequently manipulating and reposing them with remarkable flexibility. The\nproposed approach leverages an implicit function representation of 3D human\nheads, employing 3D Gaussians anchored on a parametric face model. To enhance\nrepresentational capabilities and encode spatial information, we embed a\nlightweight tri-plane payload within each Gaussian rather than directly storing\ncolor and opacity. Additionally, we parameterize the Gaussians in a 2D UV space\nvia a 3DMM, enabling effective utilization of the diffusion model for 3D head\navatar generation. Our method facilitates the creation of diverse and realistic\n3D human heads with fine-grained editing over facial features and expressions.\nExtensive experiments demonstrate the effectiveness of our method.\n", "date": "2023-12-05", "categories": []}
{"id": "2303.11938", "title": "3D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusion", "abstract": "  We tackle the task of text-to-3D creation with pre-trained latent-based NeRFs\n(NeRFs that generate 3D objects given input latent code). Recent works such as\nDreamFusion and Magic3D have shown great success in generating 3D content using\nNeRFs and text prompts, but the current approach of optimizing a NeRF for every\ntext prompt is 1) extremely time-consuming and 2) often leads to low-resolution\noutputs. To address these challenges, we propose a novel method named\n3D-CLFusion which leverages the pre-trained latent-based NeRFs and performs\nfast 3D content creation in less than a minute. In particular, we introduce a\nlatent diffusion prior network for learning the w latent from the input CLIP\ntext/image embeddings. This pipeline allows us to produce the w latent without\nfurther optimization during inference and the pre-trained NeRF is able to\nperform multi-view high-resolution 3D synthesis based on the latent. We note\nthat the novelty of our model lies in that we introduce contrastive learning\nduring training the diffusion prior which enables the generation of the valid\nview-invariant latent code. We demonstrate through experiments the\neffectiveness of our proposed view-invariant diffusion process for fast\ntext-to-3D creation, e.g., 100 times faster than DreamFusion. We note that our\nmodel is able to serve as the role of a plug-and-play tool for text-to-3D with\npre-trained NeRFs.\n", "date": "2023-03-21", "categories": []}
{"id": "2312.04875", "title": "MVDD: Multi-View Depth Diffusion Models", "abstract": "  Denoising diffusion models have demonstrated outstanding results in 2D image\ngeneration, yet it remains a challenge to replicate its success in 3D shape\ngeneration. In this paper, we propose leveraging multi-view depth, which\nrepresents complex 3D shapes in a 2D data format that is easy to denoise. We\npair this representation with a diffusion model, MVDD, that is capable of\ngenerating high-quality dense point clouds with 20K+ points with fine-grained\ndetails. To enforce 3D consistency in multi-view depth, we introduce an\nepipolar line segment attention that conditions the denoising step for a view\non its neighboring views. Additionally, a depth fusion module is incorporated\ninto diffusion steps to further ensure the alignment of depth maps. When\naugmented with surface reconstruction, MVDD can also produce high-quality 3D\nmeshes. Furthermore, MVDD stands out in other tasks such as depth completion,\nand can serve as a 3D prior, significantly boosting many downstream tasks, such\nas GAN inversion. State-of-the-art results from extensive experiments\ndemonstrate MVDD's excellent ability in 3D shape generation, depth completion,\nand its potential as a 3D prior for downstream tasks.\n", "date": "2023-12-08", "categories": []}
{"id": "2312.11666", "title": "HAAR: Text-Conditioned Generative Model of 3D Strand-based Human\n  Hairstyles", "abstract": "  We present HAAR, a new strand-based generative model for 3D human hairstyles.\nSpecifically, based on textual inputs, HAAR produces 3D hairstyles that could\nbe used as production-level assets in modern computer graphics engines. Current\nAI-based generative models take advantage of powerful 2D priors to reconstruct\n3D content in the form of point clouds, meshes, or volumetric functions.\nHowever, by using the 2D priors, they are intrinsically limited to only\nrecovering the visual parts. Highly occluded hair structures can not be\nreconstructed with those methods, and they only model the ''outer shell'',\nwhich is not ready to be used in physics-based rendering or simulation\npipelines. In contrast, we propose a first text-guided generative method that\nuses 3D hair strands as an underlying representation. Leveraging 2D visual\nquestion-answering (VQA) systems, we automatically annotate synthetic hair\nmodels that are generated from a small set of artist-created hairstyles. This\nallows us to train a latent diffusion model that operates in a common hairstyle\nUV space. In qualitative and quantitative studies, we demonstrate the\ncapabilities of the proposed model and compare it to existing hairstyle\ngeneration approaches.\n", "date": "2023-12-18", "categories": []}
{"id": "2312.11417", "title": "PolyDiff: Generating 3D Polygonal Meshes with Diffusion Models", "abstract": "  We introduce PolyDiff, the first diffusion-based approach capable of directly\ngenerating realistic and diverse 3D polygonal meshes. In contrast to methods\nthat use alternate 3D shape representations (e.g. implicit representations),\nour approach is a discrete denoising diffusion probabilistic model that\noperates natively on the polygonal mesh data structure. This enables learning\nof both the geometric properties of vertices and the topological\ncharacteristics of faces. Specifically, we treat meshes as quantized triangle\nsoups, progressively corrupted with categorical noise in the forward diffusion\nphase. In the reverse diffusion phase, a transformer-based denoising network is\ntrained to revert the noising process, restoring the original mesh structure.\nAt inference, new meshes can be generated by applying this denoising network\niteratively, starting with a completely noisy triangle soup. Consequently, our\nmodel is capable of producing high-quality 3D polygonal meshes, ready for\nintegration into downstream 3D workflows. Our extensive experimental analysis\nshows that PolyDiff achieves a significant advantage (avg. FID and JSD\nimprovement of 18.2 and 5.8 respectively) over current state-of-the-art\nmethods.\n", "date": "2023-12-18", "categories": []}
{"id": "2312.09246", "title": "SHAP-EDITOR: Instruction-guided Latent 3D Editing in Seconds", "abstract": "  We propose a novel feed-forward 3D editing framework called Shap-Editor.\nPrior research on editing 3D objects primarily concentrated on editing\nindividual objects by leveraging off-the-shelf 2D image editing networks. This\nis achieved via a process called distillation, which transfers knowledge from\nthe 2D network to 3D assets. Distillation necessitates at least tens of minutes\nper asset to attain satisfactory editing results, and is thus not very\npractical. In contrast, we ask whether 3D editing can be carried out directly\nby a feed-forward network, eschewing test-time optimisation. In particular, we\nhypothesise that editing can be greatly simplified by first encoding 3D objects\nin a suitable latent space. We validate this hypothesis by building upon the\nlatent space of Shap-E. We demonstrate that direct 3D editing in this space is\npossible and efficient by building a feed-forward editor network that only\nrequires approximately one second per edit. Our experiments show that\nShap-Editor generalises well to both in-distribution and out-of-distribution 3D\nassets with different prompts, exhibiting comparable performance with methods\nthat carry out test-time optimisation for each edited instance.\n", "date": "2023-12-14", "categories": []}
{"id": "2312.08094", "title": "3DGEN: A GAN-based approach for generating novel 3D models from image\n  data", "abstract": "  The recent advances in text and image synthesis show a great promise for the\nfuture of generative models in creative fields. However, a less explored area\nis the one of 3D model generation, with a lot of potential applications to game\ndesign, video production, and physical product design. In our paper, we present\n3DGEN, a model that leverages the recent work on both Neural Radiance Fields\nfor object reconstruction and GAN-based image generation. We show that the\nproposed architecture can generate plausible meshes for objects of the same\ncategory as the training images and compare the resulting meshes with the\nstate-of-the-art baselines, leading to visible uplifts in generation quality.\n", "date": "2023-12-13", "categories": []}
{"id": "2312.07231", "title": "Fast Training of Diffusion Transformer with Extreme Masking for 3D Point\n  Clouds Generation", "abstract": "  Diffusion Transformers have recently shown remarkable effectiveness in\ngenerating high-quality 3D point clouds. However, training voxel-based\ndiffusion models for high-resolution 3D voxels remains prohibitively expensive\ndue to the cubic complexity of attention operators, which arises from the\nadditional dimension of voxels. Motivated by the inherent redundancy of 3D\ncompared to 2D, we propose FastDiT-3D, a novel masked diffusion transformer\ntailored for efficient 3D point cloud generation, which greatly reduces\ntraining costs. Specifically, we draw inspiration from masked autoencoders to\ndynamically operate the denoising process on masked voxelized point clouds. We\nalso propose a novel voxel-aware masking strategy to adaptively aggregate\nbackground/foreground information from voxelized point clouds. Our method\nachieves state-of-the-art performance with an extreme masking ratio of nearly\n99%. Moreover, to improve multi-category 3D generation, we introduce\nMixture-of-Expert (MoE) in 3D diffusion model. Each category can learn a\ndistinct diffusion path with different experts, relieving gradient conflict.\nExperimental results on the ShapeNet dataset demonstrate that our method\nachieves state-of-the-art high-fidelity and diverse 3D point cloud generation\nperformance. Our FastDiT-3D improves 1-Nearest Neighbor Accuracy and Coverage\nmetrics when generating 128-resolution voxel point clouds, using only 6.5% of\nthe original training cost.\n", "date": "2023-12-12", "categories": []}
{"id": "2312.06655", "title": "Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D\n  Prior", "abstract": "  Recently, 3D content creation from text prompts has demonstrated remarkable\nprogress by utilizing 2D and 3D diffusion models. While 3D diffusion models\nensure great multi-view consistency, their ability to generate high-quality and\ndiverse 3D assets is hindered by the limited 3D data. In contrast, 2D diffusion\nmodels find a distillation approach that achieves excellent generalization and\nrich details without any 3D data. However, 2D lifting methods suffer from\ninherent view-agnostic ambiguity thereby leading to serious multi-face Janus\nissues, where text prompts fail to provide sufficient guidance to learn\ncoherent 3D results. Instead of retraining a costly viewpoint-aware model, we\nstudy how to fully exploit easily accessible coarse 3D knowledge to enhance the\nprompts and guide 2D lifting optimization for refinement. In this paper, we\npropose Sherpa3D, a new text-to-3D framework that achieves high-fidelity,\ngeneralizability, and geometric consistency simultaneously. Specifically, we\ndesign a pair of guiding strategies derived from the coarse 3D prior generated\nby the 3D diffusion model: a structural guidance for geometric fidelity and a\nsemantic guidance for 3D coherence. Employing the two types of guidance, the 2D\ndiffusion model enriches the 3D content with diversified and high-quality\nresults. Extensive experiments show the superiority of our Sherpa3D over the\nstate-of-the-art text-to-3D methods in terms of quality and 3D consistency.\n", "date": "2023-12-11", "categories": []}
{"id": "2312.06663", "title": "CAD: Photorealistic 3D Generation via Adversarial Distillation", "abstract": "  The increased demand for 3D data in AR/VR, robotics and gaming applications,\ngave rise to powerful generative pipelines capable of synthesizing high-quality\n3D objects. Most of these models rely on the Score Distillation Sampling (SDS)\nalgorithm to optimize a 3D representation such that the rendered image\nmaintains a high likelihood as evaluated by a pre-trained diffusion model.\nHowever, finding a correct mode in the high-dimensional distribution produced\nby the diffusion model is challenging and often leads to issues such as\nover-saturation, over-smoothing, and Janus-like artifacts. In this paper, we\npropose a novel learning paradigm for 3D synthesis that utilizes pre-trained\ndiffusion models. Instead of focusing on mode-seeking, our method directly\nmodels the distribution discrepancy between multi-view renderings and diffusion\npriors in an adversarial manner, which unlocks the generation of high-fidelity\nand photorealistic 3D content, conditioned on a single image and prompt.\nMoreover, by harnessing the latent space of GANs and expressive diffusion model\npriors, our method facilitates a wide variety of 3D applications including\nsingle-view reconstruction, high diversity generation and continuous 3D\ninterpolation in the open domain. The experiments demonstrate the superiority\nof our pipeline compared to previous works in terms of generation quality and\ndiversity.\n", "date": "2023-12-11", "categories": []}
{"id": "2312.04806", "title": "RL Dreams: Policy Gradient Optimization for Score Distillation based 3D\n  Generation", "abstract": "  3D generation has rapidly accelerated in the past decade owing to the\nprogress in the field of generative modeling. Score Distillation Sampling (SDS)\nbased rendering has improved 3D asset generation to a great extent. Further,\nthe recent work of Denoising Diffusion Policy Optimization (DDPO) demonstrates\nthat the diffusion process is compatible with policy gradient methods and has\nbeen demonstrated to improve the 2D diffusion models using an aesthetic scoring\nfunction. We first show that this aesthetic scorer acts as a strong guide for a\nvariety of SDS-based methods and demonstrates its effectiveness in text-to-3D\nsynthesis. Further, we leverage the DDPO approach to improve the quality of the\n3D rendering obtained from 2D diffusion models. Our approach, DDPO3D, employs\nthe policy gradient method in tandem with aesthetic scoring. To the best of our\nknowledge, this is the first method that extends policy gradient methods to 3D\nscore-based rendering and shows improvement across SDS-based methods such as\nDreamGaussian, which are currently driving research in text-to-3D synthesis.\nOur approach is compatible with score distillation-based methods, which would\nfacilitate the integration of diverse reward functions into the generative\nprocess. Our project page can be accessed via https://ddpo3d.github.io.\n", "date": "2023-12-08", "categories": []}
{"id": "2312.04820", "title": "Learn to Optimize Denoising Scores for 3D Generation: A Unified and\n  Improved Diffusion Prior on NeRF and 3D Gaussian Splatting", "abstract": "  We propose a unified framework aimed at enhancing the diffusion priors for 3D\ngeneration tasks. Despite the critical importance of these tasks, existing\nmethodologies often struggle to generate high-caliber results. We begin by\nexamining the inherent limitations in previous diffusion priors. We identify a\ndivergence between the diffusion priors and the training procedures of\ndiffusion models that substantially impairs the quality of 3D generation. To\naddress this issue, we propose a novel, unified framework that iteratively\noptimizes both the 3D model and the diffusion prior. Leveraging the different\nlearnable parameters of the diffusion prior, our approach offers multiple\nconfigurations, affording various trade-offs between performance and\nimplementation complexity. Notably, our experimental results demonstrate that\nour method markedly surpasses existing techniques, establishing new\nstate-of-the-art in the realm of text-to-3D generation. Furthermore, our\napproach exhibits impressive performance on both NeRF and the newly introduced\n3D Gaussian Splatting backbones. Additionally, our framework yields insightful\ncontributions to the understanding of recent score distillation methods, such\nas the VSD and DDS loss.\n", "date": "2023-12-08", "categories": []}
{"id": "2312.04963", "title": "Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D\n  priors", "abstract": "  Most 3D generation research focuses on up-projecting 2D foundation models\ninto the 3D space, either by minimizing 2D Score Distillation Sampling (SDS)\nloss or fine-tuning on multi-view datasets. Without explicit 3D priors, these\nmethods often lead to geometric anomalies and multi-view inconsistency.\nRecently, researchers have attempted to improve the genuineness of 3D objects\nby directly training on 3D datasets, albeit at the cost of low-quality texture\ngeneration due to the limited texture diversity in 3D datasets. To harness the\nadvantages of both approaches, we propose Bidirectional Diffusion(BiDiff), a\nunified framework that incorporates both a 3D and a 2D diffusion process, to\npreserve both 3D fidelity and 2D texture richness, respectively. Moreover, as a\nsimple combination may yield inconsistent generation results, we further bridge\nthem with novel bidirectional guidance. In addition, our method can be used as\nan initialization of optimization-based models to further improve the quality\nof 3D model and efficiency of optimization, reducing the generation process\nfrom 3.4 hours to 20 minutes. Experimental results have shown that our model\nachieves high-quality, diverse, and scalable 3D generation. Project website:\nhttps://bidiff.github.io/.\n", "date": "2023-12-07", "categories": []}
{"id": "2312.03869", "title": "Inpaint3D: 3D Scene Content Generation using 2D Inpainting Diffusion", "abstract": "  This paper presents a novel approach to inpainting 3D regions of a scene,\ngiven masked multi-view images, by distilling a 2D diffusion model into a\nlearned 3D scene representation (e.g. a NeRF). Unlike 3D generative methods\nthat explicitly condition the diffusion model on camera pose or multi-view\ninformation, our diffusion model is conditioned only on a single masked 2D\nimage. Nevertheless, we show that this 2D diffusion model can still serve as a\ngenerative prior in a 3D multi-view reconstruction problem where we optimize a\nNeRF using a combination of score distillation sampling and NeRF reconstruction\nlosses. Predicted depth is used as additional supervision to encourage accurate\ngeometry. We compare our approach to 3D inpainting methods that focus on object\nremoval. Because our method can generate content to fill any 3D masked region,\nwe additionally demonstrate 3D object completion, 3D object replacement, and 3D\nscene completion.\n", "date": "2023-12-06", "categories": []}
{"id": "2312.04543", "title": "HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a\n  Single Image", "abstract": "  3D content creation from a single image is a long-standing yet highly\ndesirable task. Recent advances introduce 2D diffusion priors, yielding\nreasonable results. However, existing methods are not hyper-realistic enough\nfor post-generation usage, as users cannot view, render and edit the resulting\n3D content from a full range. To address these challenges, we introduce\nHyperDreamer with several key designs and appealing properties: 1) Viewable:\n360 degree mesh modeling with high-resolution textures enables the creation of\nvisually compelling 3D models from a full range of observation points. 2)\nRenderable: Fine-grained semantic segmentation and data-driven priors are\nincorporated as guidance to learn reasonable albedo, roughness, and specular\nproperties of the materials, enabling semantic-aware arbitrary material\nestimation. 3) Editable: For a generated model or their own data, users can\ninteractively select any region via a few clicks and efficiently edit the\ntexture with text-based guidance. Extensive experiments demonstrate the\neffectiveness of HyperDreamer in modeling region-aware materials with\nhigh-resolution textures and enabling user-friendly editing. We believe that\nHyperDreamer holds promise for advancing 3D content creation and finding\napplications in various domains.\n", "date": "2023-12-07", "categories": []}
{"id": "2211.02900", "title": "Grassmann Manifold Flows for Stable Shape Generation", "abstract": "  Recently, studies on machine learning have focused on methods that use\nsymmetry implicit in a specific manifold as an inductive bias. Grassmann\nmanifolds provide the ability to handle fundamental shapes represented as shape\nspaces, enabling stable shape analysis. In this paper, we present a novel\napproach in which we establish the theoretical foundations for learning\ndistributions on the Grassmann manifold via continuous normalization flows,\nwith the explicit goal of generating stable shapes. Our approach facilitates\nmore robust generation by effectively eliminating the influence of extraneous\ntransformations, such as rotations and inversions, through learning and\ngenerating within a Grassmann manifold designed to accommodate the essential\nshape information of the object. The experimental results indicated that the\nproposed method could generate high-quality samples by capturing the data\nstructure. Furthermore, the proposed method significantly outperformed\nstate-of-the-art methods in terms of the log-likelihood or evidence lower\nbound. The results obtained are expected to stimulate further research in this\nfield, leading to advances for stable shape generation and analysis.\n", "date": "2022-11-05", "categories": []}
{"id": "2312.02189", "title": "StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D", "abstract": "  In the realm of text-to-3D generation, utilizing 2D diffusion models through\nscore distillation sampling (SDS) frequently leads to issues such as blurred\nappearances and multi-faced geometry, primarily due to the intrinsically noisy\nnature of the SDS loss. Our analysis identifies the core of these challenges as\nthe interaction among noise levels in the 2D diffusion process, the\narchitecture of the diffusion network, and the 3D model representation. To\novercome these limitations, we present StableDreamer, a methodology\nincorporating three advances. First, inspired by InstructNeRF2NeRF, we\nformalize the equivalence of the SDS generative prior and a simple supervised\nL2 reconstruction loss. This finding provides a novel tool to debug SDS, which\nwe use to show the impact of time-annealing noise levels on reducing\nmulti-faced geometries. Second, our analysis shows that while image-space\ndiffusion contributes to geometric precision, latent-space diffusion is crucial\nfor vivid color rendition. Based on this observation, StableDreamer introduces\na two-stage training strategy that effectively combines these aspects,\nresulting in high-fidelity 3D models. Third, we adopt an anisotropic 3D\nGaussians representation, replacing Neural Radiance Fields (NeRFs), to enhance\nthe overall quality, reduce memory usage during training, and accelerate\nrendering speeds, and better capture semi-transparent objects. StableDreamer\nreduces multi-face geometries, generates fine details, and converges stably.\n", "date": "2023-12-02", "categories": []}
{"id": "2312.02201", "title": "ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation", "abstract": "  We introduce \"ImageDream,\" an innovative image-prompt, multi-view diffusion\nmodel for 3D object generation. ImageDream stands out for its ability to\nproduce 3D models of higher quality compared to existing state-of-the-art,\nimage-conditioned methods. Our approach utilizes a canonical camera\ncoordination for the objects in images, improving visual geometry accuracy. The\nmodel is designed with various levels of control at each block inside the\ndiffusion model based on the input image, where global control shapes the\noverall object layout and local control fine-tunes the image details. The\neffectiveness of ImageDream is demonstrated through extensive evaluations using\na standard prompt list. For more information, visit our project page at\nhttps://Image-Dream.github.io.\n", "date": "2023-12-02", "categories": []}
{"id": "2311.11284", "title": "LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval\n  Score Matching", "abstract": "  The recent advancements in text-to-3D generation mark a significant milestone\nin generative models, unlocking new possibilities for creating imaginative 3D\nassets across various real-world scenarios. While recent advancements in\ntext-to-3D generation have shown promise, they often fall short in rendering\ndetailed and high-quality 3D models. This problem is especially prevalent as\nmany methods base themselves on Score Distillation Sampling (SDS). This paper\nidentifies a notable deficiency in SDS, that it brings inconsistent and\nlow-quality updating direction for the 3D model, causing the over-smoothing\neffect. To address this, we propose a novel approach called Interval Score\nMatching (ISM). ISM employs deterministic diffusing trajectories and utilizes\ninterval-based score matching to counteract over-smoothing. Furthermore, we\nincorporate 3D Gaussian Splatting into our text-to-3D generation pipeline.\nExtensive experiments show that our model largely outperforms the\nstate-of-the-art in quality and training efficiency.\n", "date": "2023-11-19", "categories": []}
{"id": "2311.17971", "title": "GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and\n  Consistent 3D Generation", "abstract": "  Text-to-3D generation by distilling pretrained large-scale text-to-image\ndiffusion models has shown great promise but still suffers from inconsistent 3D\ngeometric structures (Janus problems) and severe artifacts. The aforementioned\nproblems mainly stem from 2D diffusion models lacking 3D awareness during the\nlifting. In this work, we present GeoDream, a novel method that incorporates\nexplicit generalized 3D priors with 2D diffusion priors to enhance the\ncapability of obtaining unambiguous 3D consistent geometric structures without\nsacrificing diversity or fidelity. Specifically, we first utilize a multi-view\ndiffusion model to generate posed images and then construct cost volume from\nthe predicted image, which serves as native 3D geometric priors, ensuring\nspatial consistency in 3D space. Subsequently, we further propose to harness 3D\ngeometric priors to unlock the great potential of 3D awareness in 2D diffusion\npriors via a disentangled design. Notably, disentangling 2D and 3D priors\nallows us to refine 3D geometric priors further. We justify that the refined 3D\ngeometric priors aid in the 3D-aware capability of 2D diffusion priors, which\nin turn provides superior guidance for the refinement of 3D geometric priors.\nOur numerical and visual comparisons demonstrate that GeoDream generates more\n3D consistent textured meshes with high-resolution realistic renderings (i.e.,\n1024 $\\times$ 1024) and adheres more closely to semantic coherence.\n", "date": "2023-11-29", "categories": []}
{"id": "2311.17618", "title": "ShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Model", "abstract": "  The advent of large language models, enabling flexibility through\ninstruction-driven approaches, has revolutionized many traditional generative\ntasks, but large models for 3D data, particularly in comprehensively handling\n3D shapes with other modalities, are still under-explored. By achieving\ninstruction-based shape generations, versatile multimodal generative shape\nmodels can significantly benefit various fields like 3D virtual construction\nand network-aided design. In this work, we present ShapeGPT, a shape-included\nmulti-modal framework to leverage strong pre-trained language models to address\nmultiple shape-relevant tasks. Specifically, ShapeGPT employs a\nword-sentence-paragraph framework to discretize continuous shapes into shape\nwords, further assembles these words for shape sentences, as well as integrates\nshape with instructional text for multi-modal paragraphs. To learn this\nshape-language model, we use a three-stage training scheme, including shape\nrepresentation, multimodal alignment, and instruction-based generation, to\nalign shape-language codebooks and learn the intricate correlations among these\nmodalities. Extensive experiments demonstrate that ShapeGPT achieves comparable\nperformance across shape-relevant tasks, including text-to-shape,\nshape-to-text, shape completion, and shape editing.\n", "date": "2023-11-29", "categories": []}
{"id": "2312.00375", "title": "Text-Guided 3D Face Synthesis -- From Generation to Editing", "abstract": "  Text-guided 3D face synthesis has achieved remarkable results by leveraging\ntext-to-image (T2I) diffusion models. However, most existing works focus solely\non the direct generation, ignoring the editing, restricting them from\nsynthesizing customized 3D faces through iterative adjustments. In this paper,\nwe propose a unified text-guided framework from face generation to editing. In\nthe generation stage, we propose a geometry-texture decoupled generation to\nmitigate the loss of geometric details caused by coupling. Besides, decoupling\nenables us to utilize the generated geometry as a condition for texture\ngeneration, yielding highly geometry-texture aligned results. We further employ\na fine-tuned texture diffusion model to enhance texture quality in both RGB and\nYUV space. In the editing stage, we first employ a pre-trained diffusion model\nto update facial geometry or texture based on the texts. To enable sequential\nediting, we introduce a UV domain consistency preservation regularization,\npreventing unintentional changes to irrelevant facial attributes. Besides, we\npropose a self-guided consistency weight strategy to improve editing efficacy\nwhile preserving consistency. Through comprehensive experiments, we showcase\nour method's superiority in face synthesis. Project page:\nhttps://faceg2e.github.io/.\n", "date": "2023-12-01", "categories": []}
{"id": "2304.00916", "title": "DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via\n  Diffusion Models", "abstract": "  We present DreamAvatar, a text-and-shape guided framework for generating\nhigh-quality 3D human avatars with controllable poses. While encouraging\nresults have been reported by recent methods on text-guided 3D common object\ngeneration, generating high-quality human avatars remains an open challenge due\nto the complexity of the human body's shape, pose, and appearance. We propose\nDreamAvatar to tackle this challenge, which utilizes a trainable NeRF for\npredicting density and color for 3D points and pretrained text-to-image\ndiffusion models for providing 2D self-supervision. Specifically, we leverage\nthe SMPL model to provide shape and pose guidance for the generation. We\nintroduce a dual-observation-space design that involves the joint optimization\nof a canonical space and a posed space that are related by a learnable\ndeformation field. This facilitates the generation of more complete textures\nand geometry faithful to the target pose. We also jointly optimize the losses\ncomputed from the full body and from the zoomed-in 3D head to alleviate the\ncommon multi-face ''Janus'' problem and improve facial details in the generated\navatars. Extensive evaluations demonstrate that DreamAvatar significantly\noutperforms existing methods, establishing a new state-of-the-art for\ntext-and-shape guided 3D human avatar generation.\n", "date": "2023-04-03", "categories": []}
{"id": "2311.01714", "title": "EXIM: A Hybrid Explicit-Implicit Representation for Text-Guided 3D Shape\n  Generation", "abstract": "  This paper presents a new text-guided technique for generating 3D shapes. The\ntechnique leverages a hybrid 3D shape representation, namely EXIM, combining\nthe strengths of explicit and implicit representations. Specifically, the\nexplicit stage controls the topology of the generated 3D shapes and enables\nlocal modifications, whereas the implicit stage refines the shape and paints it\nwith plausible colors. Also, the hybrid approach separates the shape and color\nand generates color conditioned on shape to ensure shape-color consistency.\nUnlike the existing state-of-the-art methods, we achieve high-fidelity shape\ngeneration from natural-language descriptions without the need for\ntime-consuming per-shape optimization or reliance on human-annotated texts\nduring training or test-time optimization. Further, we demonstrate the\napplicability of our approach to generate indoor scenes with consistent styles\nusing text-induced 3D shapes. Through extensive experiments, we demonstrate the\ncompelling quality of our results and the high coherency of our generated\nshapes with the input texts, surpassing the performance of existing methods by\na significant margin. Codes and models are released at\nhttps://github.com/liuzhengzhe/EXIM.\n", "date": "2023-11-03", "categories": []}
{"id": "2310.01406", "title": "HumanNorm: Learning Normal Diffusion Model for High-quality and\n  Realistic 3D Human Generation", "abstract": "  Recent text-to-3D methods employing diffusion models have made significant\nadvancements in 3D human generation. However, these approaches face challenges\ndue to the limitations of text-to-image diffusion models, which lack an\nunderstanding of 3D structures. Consequently, these methods struggle to achieve\nhigh-quality human generation, resulting in smooth geometry and cartoon-like\nappearances. In this paper, we propose HumanNorm, a novel approach for\nhigh-quality and realistic 3D human generation. The main idea is to enhance the\nmodel's 2D perception of 3D geometry by learning a normal-adapted diffusion\nmodel and a normal-aligned diffusion model. The normal-adapted diffusion model\ncan generate high-fidelity normal maps corresponding to user prompts with\nview-dependent and body-aware text. The normal-aligned diffusion model learns\nto generate color images aligned with the normal maps, thereby transforming\nphysical geometry details into realistic appearance. Leveraging the proposed\nnormal diffusion model, we devise a progressive geometry generation strategy\nand a multi-step Score Distillation Sampling (SDS) loss to enhance the\nperformance of 3D human generation. Comprehensive experiments substantiate\nHumanNorm's ability to generate 3D humans with intricate geometry and realistic\nappearances. HumanNorm outperforms existing text-to-3D methods in both geometry\nand texture quality. The project page of HumanNorm is\nhttps://humannorm.github.io/.\n", "date": "2023-10-02", "categories": []}
{"id": "2311.17857", "title": "Gaussian Shell Maps for Efficient 3D Human Generation", "abstract": "  Efficient generation of 3D digital humans is important in several industries,\nincluding virtual reality, social media, and cinematic production. 3D\ngenerative adversarial networks (GANs) have demonstrated state-of-the-art\n(SOTA) quality and diversity for generated assets. Current 3D GAN\narchitectures, however, typically rely on volume representations, which are\nslow to render, thereby hampering the GAN training and requiring\nmulti-view-inconsistent 2D upsamplers. Here, we introduce Gaussian Shell Maps\n(GSMs) as a framework that connects SOTA generator network architectures with\nemerging 3D Gaussian rendering primitives using an articulable multi\nshell--based scaffold. In this setting, a CNN generates a 3D texture stack with\nfeatures that are mapped to the shells. The latter represent inflated and\ndeflated versions of a template surface of a digital human in a canonical body\npose. Instead of rasterizing the shells directly, we sample 3D Gaussians on the\nshells whose attributes are encoded in the texture features. These Gaussians\nare efficiently and differentiably rendered. The ability to articulate the\nshells is important during GAN training and, at inference time, to deform a\nbody into arbitrary user-defined poses. Our efficient rendering scheme bypasses\nthe need for view-inconsistent upsamplers and achieves high-quality multi-view\nconsistent renderings at a native resolution of $512 \\times 512$ pixels. We\ndemonstrate that GSMs successfully generate 3D humans when trained on\nsingle-view datasets, including SHHQ and DeepFashion.\n", "date": "2023-11-29", "categories": []}
{"id": "2311.17907", "title": "CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting", "abstract": "  With the onset of diffusion-based generative models and their ability to\ngenerate text-conditioned images, content generation has received a massive\ninvigoration. Recently, these models have been shown to provide useful guidance\nfor the generation of 3D graphics assets. However, existing work in\ntext-conditioned 3D generation faces fundamental constraints: (i) inability to\ngenerate detailed, multi-object scenes, (ii) inability to textually control\nmulti-object configurations, and (iii) physically realistic scene composition.\nIn this work, we propose CG3D, a method for compositionally generating scalable\n3D assets that resolves these constraints. We find that explicit Gaussian\nradiance fields, parameterized to allow for compositions of objects, possess\nthe capability to enable semantically and physically consistent scenes. By\nutilizing a guidance framework built around this explicit representation, we\nshow state of the art results, capable of even exceeding the guiding diffusion\nmodel in terms of object combinations and physics accuracy.\n", "date": "2023-11-29", "categories": []}
{"id": "2311.17917", "title": "AvatarStudio: High-fidelity and Animatable 3D Avatar Creation from Text", "abstract": "  We study the problem of creating high-fidelity and animatable 3D avatars from\nonly textual descriptions. Existing text-to-avatar methods are either limited\nto static avatars which cannot be animated or struggle to generate animatable\navatars with promising quality and precise pose control. To address these\nlimitations, we propose AvatarStudio, a coarse-to-fine generative model that\ngenerates explicit textured 3D meshes for animatable human avatars.\nSpecifically, AvatarStudio begins with a low-resolution NeRF-based\nrepresentation for coarse generation, followed by incorporating SMPL-guided\narticulation into the explicit mesh representation to support avatar animation\nand high resolution rendering. To ensure view consistency and pose\ncontrollability of the resulting avatars, we introduce a 2D diffusion model\nconditioned on DensePose for Score Distillation Sampling supervision. By\neffectively leveraging the synergy between the articulated mesh representation\nand the DensePose-conditional diffusion model, AvatarStudio can create\nhigh-quality avatars from text that are ready for animation, significantly\noutperforming previous methods. Moreover, it is competent for many\napplications, e.g., multimodal avatar animations and style-guided avatar\ncreation. For more results, please refer to our project page:\nhttp://jeff95.me/projects/avatarstudio.html\n", "date": "2023-11-29", "categories": []}
{"id": "2311.14494", "title": "MVControl: Adding Conditional Control to Multi-view Diffusion for\n  Controllable Text-to-3D Generation", "abstract": "  We introduce MVControl, a novel neural network architecture that enhances\nexisting pre-trained multi-view 2D diffusion models by incorporating additional\ninput conditions, e.g. edge maps. Our approach enables the generation of\ncontrollable multi-view images and view-consistent 3D content. To achieve\ncontrollable multi-view image generation, we leverage MVDream as our base\nmodel, and train a new neural network module as additional plugin for\nend-to-end task-specific condition learning. To precisely control the shapes\nand views of generated images, we innovatively propose a new conditioning\nmechanism that predicts an embedding encapsulating the input spatial and view\nconditions, which is then injected to the network globally. Once MVControl is\ntrained, score-distillation (SDS) loss based optimization can be performed to\ngenerate 3D content, in which process we propose to use a hybrid diffusion\nprior. The hybrid prior relies on a pre-trained Stable-Diffusion network and\nour trained MVControl for additional guidance. Extensive experiments\ndemonstrate that our method achieves robust generalization and enables the\ncontrollable generation of high-quality 3D content. Code available at\nhttps://github.com/WU-CVGL/MVControl/.\n", "date": "2023-11-24", "categories": []}
{"id": "2311.16961", "title": "HumanRef: Single Image to 3D Human Generation via Reference-Guided\n  Diffusion", "abstract": "  Generating a 3D human model from a single reference image is challenging\nbecause it requires inferring textures and geometries in invisible views while\nmaintaining consistency with the reference image. Previous methods utilizing 3D\ngenerative models are limited by the availability of 3D training data.\nOptimization-based methods that lift text-to-image diffusion models to 3D\ngeneration often fail to preserve the texture details of the reference image,\nresulting in inconsistent appearances in different views. In this paper, we\npropose HumanRef, a 3D human generation framework from a single-view input. To\nensure the generated 3D model is photorealistic and consistent with the input\nimage, HumanRef introduces a novel method called reference-guided score\ndistillation sampling (Ref-SDS), which effectively incorporates image guidance\ninto the generation process. Furthermore, we introduce region-aware attention\nto Ref-SDS, ensuring accurate correspondence between different body regions.\nExperimental results demonstrate that HumanRef outperforms state-of-the-art\nmethods in generating 3D clothed humans with fine geometry, photorealistic\ntextures, and view-consistent appearances.\n", "date": "2023-11-28", "categories": []}
{"id": "2311.15475", "title": "MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers", "abstract": "  We introduce MeshGPT, a new approach for generating triangle meshes that\nreflects the compactness typical of artist-created meshes, in contrast to dense\ntriangle meshes extracted by iso-surfacing methods from neural fields. Inspired\nby recent advances in powerful large language models, we adopt a sequence-based\napproach to autoregressively generate triangle meshes as sequences of\ntriangles. We first learn a vocabulary of latent quantized embeddings, using\ngraph convolutions, which inform these embeddings of the local mesh geometry\nand topology. These embeddings are sequenced and decoded into triangles by a\ndecoder, ensuring that they can effectively reconstruct the mesh. A transformer\nis then trained on this learned vocabulary to predict the index of the next\nembedding given previous embeddings. Once trained, our model can be\nautoregressively sampled to generate new triangle meshes, directly generating\ncompact meshes with sharp edges, more closely imitating the efficient\ntriangulation patterns of human-crafted meshes. MeshGPT demonstrates a notable\nimprovement over state of the art mesh generation methods, with a 9% increase\nin shape coverage and a 30-point enhancement in FID scores across various\ncategories.\n", "date": "2023-11-27", "categories": []}
{"id": "2311.15561", "title": "ET3D: Efficient Text-to-3D Generation via Multi-View Distillation", "abstract": "  Recent breakthroughs in text-to-image generation has shown encouraging\nresults via large generative models. Due to the scarcity of 3D assets, it is\nhardly to transfer the success of text-to-image generation to that of\ntext-to-3D generation. Existing text-to-3D generation methods usually adopt the\nparadigm of DreamFusion, which conducts per-asset optimization by distilling a\npretrained text-to-image diffusion model. The generation speed usually ranges\nfrom several minutes to tens of minutes per 3D asset, which degrades the user\nexperience and also imposes a burden to the service providers due to the high\ncomputational budget.\n  In this work, we present an efficient text-to-3D generation method, which\nrequires only around 8 $ms$ to generate a 3D asset given the text prompt on a\nconsumer graphic card. The main insight is that we exploit the images generated\nby a large pre-trained text-to-image diffusion model, to supervise the training\nof a text conditioned 3D generative adversarial network. Once the network is\ntrained, we are able to efficiently generate a 3D asset via a single forward\npass. Our method requires no 3D training data and provides an alternative\napproach for efficient text-to-3D generation by distilling pre-trained image\ndiffusion models.\n", "date": "2023-11-27", "categories": []}
{"id": "2311.06214", "title": "Instant3D: Fast Text-to-3D with Sparse-View Generation and Large\n  Reconstruction Model", "abstract": "  Text-to-3D with diffusion models has achieved remarkable progress in recent\nyears. However, existing methods either rely on score distillation-based\noptimization which suffer from slow inference, low diversity and Janus\nproblems, or are feed-forward methods that generate low-quality results due to\nthe scarcity of 3D training data. In this paper, we propose Instant3D, a novel\nmethod that generates high-quality and diverse 3D assets from text prompts in a\nfeed-forward manner. We adopt a two-stage paradigm, which first generates a\nsparse set of four structured and consistent views from text in one shot with a\nfine-tuned 2D text-to-image diffusion model, and then directly regresses the\nNeRF from the generated images with a novel transformer-based sparse-view\nreconstructor. Through extensive experiments, we demonstrate that our method\ncan generate diverse 3D assets of high visual quality within 20 seconds, which\nis two orders of magnitude faster than previous optimization-based methods that\ncan take 1 to 10 hours. Our project webpage: https://jiahao.ai/instant3d/.\n", "date": "2023-11-10", "categories": []}
{"id": "2311.13617", "title": "Boosting3D: High-Fidelity Image-to-3D by Boosting 2D Diffusion Prior to\n  3D Prior with Progressive Learning", "abstract": "  We present Boosting3D, a multi-stage single image-to-3D generation method\nthat can robustly generate reasonable 3D objects in different data domains. The\npoint of this work is to solve the view consistency problem in single\nimage-guided 3D generation by modeling a reasonable geometric structure. For\nthis purpose, we propose to utilize better 3D prior to training the NeRF. More\nspecifically, we train an object-level LoRA for the target object using\noriginal image and the rendering output of NeRF. And then we train the LoRA and\nNeRF using a progressive training strategy. The LoRA and NeRF will boost each\nother while training. After the progressive training, the LoRA learns the 3D\ninformation of the generated object and eventually turns to an object-level 3D\nprior. In the final stage, we extract the mesh from the trained NeRF and use\nthe trained LoRA to optimize the structure and appearance of the mesh. The\nexperiments demonstrate the effectiveness of the proposed method. Boosting3D\nlearns object-specific 3D prior which is beyond the ability of pre-trained\ndiffusion priors and achieves state-of-the-art performance in the single\nimage-to-3d generation task.\n", "date": "2023-11-22", "categories": []}
{"id": "2305.16213", "title": "ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with\n  Variational Score Distillation", "abstract": "  Score distillation sampling (SDS) has shown great promise in text-to-3D\ngeneration by distilling pretrained large-scale text-to-image diffusion models,\nbut suffers from over-saturation, over-smoothing, and low-diversity problems.\nIn this work, we propose to model the 3D parameter as a random variable instead\nof a constant as in SDS and present variational score distillation (VSD), a\nprincipled particle-based variational framework to explain and address the\naforementioned issues in text-to-3D generation. We show that SDS is a special\ncase of VSD and leads to poor samples with both small and large CFG weights. In\ncomparison, VSD works well with various CFG weights as ancestral sampling from\ndiffusion models and simultaneously improves the diversity and sample quality\nwith a common CFG weight (i.e., $7.5$). We further present various improvements\nin the design space for text-to-3D such as distillation time schedule and\ndensity initialization, which are orthogonal to the distillation algorithm yet\nnot well explored. Our overall approach, dubbed ProlificDreamer, can generate\nhigh rendering resolution (i.e., $512\\times512$) and high-fidelity NeRF with\nrich structure and complex effects (e.g., smoke and drops). Further,\ninitialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and\nphoto-realistic. Project page and codes:\nhttps://ml.cs.tsinghua.edu.cn/prolificdreamer/\n", "date": "2023-05-25", "categories": []}
{"id": "2306.04889", "title": "ShaDDR: Interactive Example-Based Geometry and Texture Generation via 3D\n  Shape Detailization and Differentiable Rendering", "abstract": "  We present ShaDDR, an example-based deep generative neural network which\nproduces a high-resolution textured 3D shape through geometry detailization and\nconditional texture generation applied to an input coarse voxel shape. Trained\non a small set of detailed and textured exemplar shapes, our method learns to\ndetailize the geometry via multi-resolution voxel upsampling and generate\ntextures on voxel surfaces via differentiable rendering against exemplar\ntexture images from a few views. The generation is interactive, taking less\nthan 1 second to produce a 3D model with voxel resolutions up to 512^3. The\ngenerated shape preserves the overall structure of the input coarse voxel\nmodel, while the style of the generated geometric details and textures can be\nmanipulated through learned latent codes. In the experiments, we show that our\nmethod can generate higher-resolution shapes with plausible and improved\ngeometric details and clean textures compared to prior works. Furthermore, we\nshowcase the ability of our method to learn geometric details and textures from\nshapes reconstructed from real-world photos. In addition, we have developed an\ninteractive modeling application to demonstrate the generalizability of our\nmethod to various user inputs and the controllability it offers, allowing users\nto interactively sculpt a coarse voxel shape to define the overall structure of\nthe detailized 3D shape. Code and data are available at\nhttps://github.com/qiminchen/ShaDDR.\n", "date": "2023-06-08", "categories": []}
{"id": "2311.10123", "title": "MetaDreamer: Efficient Text-to-3D Creation With Disentangling Geometry\n  and Texture", "abstract": "  Generative models for 3D object synthesis have seen significant advancements\nwith the incorporation of prior knowledge distilled from 2D diffusion models.\nNevertheless, challenges persist in the form of multi-view geometric\ninconsistencies and slow generation speeds within the existing 3D synthesis\nframeworks. This can be attributed to two factors: firstly, the deficiency of\nabundant geometric a priori knowledge in optimization, and secondly, the\nentanglement issue between geometry and texture in conventional 3D generation\nmethods.In response, we introduce MetaDreammer, a two-stage optimization\napproach that leverages rich 2D and 3D prior knowledge. In the first stage, our\nemphasis is on optimizing the geometric representation to ensure multi-view\nconsistency and accuracy of 3D objects. In the second stage, we concentrate on\nfine-tuning the geometry and optimizing the texture, thereby achieving a more\nrefined 3D object. Through leveraging 2D and 3D prior knowledge in two stages,\nrespectively, we effectively mitigate the interdependence between geometry and\ntexture. MetaDreamer establishes clear optimization objectives for each stage,\nresulting in significant time savings in the 3D generation process. Ultimately,\nMetaDreamer can generate high-quality 3D objects based on textual prompts\nwithin 20 minutes, and to the best of our knowledge, it is the most efficient\ntext-to-3D generation method. Furthermore, we introduce image control into the\nprocess, enhancing the controllability of 3D generation. Extensive empirical\nevidence confirms that our method is not only highly efficient but also\nachieves a quality level that is at the forefront of current state-of-the-art\n3D generation techniques.\n", "date": "2023-11-16", "categories": []}
{"id": "2311.09571", "title": "3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score\n  Distillation", "abstract": "  In this work we develop 3D Paintbrush, a technique for automatically\ntexturing local semantic regions on meshes via text descriptions. Our method is\ndesigned to operate directly on meshes, producing texture maps which seamlessly\nintegrate into standard graphics pipelines. We opt to simultaneously produce a\nlocalization map (to specify the edit region) and a texture map which conforms\nto it. This synergistic approach improves the quality of both the localization\nand the stylization. To enhance the details and resolution of the textured\narea, we leverage multiple stages of a cascaded diffusion model to supervise\nour local editing technique with generative priors learned from images at\ndifferent resolutions. Our technique, referred to as Cascaded Score\nDistillation (CSD), simultaneously distills scores at multiple resolutions in a\ncascaded fashion, enabling control over both the granularity and global\nunderstanding of the supervision. We demonstrate the effectiveness of 3D\nPaintbrush to locally texture a variety of shapes within different semantic\nregions. Project page: https://threedle.github.io/3d-paintbrush\n", "date": "2023-11-16", "categories": []}
{"id": "2311.09217", "title": "DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction\n  Model", "abstract": "  We propose \\textbf{DMV3D}, a novel 3D generation approach that uses a\ntransformer-based 3D large reconstruction model to denoise multi-view\ndiffusion. Our reconstruction model incorporates a triplane NeRF representation\nand can denoise noisy multi-view images via NeRF reconstruction and rendering,\nachieving single-stage 3D generation in $\\sim$30s on single A100 GPU. We train\n\\textbf{DMV3D} on large-scale multi-view image datasets of highly diverse\nobjects using only image reconstruction losses, without accessing 3D assets. We\ndemonstrate state-of-the-art results for the single-image reconstruction\nproblem where probabilistic modeling of unseen object parts is required for\ngenerating diverse reconstructions with sharp textures. We also show\nhigh-quality text-to-3D generation results outperforming previous 3D diffusion\nmodels. Our project website is at: https://justimyhxu.github.io/projects/dmv3d/ .\n", "date": "2023-11-15", "categories": []}
{"id": "2301.13591", "title": "Zero3D: Semantic-Driven Multi-Category 3D Shape Generation", "abstract": "  Semantic-driven 3D shape generation aims to generate 3D objects conditioned\non text. Previous works face problems with single-category generation,\nlow-frequency 3D details, and requiring a large number of paired datasets for\ntraining. To tackle these challenges, we propose a multi-category conditional\ndiffusion model. Specifically, 1) to alleviate the problem of lack of\nlarge-scale paired data, we bridge the text, 2D image and 3D shape based on the\npre-trained CLIP model, and 2) to obtain the multi-category 3D shape feature,\nwe apply the conditional flow model to generate 3D shape vector conditioned on\nCLIP embedding. 3) to generate multi-category 3D shape, we employ the\nhidden-layer diffusion model conditioned on the multi-category shape vector,\nwhich greatly reduces the training time and memory consumption.\n", "date": "2023-01-31", "categories": []}
{"id": "2311.07885", "title": "One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View\n  Generation and 3D Diffusion", "abstract": "  Recent advancements in open-world 3D object generation have been remarkable,\nwith image-to-3D methods offering superior fine-grained control over their\ntext-to-3D counterparts. However, most existing models fall short in\nsimultaneously providing rapid generation speeds and high fidelity to input\nimages - two features essential for practical applications. In this paper, we\npresent One-2-3-45++, an innovative method that transforms a single image into\na detailed 3D textured mesh in approximately one minute. Our approach aims to\nfully harness the extensive knowledge embedded in 2D diffusion models and\npriors from valuable yet limited 3D data. This is achieved by initially\nfinetuning a 2D diffusion model for consistent multi-view image generation,\nfollowed by elevating these images to 3D with the aid of multi-view conditioned\n3D native diffusion models. Extensive experimental evaluations demonstrate that\nour method can produce high-quality, diverse 3D assets that closely mirror the\noriginal input image. Our project webpage:\nhttps://sudo-ai-3d.github.io/One2345plus_page.\n", "date": "2023-11-14", "categories": []}
{"id": "2310.15008", "title": "Wonder3D: Single Image to 3D using Cross-Domain Diffusion", "abstract": "  In this work, we introduce Wonder3D, a novel method for efficiently\ngenerating high-fidelity textured meshes from single-view images.Recent methods\nbased on Score Distillation Sampling (SDS) have shown the potential to recover\n3D geometry from 2D diffusion priors, but they typically suffer from\ntime-consuming per-shape optimization and inconsistent geometry. In contrast,\ncertain works directly produce 3D information via fast network inferences, but\ntheir results are often of low quality and lack geometric details. To\nholistically improve the quality, consistency, and efficiency of image-to-3D\ntasks, we propose a cross-domain diffusion model that generates multi-view\nnormal maps and the corresponding color images. To ensure consistency, we\nemploy a multi-view cross-domain attention mechanism that facilitates\ninformation exchange across views and modalities. Lastly, we introduce a\ngeometry-aware normal fusion algorithm that extracts high-quality surfaces from\nthe multi-view 2D representations. Our extensive evaluations demonstrate that\nour method achieves high-quality reconstruction results, robust generalization,\nand reasonably good efficiency compared to prior works.\n", "date": "2023-10-23", "categories": []}
{"id": "2311.05230", "title": "ConRad: Image Constrained Radiance Fields for 3D Generation from a\n  Single Image", "abstract": "  We present a novel method for reconstructing 3D objects from a single RGB\nimage. Our method leverages the latest image generation models to infer the\nhidden 3D structure while remaining faithful to the input image. While existing\nmethods obtain impressive results in generating 3D models from text prompts,\nthey do not provide an easy approach for conditioning on input RGB data.\nNa\\\"ive extensions of these methods often lead to improper alignment in\nappearance between the input image and the 3D reconstructions. We address these\nchallenges by introducing Image Constrained Radiance Fields (ConRad), a novel\nvariant of neural radiance fields. ConRad is an efficient 3D representation\nthat explicitly captures the appearance of an input image in one viewpoint. We\npropose a training algorithm that leverages the single RGB image in conjunction\nwith pretrained Diffusion Models to optimize the parameters of a ConRad\nrepresentation. Extensive experiments show that ConRad representations can\nsimplify preservation of image details while producing a realistic 3D\nreconstruction. Compared to existing state-of-the-art baselines, we show that\nour 3D reconstructions remain more faithful to the input and produce more\nconsistent 3D models while demonstrating significantly improved quantitative\nperformance on a ShapeNet object benchmark.\n", "date": "2023-11-09", "categories": []}
{"id": "2311.05461", "title": "Control3D: Towards Controllable Text-to-3D Generation", "abstract": "  Recent remarkable advances in large-scale text-to-image diffusion models have\ninspired a significant breakthrough in text-to-3D generation, pursuing 3D\ncontent creation solely from a given text prompt. However, existing text-to-3D\ntechniques lack a crucial ability in the creative process: interactively\ncontrol and shape the synthetic 3D contents according to users' desired\nspecifications (e.g., sketch). To alleviate this issue, we present the first\nattempt for text-to-3D generation conditioning on the additional hand-drawn\nsketch, namely Control3D, which enhances controllability for users. In\nparticular, a 2D conditioned diffusion model (ControlNet) is remoulded to guide\nthe learning of 3D scene parameterized as NeRF, encouraging each view of 3D\nscene aligned with the given text prompt and hand-drawn sketch. Moreover, we\nexploit a pre-trained differentiable photo-to-sketch model to directly estimate\nthe sketch of the rendered image over synthetic 3D scene. Such estimated sketch\nalong with each sampled view is further enforced to be geometrically consistent\nwith the given sketch, pursuing better controllable text-to-3D generation.\nThrough extensive experiments, we demonstrate that our proposal can generate\naccurate and faithful 3D scenes that align closely with the input text prompts\nand sketches.\n", "date": "2023-11-09", "categories": []}
{"id": "2311.05464", "title": "3DStyle-Diffusion: Pursuing Fine-grained Text-driven 3D Stylization with\n  2D Diffusion Models", "abstract": "  3D content creation via text-driven stylization has played a fundamental\nchallenge to multimedia and graphics community. Recent advances of cross-modal\nfoundation models (e.g., CLIP) have made this problem feasible. Those\napproaches commonly leverage CLIP to align the holistic semantics of stylized\nmesh with the given text prompt. Nevertheless, it is not trivial to enable more\ncontrollable stylization of fine-grained details in 3D meshes solely based on\nsuch semantic-level cross-modal supervision. In this work, we propose a new\n3DStyle-Diffusion model that triggers fine-grained stylization of 3D meshes\nwith additional controllable appearance and geometric guidance from 2D\nDiffusion models. Technically, 3DStyle-Diffusion first parameterizes the\ntexture of 3D mesh into reflectance properties and scene lighting using\nimplicit MLP networks. Meanwhile, an accurate depth map of each sampled view is\nachieved conditioned on 3D mesh. Then, 3DStyle-Diffusion leverages a\npre-trained controllable 2D Diffusion model to guide the learning of rendered\nimages, encouraging the synthesized image of each view semantically aligned\nwith text prompt and geometrically consistent with depth map. This way\nelegantly integrates both image rendering via implicit MLP networks and\ndiffusion process of image synthesis in an end-to-end fashion, enabling a\nhigh-quality fine-grained stylization of 3D meshes. We also build a new dataset\nderived from Objaverse and the evaluation protocol for this task. Through both\nqualitative and quantitative experiments, we validate the capability of our\n3DStyle-Diffusion. Source code and data are available at\n\\url{https://github.com/yanghb22-fdu/3DStyle-Diffusion-Official}.\n", "date": "2023-11-09", "categories": []}
{"id": "2310.19415", "title": "Text-to-3D with Classifier Score Distillation", "abstract": "  Text-to-3D generation has made remarkable progress recently, particularly\nwith methods based on Score Distillation Sampling (SDS) that leverages\npre-trained 2D diffusion models. While the usage of classifier-free guidance is\nwell acknowledged to be crucial for successful optimization, it is considered\nan auxiliary trick rather than the most essential component. In this paper, we\nre-evaluate the role of classifier-free guidance in score distillation and\ndiscover a surprising finding: the guidance alone is enough for effective\ntext-to-3D generation tasks. We name this method Classifier Score Distillation\n(CSD), which can be interpreted as using an implicit classification model for\ngeneration. This new perspective reveals new insights for understanding\nexisting techniques. We validate the effectiveness of CSD across a variety of\ntext-to-3D tasks including shape generation, texture synthesis, and shape\nediting, achieving results superior to those of state-of-the-art methods. Our\nproject page is https://xinyu-andy.github.io/Classifier-Score-Distillation\n", "date": "2023-10-30", "categories": []}
{"id": "2310.18141", "title": "Unsupervised Representation Learning for Diverse Deformable Shape\n  Collections", "abstract": "  We introduce a novel learning-based method for encoding and manipulating 3D\nsurface meshes. Our method is specifically designed to create an interpretable\nembedding space for deformable shape collections. Unlike previous 3D mesh\nautoencoders that require meshes to be in a 1-to-1 correspondence, our approach\nis trained on diverse meshes in an unsupervised manner. Central to our method\nis a spectral pooling technique that establishes a universal latent space,\nbreaking free from traditional constraints of mesh connectivity and shape\ncategories. The entire process consists of two stages. In the first stage, we\nemploy the functional map paradigm to extract point-to-point (p2p) maps between\na collection of shapes in an unsupervised manner. These p2p maps are then\nutilized to construct a common latent space, which ensures straightforward\ninterpretation and independence from mesh connectivity and shape category.\nThrough extensive experiments, we demonstrate that our method achieves\nexcellent reconstructions and produces more realistic and smoother\ninterpolations than baseline approaches.\n", "date": "2023-10-27", "categories": []}
{"id": "2310.16818", "title": "DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion\n  Prior", "abstract": "  We present DreamCraft3D, a hierarchical 3D content generation method that\nproduces high-fidelity and coherent 3D objects. We tackle the problem by\nleveraging a 2D reference image to guide the stages of geometry sculpting and\ntexture boosting. A central focus of this work is to address the consistency\nissue that existing works encounter. To sculpt geometries that render\ncoherently, we perform score distillation sampling via a view-dependent\ndiffusion model. This 3D prior, alongside several training strategies,\nprioritizes the geometry consistency but compromises the texture fidelity. We\nfurther propose Bootstrapped Score Distillation to specifically boost the\ntexture. We train a personalized diffusion model, Dreambooth, on the augmented\nrenderings of the scene, imbuing it with 3D knowledge of the scene being\noptimized. The score distillation from this 3D-aware diffusion prior provides\nview-consistent guidance for the scene. Notably, through an alternating\noptimization of the diffusion prior and 3D scene representation, we achieve\nmutually reinforcing improvements: the optimized 3D scene aids in training the\nscene-specific diffusion model, which offers increasingly view-consistent\nguidance for 3D optimization. The optimization is thus bootstrapped and leads\nto substantial texture boosting. With tailored 3D priors throughout the\nhierarchical generation, DreamCraft3D generates coherent 3D objects with\nphotorealistic renderings, advancing the state-of-the-art in 3D content\ngeneration. Code available at https://github.com/deepseek-ai/DreamCraft3D.\n", "date": "2023-10-25", "categories": []}
{"id": "2212.07275", "title": "PhoMoH: Implicit Photorealistic 3D Models of Human Heads", "abstract": "  We present PhoMoH, a neural network methodology to construct generative\nmodels of photo-realistic 3D geometry and appearance of human heads including\nhair, beards, an oral cavity, and clothing. In contrast to prior work, PhoMoH\nmodels the human head using neural fields, thus supporting complex topology.\nInstead of learning a head model from scratch, we propose to augment an\nexisting expressive head model with new features. Concretely, we learn a highly\ndetailed geometry network layered on top of a mid-resolution head model\ntogether with a detailed, local geometry-aware, and disentangled color field.\nOur proposed architecture allows us to learn photo-realistic human head models\nfrom relatively little data. The learned generative geometry and appearance\nnetworks can be sampled individually and enable the creation of diverse and\nrealistic human heads. Extensive experiments validate our method qualitatively\nand across different metrics.\n", "date": "2022-12-14", "categories": []}
{"id": "2307.16605", "title": "VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive\n  Representation", "abstract": "  Conditional 3D generation is undergoing a significant advancement, enabling\nthe free creation of 3D content from inputs such as text or 2D images. However,\nprevious approaches have suffered from low inference efficiency, limited\ngeneration categories, and restricted downstream applications. In this work, we\nrevisit the impact of different 3D representations on generation quality and\nefficiency. We propose a progressive generation method through Voxel-Point\nProgressive Representation (VPP). VPP leverages structured voxel representation\nin the proposed Voxel Semantic Generator and the sparsity of unstructured point\nrepresentation in the Point Upsampler, enabling efficient generation of\nmulti-category objects. VPP can generate high-quality 8K point clouds within\n0.2 seconds. Additionally, the masked generation Transformer allows for various\n3D downstream tasks, such as generation, editing, completion, and pre-training.\nExtensive experiments demonstrate that VPP efficiently generates high-fidelity\nand diverse 3D shapes across different categories, while also exhibiting\nexcellent representation transfer performance. Codes will be released at\n\\url{https://github.com/qizekun/VPP}.\n", "date": "2023-07-28", "categories": []}
{"id": "2211.14108", "title": "3DDesigner: Towards Photorealistic 3D Object Generation and Editing with\n  Text-guided Diffusion Models", "abstract": "  Text-guided diffusion models have shown superior performance in image/video\ngeneration and editing. While few explorations have been performed in 3D\nscenarios. In this paper, we discuss three fundamental and interesting problems\non this topic. First, we equip text-guided diffusion models to achieve\n3D-consistent generation. Specifically, we integrate a NeRF-like neural field\nto generate low-resolution coarse results for a given camera view. Such results\ncan provide 3D priors as condition information for the following diffusion\nprocess. During denoising diffusion, we further enhance the 3D consistency by\nmodeling cross-view correspondences with a novel two-stream (corresponding to\ntwo different views) asynchronous diffusion process. Second, we study 3D local\nediting and propose a two-step solution that can generate 360-degree\nmanipulated results by editing an object from a single view. Step 1, we propose\nto perform 2D local editing by blending the predicted noises. Step 2, we\nconduct a noise-to-text inversion process that maps 2D blended noises into the\nview-independent text embedding space. Once the corresponding text embedding is\nobtained, 360-degree images can be generated. Last but not least, we extend our\nmodel to perform one-shot novel view synthesis by fine-tuning on a single\nimage, firstly showing the potential of leveraging text guidance for novel view\nsynthesis. Extensive experiments and various applications show the prowess of\nour 3DDesigner. The project page is available at\nhttps://3ddesigner-diffusion.github.io/.\n", "date": "2022-11-25", "categories": []}
{"id": "2310.07814", "title": "Explorable Mesh Deformation Subspaces from Unstructured Generative\n  Models", "abstract": "  Exploring variations of 3D shapes is a time-consuming process in traditional\n3D modeling tools. Deep generative models of 3D shapes often feature continuous\nlatent spaces that can, in principle, be used to explore potential variations\nstarting from a set of input shapes. In practice, doing so can be problematic:\nlatent spaces are high dimensional and hard to visualize, contain shapes that\nare not relevant to the input shapes, and linear paths through them often lead\nto sub-optimal shape transitions. Furthermore, one would ideally be able to\nexplore variations in the original high-quality meshes used to train the\ngenerative model, not its lower-quality output geometry. In this paper, we\npresent a method to explore variations among a given set of landmark shapes by\nconstructing a mapping from an easily-navigable 2D exploration space to a\nsubspace of a pre-trained generative model. We first describe how to find a\nmapping that spans the set of input landmark shapes and exhibits smooth\nvariations between them. We then show how to turn the variations in this\nsubspace into deformation fields, to transfer those variations to high-quality\nmeshes for the landmark shapes. Our results show that our method can produce\nvisually-pleasing and easily-navigable 2D exploration spaces for several\ndifferent shape categories, especially as compared to prior work on learning\ndeformation spaces for 3D shapes.\n", "date": "2023-10-11", "categories": []}
{"id": "2310.02714", "title": "GETAvatar: Generative Textured Meshes for Animatable Human Avatars", "abstract": "  We study the problem of 3D-aware full-body human generation, aiming at\ncreating animatable human avatars with high-quality textures and geometries.\nGenerally, two challenges remain in this field: i) existing methods struggle to\ngenerate geometries with rich realistic details such as the wrinkles of\ngarments; ii) they typically utilize volumetric radiance fields and neural\nrenderers in the synthesis process, making high-resolution rendering\nnon-trivial. To overcome these problems, we propose GETAvatar, a Generative\nmodel that directly generates Explicit Textured 3D meshes for animatable human\nAvatar, with photo-realistic appearance and fine geometric details.\nSpecifically, we first design an articulated 3D human representation with\nexplicit surface modeling, and enrich the generated humans with realistic\nsurface details by learning from the 2D normal maps of 3D scan data. Second,\nwith the explicit mesh representation, we can use a rasterization-based\nrenderer to perform surface rendering, allowing us to achieve high-resolution\nimage generation efficiently. Extensive experiments demonstrate that GETAvatar\nachieves state-of-the-art performance on 3D-aware human generation both in\nappearance and geometry quality. Notably, GETAvatar can generate images at\n512x512 resolution with 17FPS and 1024x1024 resolution with 14FPS, improving\nupon previous methods by 2x. Our code and models will be available.\n", "date": "2023-10-04", "categories": []}
{"id": "2310.02753", "title": "MUNCH: Modelling Unique 'N Controllable Heads", "abstract": "  The automated generation of 3D human heads has been an intriguing and\nchallenging task for computer vision researchers. Prevailing methods synthesize\nrealistic avatars but with limited control over the diversity and quality of\nrendered outputs and suffer from limited correlation between shape and texture\nof the character. We propose a method that offers quality, diversity, control,\nand realism along with explainable network design, all desirable features to\ngame-design artists in the domain. First, our proposed Geometry Generator\nidentifies disentangled latent directions and generate novel and diverse\nsamples. A Render Map Generator then learns to synthesize multiply high-fidelty\nphysically-based render maps including Albedo, Glossiness, Specular, and\nNormals. For artists preferring fine-grained control over the output, we\nintroduce a novel Color Transformer Model that allows semantic color control\nover generated maps. We also introduce quantifiable metrics called Uniqueness\nand Novelty and a combined metric to test the overall performance of our model.\nDemo for both shapes and textures can be found:\nhttps://munch-seven.vercel.app/. We will release our model along with the\nsynthetic dataset.\n", "date": "2023-10-04", "categories": []}
{"id": "2303.13873", "title": "Fantasia3D: Disentangling Geometry and Appearance for High-quality\n  Text-to-3D Content Creation", "abstract": "  Automatic 3D content creation has achieved rapid progress recently due to the\navailability of pre-trained, large language models and image diffusion models,\nforming the emerging topic of text-to-3D content creation. Existing text-to-3D\nmethods commonly use implicit scene representations, which couple the geometry\nand appearance via volume rendering and are suboptimal in terms of recovering\nfiner geometries and achieving photorealistic rendering; consequently, they are\nless effective for generating high-quality 3D assets. In this work, we propose\na new method of Fantasia3D for high-quality text-to-3D content creation. Key to\nFantasia3D is the disentangled modeling and learning of geometry and\nappearance. For geometry learning, we rely on a hybrid scene representation,\nand propose to encode surface normal extracted from the representation as the\ninput of the image diffusion model. For appearance modeling, we introduce the\nspatially varying bidirectional reflectance distribution function (BRDF) into\nthe text-to-3D task, and learn the surface material for photorealistic\nrendering of the generated surface. Our disentangled framework is more\ncompatible with popular graphics engines, supporting relighting, editing, and\nphysical simulation of the generated 3D assets. We conduct thorough experiments\nthat show the advantages of our method over existing ones under different\ntext-to-3D task settings. Project page and source codes:\nhttps://fantasia3d.github.io/.\n", "date": "2023-03-24", "categories": []}
{"id": "2303.15181", "title": "DreamStone: Image as Stepping Stone for Text-Guided 3D Shape Generation", "abstract": "  In this paper, we present a new text-guided 3D shape generation approach\nDreamStone that uses images as a stepping stone to bridge the gap between text\nand shape modalities for generating 3D shapes without requiring paired text and\n3D data. The core of our approach is a two-stage feature-space alignment\nstrategy that leverages a pre-trained single-view reconstruction (SVR) model to\nmap CLIP features to shapes: to begin with, map the CLIP image feature to the\ndetail-rich 3D shape space of the SVR model, then map the CLIP text feature to\nthe 3D shape space through encouraging the CLIP-consistency between rendered\nimages and the input text. Besides, to extend beyond the generative capability\nof the SVR model, we design a text-guided 3D shape stylization module that can\nenhance the output shapes with novel structures and textures. Further, we\nexploit pre-trained text-to-image diffusion models to enhance the generative\ndiversity, fidelity, and stylization capability. Our approach is generic,\nflexible, and scalable, and it can be easily integrated with various SVR models\nto expand the generative space and improve the generative fidelity. Extensive\nexperimental results demonstrate that our approach outperforms the\nstate-of-the-art methods in terms of generative quality and consistency with\nthe input text. Codes and models are released at\nhttps://github.com/liuzhengzhe/DreamStone-ISS.\n", "date": "2023-03-24", "categories": []}
{"id": "2309.14600", "title": "Progressive Text-to-3D Generation for Automatic 3D Prototyping", "abstract": "  Text-to-3D generation is to craft a 3D object according to a natural language\ndescription. This can significantly reduce the workload for manually designing\n3D models and provide a more natural way of interaction for users. However,\nthis problem remains challenging in recovering the fine-grained details\neffectively and optimizing a large-size 3D output efficiently. Inspired by the\nsuccess of progressive learning, we propose a Multi-Scale Triplane Network\n(MTN) and a new progressive learning strategy. As the name implies, the\nMulti-Scale Triplane Network consists of four triplanes transitioning from low\nto high resolution. The low-resolution triplane could serve as an initial shape\nfor the high-resolution ones, easing the optimization difficulty. To further\nenable the fine-grained details, we also introduce the progressive learning\nstrategy, which explicitly demands the network to shift its focus of attention\nfrom simple coarse-grained patterns to difficult fine-grained patterns. Our\nexperiment verifies that the proposed method performs favorably against\nexisting methods. For even the most challenging descriptions, where most\nexisting methods struggle to produce a viable shape, our proposed method\nconsistently delivers. We aspire for our work to pave the way for automatic 3D\nprototyping via natural language descriptions.\n", "date": "2023-09-26", "categories": []}
{"id": "2303.05916", "title": "GECCO: Geometrically-Conditioned Point Diffusion Models", "abstract": "  Diffusion models generating images conditionally on text, such as Dall-E 2\nand Stable Diffusion, have recently made a splash far beyond the computer\nvision community. Here, we tackle the related problem of generating point\nclouds, both unconditionally, and conditionally with images. For the latter, we\nintroduce a novel geometrically-motivated conditioning scheme based on\nprojecting sparse image features into the point cloud and attaching them to\neach individual point, at every step in the denoising process. This approach\nimproves geometric consistency and yields greater fidelity than current methods\nrelying on unstructured, global latent codes. Additionally, we show how to\napply recent continuous-time diffusion schemes. Our method performs on par or\nabove the state of art on conditional and unconditional experiments on\nsynthetic data, while being faster, lighter, and delivering tractable\nlikelihoods. We show it can also scale to diverse indoors scenes.\n", "date": "2023-03-10", "categories": []}
{"id": "2309.13006", "title": "Deep3DSketch+: Rapid 3D Modeling from Single Free-hand Sketches", "abstract": "  The rapid development of AR/VR brings tremendous demands for 3D content.\nWhile the widely-used Computer-Aided Design (CAD) method requires a\ntime-consuming and labor-intensive modeling process, sketch-based 3D modeling\noffers a potential solution as a natural form of computer-human interaction.\nHowever, the sparsity and ambiguity of sketches make it challenging to generate\nhigh-fidelity content reflecting creators' ideas. Precise drawing from multiple\nviews or strategic step-by-step drawings is often required to tackle the\nchallenge but is not friendly to novice users. In this work, we introduce a\nnovel end-to-end approach, Deep3DSketch+, which performs 3D modeling using only\na single free-hand sketch without inputting multiple sketches or view\ninformation. Specifically, we introduce a lightweight generation network for\nefficient inference in real-time and a structural-aware adversarial training\napproach with a Stroke Enhancement Module (SEM) to capture the structural\ninformation to facilitate learning of the realistic and fine-detailed shape\nstructures for high-fidelity performance. Extensive experiments demonstrated\nthe effectiveness of our approach with the state-of-the-art (SOTA) performance\non both synthetic and real datasets.\n", "date": "2023-09-22", "categories": []}
{"id": "2305.11870", "title": "Chupa: Carving 3D Clothed Humans from Skinned Shape Priors using 2D\n  Diffusion Probabilistic Models", "abstract": "  We propose a 3D generation pipeline that uses diffusion models to generate\nrealistic human digital avatars. Due to the wide variety of human identities,\nposes, and stochastic details, the generation of 3D human meshes has been a\nchallenging problem. To address this, we decompose the problem into 2D normal\nmap generation and normal map-based 3D reconstruction. Specifically, we first\nsimultaneously generate realistic normal maps for the front and backside of a\nclothed human, dubbed dual normal maps, using a pose-conditional diffusion\nmodel. For 3D reconstruction, we \"carve\" the prior SMPL-X mesh to a detailed 3D\nmesh according to the normal maps through mesh optimization. To further enhance\nthe high-frequency details, we present a diffusion resampling scheme on both\nbody and facial regions, thus encouraging the generation of realistic digital\navatars. We also seamlessly incorporate a recent text-to-image diffusion model\nto support text-based human identity control. Our method, namely, Chupa, is\ncapable of generating realistic 3D clothed humans with better perceptual\nquality and identity variety.\n", "date": "2023-05-19", "categories": []}
{"id": "2309.07920", "title": "Large-Vocabulary 3D Diffusion Model with Transformer", "abstract": "  Creating diverse and high-quality 3D assets with an automatic generative\nmodel is highly desirable. Despite extensive efforts on 3D generation, most\nexisting works focus on the generation of a single category or a few\ncategories. In this paper, we introduce a diffusion-based feed-forward\nframework for synthesizing massive categories of real-world 3D objects with a\nsingle generative model. Notably, there are three major challenges for this\nlarge-vocabulary 3D generation: a) the need for expressive yet efficient 3D\nrepresentation; b) large diversity in geometry and texture across categories;\nc) complexity in the appearances of real-world objects. To this end, we propose\na novel triplane-based 3D-aware Diffusion model with TransFormer, DiffTF, for\nhandling challenges via three aspects. 1) Considering efficiency and\nrobustness, we adopt a revised triplane representation and improve the fitting\nspeed and accuracy. 2) To handle the drastic variations in geometry and\ntexture, we regard the features of all 3D objects as a combination of\ngeneralized 3D knowledge and specialized 3D features. To extract generalized 3D\nknowledge from diverse categories, we propose a novel 3D-aware transformer with\nshared cross-plane attention. It learns the cross-plane relations across\ndifferent planes and aggregates the generalized 3D knowledge with specialized\n3D features. 3) In addition, we devise the 3D-aware encoder/decoder to enhance\nthe generalized 3D knowledge in the encoded triplanes for handling categories\nwith complex appearances. Extensive experiments on ShapeNet and OmniObject3D\n(over 200 diverse real-world categories) convincingly demonstrate that a single\nDiffTF model achieves state-of-the-art large-vocabulary 3D object generation\nperformance with large diversity, rich semantics, and high quality.\n", "date": "2023-09-14", "categories": []}
{"id": "2211.10865", "title": "IC3D: Image-Conditioned 3D Diffusion for Shape Generation", "abstract": "  In recent years, Denoising Diffusion Probabilistic Models (DDPMs) have\ndemonstrated exceptional performance in various 2D generative tasks. Following\nthis success, DDPMs have been extended to 3D shape generation, surpassing\nprevious methodologies in this domain. While many of these models are\nunconditional, some have explored the potential of using guidance from\ndifferent modalities. In particular, image guidance for 3D generation has been\nexplored through the utilization of CLIP embeddings. However, these embeddings\nare designed to align images and text, and do not necessarily capture the\nspecific details needed for shape generation. To address this limitation and\nenhance image-guided 3D DDPMs with augmented 3D understanding, we introduce\nCISP (Contrastive Image-Shape Pre-training), obtaining a well-structured\nimage-shape joint embedding space. Building upon CISP, we then introduce IC3D,\na DDPM that harnesses CISP's guidance for 3D shape generation from single-view\nimages. This generative diffusion model outperforms existing benchmarks in both\nquality and diversity of generated 3D shapes. Moreover, despite IC3D's\ngenerative nature, its generated shapes are preferred by human evaluators over\na competitive single-view 3D reconstruction model. These properties contribute\nto a coherent embedding space, enabling latent interpolation and conditioned\ngeneration also from out-of-distribution images. We find IC3D able to generate\ncoherent and diverse completions also when presented with occluded views,\nrendering it applicable in controlled real-world scenarios.\n", "date": "2022-11-20", "categories": []}
{"id": "2309.07125", "title": "Text-Guided Generation and Editing of Compositional 3D Avatars", "abstract": "  Our goal is to create a realistic 3D facial avatar with hair and accessories\nusing only a text description. While this challenge has attracted significant\nrecent interest, existing methods either lack realism, produce unrealistic\nshapes, or do not support editing, such as modifications to the hairstyle. We\nargue that existing methods are limited because they employ a monolithic\nmodeling approach, using a single representation for the head, face, hair, and\naccessories. Our observation is that the hair and face, for example, have very\ndifferent structural qualities that benefit from different representations.\nBuilding on this insight, we generate avatars with a compositional model, in\nwhich the head, face, and upper body are represented with traditional 3D\nmeshes, and the hair, clothing, and accessories with neural radiance fields\n(NeRF). The model-based mesh representation provides a strong geometric prior\nfor the face region, improving realism while enabling editing of the person's\nappearance. By using NeRFs to represent the remaining components, our method is\nable to model and synthesize parts with complex geometry and appearance, such\nas curly hair and fluffy scarves. Our novel system synthesizes these\nhigh-quality compositional avatars from text descriptions. The experimental\nresults demonstrate that our method, Text-guided generation and Editing of\nCompositional Avatars (TECA), produces avatars that are more realistic than\nthose of recent methods while being editable because of their compositional\nnature. For example, our TECA enables the seamless transfer of compositional\nfeatures like hairstyles, scarves, and other accessories between avatars. This\ncapability supports applications such as virtual try-on.\n", "date": "2023-09-13", "categories": []}
{"id": "2309.03550", "title": "Text2Control3D: Controllable 3D Avatar Generation in Neural Radiance\n  Fields using Geometry-Guided Text-to-Image Diffusion Model", "abstract": "  Recent advances in diffusion models such as ControlNet have enabled\ngeometrically controllable, high-fidelity text-to-image generation. However,\nnone of them addresses the question of adding such controllability to\ntext-to-3D generation. In response, we propose Text2Control3D, a controllable\ntext-to-3D avatar generation method whose facial expression is controllable\ngiven a monocular video casually captured with hand-held camera. Our main\nstrategy is to construct the 3D avatar in Neural Radiance Fields (NeRF)\noptimized with a set of controlled viewpoint-aware images that we generate from\nControlNet, whose condition input is the depth map extracted from the input\nvideo. When generating the viewpoint-aware images, we utilize cross-reference\nattention to inject well-controlled, referential facial expression and\nappearance via cross attention. We also conduct low-pass filtering of Gaussian\nlatent of the diffusion model in order to ameliorate the viewpoint-agnostic\ntexture problem we observed from our empirical analysis, where the\nviewpoint-aware images contain identical textures on identical pixel positions\nthat are incomprehensible in 3D. Finally, to train NeRF with the images that\nare viewpoint-aware yet are not strictly consistent in geometry, our approach\nconsiders per-image geometric variation as a view of deformation from a shared\n3D canonical space. Consequently, we construct the 3D avatar in a canonical\nspace of deformable NeRF by learning a set of per-image deformation via\ndeformation field table. We demonstrate the empirical results and discuss the\neffectiveness of our method.\n", "date": "2023-09-07", "categories": []}
{"id": "2309.03599", "title": "Chasing Consistency in Text-to-3D Generation from a Single Image", "abstract": "  Text-to-3D generation from a single-view image is a popular but challenging\ntask in 3D vision. Although numerous methods have been proposed, existing works\nstill suffer from the inconsistency issues, including 1) semantic\ninconsistency, 2) geometric inconsistency, and 3) saturation inconsistency,\nresulting in distorted, overfitted, and over-saturated generations. In light of\nthe above issues, we present Consist3D, a three-stage framework Chasing for\nsemantic-, geometric-, and saturation-Consistent Text-to-3D generation from a\nsingle image, in which the first two stages aim to learn parameterized\nconsistency tokens, and the last stage is for optimization. Specifically, the\nsemantic encoding stage learns a token independent of views and estimations,\npromoting semantic consistency and robustness. Meanwhile, the geometric\nencoding stage learns another token with comprehensive geometry and\nreconstruction constraints under novel-view estimations, reducing overfitting\nand encouraging geometric consistency. Finally, the optimization stage benefits\nfrom the semantic and geometric tokens, allowing a low classifier-free guidance\nscale and therefore preventing oversaturation. Experimental results demonstrate\nthat Consist3D produces more consistent, faithful, and photo-realistic 3D\nassets compared to previous state-of-the-art methods. Furthermore, Consist3D\nalso allows background and object editing through text prompts.\n", "date": "2023-09-07", "categories": []}
{"id": "2306.07881", "title": "Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D\n  Data", "abstract": "  We present Viewset Diffusion, a diffusion-based generator that outputs 3D\nobjects while only using multi-view 2D data for supervision. We note that there\nexists a one-to-one mapping between viewsets, i.e., collections of several 2D\nviews of an object, and 3D models. Hence, we train a diffusion model to\ngenerate viewsets, but design the neural network generator to reconstruct\ninternally corresponding 3D models, thus generating those too. We fit a\ndiffusion model to a large number of viewsets for a given category of objects.\nThe resulting generator can be conditioned on zero, one or more input views.\nConditioned on a single view, it performs 3D reconstruction accounting for the\nambiguity of the task and allowing to sample multiple solutions compatible with\nthe input. The model performs reconstruction efficiently, in a feed-forward\nmanner, and is trained using only rendering losses using as few as three views\nper viewset. Project page: szymanowiczs.github.io/viewset-diffusion.\n", "date": "2023-06-13", "categories": []}
{"id": "2309.00158", "title": "BuilDiff: 3D Building Shape Generation using Single-Image Conditional\n  Point Cloud Diffusion Models", "abstract": "  3D building generation with low data acquisition costs, such as single\nimage-to-3D, becomes increasingly important. However, most of the existing\nsingle image-to-3D building creation works are restricted to those images with\nspecific viewing angles, hence they are difficult to scale to general-view\nimages that commonly appear in practical cases. To fill this gap, we propose a\nnovel 3D building shape generation method exploiting point cloud diffusion\nmodels with image conditioning schemes, which demonstrates flexibility to the\ninput images. By cooperating two conditional diffusion models and introducing a\nregularization strategy during denoising process, our method is able to\nsynthesize building roofs while maintaining the overall structures. We validate\nour framework on two newly built datasets and extensive experiments show that\nour method outperforms previous works in terms of building generation quality.\n", "date": "2023-08-31", "categories": []}
{"id": "2308.16758", "title": "Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation\n  Using only Images", "abstract": "  Generating 3D faces from textual descriptions has a multitude of\napplications, such as gaming, movie, and robotics. Recent progresses have\ndemonstrated the success of unconditional 3D face generation and text-to-3D\nshape generation. However, due to the limited text-3D face data pairs,\ntext-driven 3D face generation remains an open problem. In this paper, we\npropose a text-guided 3D faces generation method, refer as TG-3DFace, for\ngenerating realistic 3D faces using text guidance. Specifically, we adopt an\nunconditional 3D face generation framework and equip it with text conditions,\nwhich learns the text-guided 3D face generation with only text-2D face data. On\ntop of that, we propose two text-to-face cross-modal alignment techniques,\nincluding the global contrastive learning and the fine-grained alignment\nmodule, to facilitate high semantic consistency between generated 3D faces and\ninput texts. Besides, we present directional classifier guidance during the\ninference process, which encourages creativity for out-of-domain generations.\nCompared to the existing methods, TG-3DFace creates more realistic and\naesthetically pleasing 3D faces, boosting 9% multi-view consistency (MVIC) over\nLatent3D. The rendered face images generated by TG-3DFace achieve higher FID\nand CLIP score than text-to-2D face/image generation models, demonstrating our\nsuperiority in generating realistic and semantic-consistent textures.\n", "date": "2023-08-31", "categories": []}
{"id": "2306.03038", "title": "HeadSculpt: Crafting 3D Head Avatars with Text", "abstract": "  Recently, text-guided 3D generative methods have made remarkable advancements\nin producing high-quality textures and geometry, capitalizing on the\nproliferation of large vision-language and image diffusion models. However,\nexisting methods still struggle to create high-fidelity 3D head avatars in two\naspects: (1) They rely mostly on a pre-trained text-to-image diffusion model\nwhilst missing the necessary 3D awareness and head priors. This makes them\nprone to inconsistency and geometric distortions in the generated avatars. (2)\nThey fall short in fine-grained editing. This is primarily due to the inherited\nlimitations from the pre-trained 2D image diffusion models, which become more\npronounced when it comes to 3D head avatars. In this work, we address these\nchallenges by introducing a versatile coarse-to-fine pipeline dubbed HeadSculpt\nfor crafting (i.e., generating and editing) 3D head avatars from textual\nprompts. Specifically, we first equip the diffusion model with 3D awareness by\nleveraging landmark-based control and a learned textual embedding representing\nthe back view appearance of heads, enabling 3D-consistent head avatar\ngenerations. We further propose a novel identity-aware editing score\ndistillation strategy to optimize a textured mesh with a high-resolution\ndifferentiable rendering technique. This enables identity preservation while\nfollowing the editing instruction. We showcase HeadSculpt's superior fidelity\nand editing capabilities through comprehensive experiments and comparisons with\nexisting methods.\n", "date": "2023-06-05", "categories": []}
{"id": "2210.15663", "title": "Deep Generative Models on 3D Representations: A Survey", "abstract": "  Generative models aim to learn the distribution of observed data by\ngenerating new instances. With the advent of neural networks, deep generative\nmodels, including variational autoencoders (VAEs), generative adversarial\nnetworks (GANs), and diffusion models (DMs), have progressed remarkably in\nsynthesizing 2D images. Recently, researchers started to shift focus from 2D to\n3D space, considering that 3D data is more closely aligned with our physical\nworld and holds immense practical potential. However, unlike 2D images, which\npossess an inherent and efficient representation (\\textit{i.e.}, a pixel grid),\nrepresenting 3D data poses significantly greater challenges. Ideally, a robust\n3D representation should be capable of accurately modeling complex shapes and\nappearances while being highly efficient in handling high-resolution data with\nhigh processing speeds and low memory requirements. Regrettably, existing 3D\nrepresentations, such as point clouds, meshes, and neural fields, often fail to\nsatisfy all of these requirements simultaneously. In this survey, we thoroughly\nreview the ongoing developments of 3D generative models, including methods that\nemploy 2D and 3D supervision. Our analysis centers on generative models, with a\nparticular focus on the representations utilized in this context. We believe\nour survey will help the community to track the field's evolution and to spark\ninnovative ideas to propel progress towards solving this challenging task.\n", "date": "2022-10-27", "categories": []}
{"id": "2304.02163", "title": "GINA-3D: Learning to Generate Implicit Neural Assets in the Wild", "abstract": "  Modeling the 3D world from sensor data for simulation is a scalable way of\ndeveloping testing and validation environments for robotic learning problems\nsuch as autonomous driving. However, manually creating or re-creating\nreal-world-like environments is difficult, expensive, and not scalable. Recent\ngenerative model techniques have shown promising progress to address such\nchallenges by learning 3D assets using only plentiful 2D images -- but still\nsuffer limitations as they leverage either human-curated image datasets or\nrenderings from manually-created synthetic 3D environments. In this paper, we\nintroduce GINA-3D, a generative model that uses real-world driving data from\ncamera and LiDAR sensors to create realistic 3D implicit neural assets of\ndiverse vehicles and pedestrians. Compared to the existing image datasets, the\nreal-world driving setting poses new challenges due to occlusions,\nlighting-variations and long-tail distributions. GINA-3D tackles these\nchallenges by decoupling representation learning and generative modeling into\ntwo stages with a learned tri-plane latent structure, inspired by recent\nadvances in generative modeling of images. To evaluate our approach, we\nconstruct a large-scale object-centric dataset containing over 1.2M images of\nvehicles and pedestrians from the Waymo Open Dataset, and a new set of 80K\nimages of long-tail instances such as construction equipment, garbage trucks,\nand cable cars. We compare our model with existing approaches and demonstrate\nthat it achieves state-of-the-art performance in quality and diversity for both\ngenerated images and geometries.\n", "date": "2023-04-04", "categories": []}
{"id": "2308.14244", "title": "HoloFusion: Towards Photo-realistic 3D Generative Modeling", "abstract": "  Diffusion-based image generators can now produce high-quality and diverse\nsamples, but their success has yet to fully translate to 3D generation:\nexisting diffusion methods can either generate low-resolution but 3D consistent\noutputs, or detailed 2D views of 3D objects but with potential structural\ndefects and lacking view consistency or realism. We present HoloFusion, a\nmethod that combines the best of these approaches to produce high-fidelity,\nplausible, and diverse 3D samples while learning from a collection of\nmulti-view 2D images only. The method first generates coarse 3D samples using a\nvariant of the recently proposed HoloDiffusion generator. Then, it\nindependently renders and upsamples a large number of views of the coarse 3D\nmodel, super-resolves them to add detail, and distills those into a single,\nhigh-fidelity implicit 3D representation, which also ensures view consistency\nof the final renders. The super-resolution network is trained as an integral\npart of HoloFusion, end-to-end, and the final distillation uses a new sampling\nscheme to capture the space of super-resolved signals. We compare our method\nagainst existing baselines, including DreamFusion, Get3D, EG3D, and\nHoloDiffusion, and achieve, to the best of our knowledge, the most realistic\nresults on the challenging CO3Dv2 dataset.\n", "date": "2023-08-28", "categories": []}
{"id": "2304.06714", "title": "Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and\n  Reconstruction", "abstract": "  3D-aware image synthesis encompasses a variety of tasks, such as scene\ngeneration and novel view synthesis from images. Despite numerous task-specific\nmethods, developing a comprehensive model remains challenging. In this paper,\nwe present SSDNeRF, a unified approach that employs an expressive diffusion\nmodel to learn a generalizable prior of neural radiance fields (NeRF) from\nmulti-view images of diverse objects. Previous studies have used two-stage\napproaches that rely on pretrained NeRFs as real data to train diffusion\nmodels. In contrast, we propose a new single-stage training paradigm with an\nend-to-end objective that jointly optimizes a NeRF auto-decoder and a latent\ndiffusion model, enabling simultaneous 3D reconstruction and prior learning,\neven from sparsely available views. At test time, we can directly sample the\ndiffusion prior for unconditional generation, or combine it with arbitrary\nobservations of unseen objects for NeRF reconstruction. SSDNeRF demonstrates\nrobust results comparable to or better than leading task-specific methods in\nunconditional generation and single/sparse-view 3D reconstruction.\n", "date": "2023-04-13", "categories": []}
{"id": "2305.01921", "title": "DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross\n  Diffusion", "abstract": "  While the community of 3D point cloud generation has witnessed a big growth\nin recent years, there still lacks an effective way to enable intuitive user\ncontrol in the generation process, hence limiting the general utility of such\nmethods. Since an intuitive way of decomposing a shape is through its parts, we\npropose to tackle the task of controllable part-based point cloud generation.\nWe introduce DiffFacto, a novel probabilistic generative model that learns the\ndistribution of shapes with part-level control. We propose a factorization that\nmodels independent part style and part configuration distributions and presents\na novel cross-diffusion network that enables us to generate coherent and\nplausible shapes under our proposed factorization. Experiments show that our\nmethod is able to generate novel shapes with multiple axes of control. It\nachieves state-of-the-art part-level generation quality and generates plausible\nand coherent shapes while enabling various downstream editing applications such\nas shape interpolation, mixing, and transformation editing. Project website:\nhttps://difffacto.github.io/\n", "date": "2023-05-03", "categories": []}
{"id": "2306.11510", "title": "Pushing the Limits of 3D Shape Generation at Scale", "abstract": "  We present a significant breakthrough in 3D shape generation by scaling it to\nunprecedented dimensions. Through the adaptation of the Auto-Regressive model\nand the utilization of large language models, we have developed a remarkable\nmodel with an astounding 3.6 billion trainable parameters, establishing it as\nthe largest 3D shape generation model to date, named Argus-3D. Our approach\naddresses the limitations of existing methods by enhancing the quality and\ndiversity of generated 3D shapes. To tackle the challenges of high-resolution\n3D shape generation, our model incorporates tri-plane features as latent\nrepresentations, effectively reducing computational complexity. Additionally,\nwe introduce a discrete codebook for efficient quantization of these\nrepresentations. Leveraging the power of transformers, we enable multi-modal\nconditional generation, facilitating the production of diverse and visually\nimpressive 3D shapes. To train our expansive model, we leverage an ensemble of\npublicly-available 3D datasets, consisting of a comprehensive collection of\napproximately 900,000 objects from renowned repositories such as ModelNet40,\nShapeNet, Pix3D, 3D-Future, and Objaverse. This diverse dataset empowers our\nmodel to learn from a wide range of object variations, bolstering its ability\nto generate high-quality and diverse 3D shapes. Extensive experimentation\ndemonstrate the remarkable efficacy of our approach in significantly improving\nthe visual quality of generated 3D shapes. By pushing the boundaries of 3D\ngeneration, introducing novel methods for latent representation learning, and\nharnessing the power of transformers for multi-modal conditional generation,\nour contributions pave the way for substantial advancements in the field. Our\nwork unlocks new possibilities for applications in gaming, virtual reality,\nproduct design, and other domains that demand high-quality and diverse 3D\nobjects.\n", "date": "2023-06-20", "categories": []}
{"id": "2308.10898", "title": "Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical\n  Deformation", "abstract": "  We study the problem of few-shot physically-aware articulated mesh\ngeneration. By observing an articulated object dataset containing only a few\nexamples, we wish to learn a model that can generate diverse meshes with high\nvisual fidelity and physical validity. Previous mesh generative models either\nhave difficulties in depicting a diverse data space from only a few examples or\nfail to ensure physical validity of their samples. Regarding the above\nchallenges, we propose two key innovations, including 1) a hierarchical mesh\ndeformation-based generative model based upon the divide-and-conquer philosophy\nto alleviate the few-shot challenge by borrowing transferrable deformation\npatterns from large scale rigid meshes and 2) a physics-aware deformation\ncorrection scheme to encourage physically plausible generations. We conduct\nextensive experiments on 6 articulated categories to demonstrate the\nsuperiority of our method in generating articulated meshes with better\ndiversity, higher visual fidelity, and better physical validity over previous\nmethods in the few-shot setting. Further, we validate solid contributions of\nour two innovations in the ablation study. Project page with code is available\nat https://meowuu7.github.io/few-arti-obj-gen.\n", "date": "2023-08-21", "categories": []}
{"id": "2308.09705", "title": "Guide3D: Create 3D Avatars from Text and Image Guidance", "abstract": "  Recently, text-to-image generation has exhibited remarkable advancements,\nwith the ability to produce visually impressive results. In contrast,\ntext-to-3D generation has not yet reached a comparable level of quality.\nExisting methods primarily rely on text-guided score distillation sampling\n(SDS), and they encounter difficulties in transferring 2D attributes of the\ngenerated images to 3D content. In this work, we aim to develop an effective 3D\ngenerative model capable of synthesizing high-resolution textured meshes by\nleveraging both textual and image information. To this end, we introduce\nGuide3D, a zero-shot text-and-image-guided generative model for 3D avatar\ngeneration based on diffusion models. Our model involves (1) generating\nsparse-view images of a text-consistent character using diffusion models, and\n(2) jointly optimizing multi-resolution differentiable marching tetrahedral\ngrids with pixel-aligned image features. We further propose a similarity-aware\nfeature fusion strategy for efficiently integrating features from different\nviews. Moreover, we introduce two novel training objectives as an alternative\nto calculating SDS, significantly enhancing the optimization process. We\nthoroughly evaluate the performance and components of our framework, which\noutperforms the current state-of-the-art in producing topologically and\nstructurally correct geometry and high-resolution textures. Guide3D enables the\ndirect transfer of 2D-generated images to the 3D space. Our code will be made\npublicly available.\n", "date": "2023-08-18", "categories": []}
{"id": "2308.09712", "title": "HumanLiff: Layer-wise 3D Human Generation with Diffusion Model", "abstract": "  3D human generation from 2D images has achieved remarkable progress through\nthe synergistic utilization of neural rendering and generative models. Existing\n3D human generative models mainly generate a clothed 3D human as an\nundetectable 3D model in a single pass, while rarely considering the layer-wise\nnature of a clothed human body, which often consists of the human body and\nvarious clothes such as underwear, outerwear, trousers, shoes, etc. In this\nwork, we propose HumanLiff, the first layer-wise 3D human generative model with\na unified diffusion process. Specifically, HumanLiff firstly generates\nminimal-clothed humans, represented by tri-plane features, in a canonical\nspace, and then progressively generates clothes in a layer-wise manner. In this\nway, the 3D human generation is thus formulated as a sequence of\ndiffusion-based 3D conditional generation. To reconstruct more fine-grained 3D\nhumans with tri-plane representation, we propose a tri-plane shift operation\nthat splits each tri-plane into three sub-planes and shifts these sub-planes to\nenable feature grid subdivision. To further enhance the controllability of 3D\ngeneration with 3D layered conditions, HumanLiff hierarchically fuses tri-plane\nfeatures and 3D layered conditions to facilitate the 3D diffusion model\nlearning. Extensive experiments on two layer-wise 3D human datasets, SynBody\n(synthetic) and TightCap (real-world), validate that HumanLiff significantly\noutperforms state-of-the-art methods in layer-wise 3D human generation. Our\ncode will be available at https://skhu101.github.io/HumanLiff.\n", "date": "2023-08-18", "categories": []}
{"id": "2212.01103", "title": "3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation", "abstract": "  Text-guided 3D object generation aims to generate 3D objects described by\nuser-defined captions, which paves a flexible way to visualize what we\nimagined. Although some works have been devoted to solving this challenging\ntask, these works either utilize some explicit 3D representations (e.g., mesh),\nwhich lack texture and require post-processing for rendering photo-realistic\nviews; or require individual time-consuming optimization for every single case.\nHere, we make the first attempt to achieve generic text-guided cross-category\n3D object generation via a new 3D-TOGO model, which integrates a text-to-views\ngeneration module and a views-to-3D generation module. The text-to-views\ngeneration module is designed to generate different views of the target 3D\nobject given an input caption. prior-guidance, caption-guidance and view\ncontrastive learning are proposed for achieving better view-consistency and\ncaption similarity. Meanwhile, a pixelNeRF model is adopted for the views-to-3D\ngeneration module to obtain the implicit 3D neural representation from the\npreviously-generated views. Our 3D-TOGO model generates 3D objects in the form\nof the neural radiance field with good texture and requires no time-cost\noptimization for every single caption. Besides, 3D-TOGO can control the\ncategory, color and shape of generated 3D objects with the input caption.\nExtensive experiments on the largest 3D object dataset (i.e., ABO) are\nconducted to verify that 3D-TOGO can better generate high-quality 3D objects\naccording to the input captions across 98 different categories, in terms of\nPSNR, SSIM, LPIPS and CLIP-score, compared with text-NeRF and Dreamfields.\n", "date": "2022-12-02", "categories": []}
{"id": "2307.14051", "title": "3D Semantic Subspace Traverser: Empowering 3D Generative Model with\n  Shape Editing Capability", "abstract": "  Shape generation is the practice of producing 3D shapes as various\nrepresentations for 3D content creation. Previous studies on 3D shape\ngeneration have focused on shape quality and structure, without or less\nconsidering the importance of semantic information. Consequently, such\ngenerative models often fail to preserve the semantic consistency of shape\nstructure or enable manipulation of the semantic attributes of shapes during\ngeneration. In this paper, we proposed a novel semantic generative model named\n3D Semantic Subspace Traverser that utilizes semantic attributes for\ncategory-specific 3D shape generation and editing. Our method utilizes implicit\nfunctions as the 3D shape representation and combines a novel latent-space GAN\nwith a linear subspace model to discover semantic dimensions in the local\nlatent space of 3D shapes. Each dimension of the subspace corresponds to a\nparticular semantic attribute, and we can edit the attributes of generated\nshapes by traversing the coefficients of those dimensions. Experimental results\ndemonstrate that our method can produce plausible shapes with complex\nstructures and enable the editing of semantic attributes. The code and trained\nmodels are available at\nhttps://github.com/TrepangCat/3D_Semantic_Subspace_Traverser\n", "date": "2023-07-26", "categories": []}
{"id": "2308.02874", "title": "Sketch and Text Guided Diffusion Model for Colored Point Cloud\n  Generation", "abstract": "  Diffusion probabilistic models have achieved remarkable success in text\nguided image generation. However, generating 3D shapes is still challenging due\nto the lack of sufficient data containing 3D models along with their\ndescriptions. Moreover, text based descriptions of 3D shapes are inherently\nambiguous and lack details. In this paper, we propose a sketch and text guided\nprobabilistic diffusion model for colored point cloud generation that\nconditions the denoising process jointly with a hand drawn sketch of the object\nand its textual description. We incrementally diffuse the point coordinates and\ncolor values in a joint diffusion process to reach a Gaussian distribution.\nColored point cloud generation thus amounts to learning the reverse diffusion\nprocess, conditioned by the sketch and text, to iteratively recover the desired\nshape and color. Specifically, to learn effective sketch-text embedding, our\nmodel adaptively aggregates the joint embedding of text prompt and the sketch\nbased on a capsule attention network. Our model uses staged diffusion to\ngenerate the shape and then assign colors to different parts conditioned on the\nappearance prompt while preserving precise shapes from the first stage. This\ngives our model the flexibility to extend to multiple tasks, such as appearance\nre-editing and part segmentation. Experimental results demonstrate that our\nmodel outperforms recent state-of-the-art in point cloud generation.\n", "date": "2023-08-05", "categories": []}
{"id": "2308.03610", "title": "AvatarVerse: High-quality & Stable 3D Avatar Creation from Text and Pose", "abstract": "  Creating expressive, diverse and high-quality 3D avatars from highly\ncustomized text descriptions and pose guidance is a challenging task, due to\nthe intricacy of modeling and texturing in 3D that ensure details and various\nstyles (realistic, fictional, etc). We present AvatarVerse, a stable pipeline\nfor generating expressive high-quality 3D avatars from nothing but text\ndescriptions and pose guidance. In specific, we introduce a 2D diffusion model\nconditioned on DensePose signal to establish 3D pose control of avatars through\n2D images, which enhances view consistency from partially observed scenarios.\nIt addresses the infamous Janus Problem and significantly stablizes the\ngeneration process. Moreover, we propose a progressive high-resolution 3D\nsynthesis strategy, which obtains substantial improvement over the quality of\nthe created 3D avatars. To this end, the proposed AvatarVerse pipeline achieves\nzero-shot 3D modeling of 3D avatars that are not only more expressive, but also\nin higher quality and fidelity than previous works. Rigorous qualitative\nevaluations and user studies showcase AvatarVerse's superiority in synthesizing\nhigh-fidelity 3D avatars, leading to a new standard in high-quality and stable\n3D avatar creation. Our project page is: https://avatarverse3d.github.io\n", "date": "2023-08-07", "categories": []}
{"id": "2303.15764", "title": "X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic\n  Textual Guidance", "abstract": "  Text-driven 3D stylization is a complex and crucial task in the fields of\ncomputer vision (CV) and computer graphics (CG), aimed at transforming a bare\nmesh to fit a target text. Prior methods adopt text-independent multilayer\nperceptrons (MLPs) to predict the attributes of the target mesh with the\nsupervision of CLIP loss. However, such text-independent architecture lacks\ntextual guidance during predicting attributes, thus leading to unsatisfactory\nstylization and slow convergence. To address these limitations, we present\nX-Mesh, an innovative text-driven 3D stylization framework that incorporates a\nnovel Text-guided Dynamic Attention Module (TDAM). The TDAM dynamically\nintegrates the guidance of the target text by utilizing text-relevant spatial\nand channel-wise attentions during vertex feature extraction, resulting in more\naccurate attribute prediction and faster convergence speed. Furthermore,\nexisting works lack standard benchmarks and automated metrics for evaluation,\noften relying on subjective and non-reproducible user studies to assess the\nquality of stylized 3D assets. To overcome this limitation, we introduce a new\nstandard text-mesh benchmark, namely MIT-30, and two automated metrics, which\nwill enable future research to achieve fair and objective comparisons. Our\nextensive qualitative and quantitative experiments demonstrate that X-Mesh\noutperforms previous state-of-the-art methods.\n", "date": "2023-03-28", "categories": []}
{"id": "2307.16183", "title": "HD-Fusion: Detailed Text-to-3D Generation Leveraging Multiple Noise\n  Estimation", "abstract": "  In this paper, we study Text-to-3D content generation leveraging 2D diffusion\npriors to enhance the quality and detail of the generated 3D models. Recent\nprogress (Magic3D) in text-to-3D has shown that employing high-resolution\n(e.g., 512 x 512) renderings can lead to the production of high-quality 3D\nmodels using latent diffusion priors. To enable rendering at even higher\nresolutions, which has the potential to further augment the quality and detail\nof the models, we propose a novel approach that combines multiple noise\nestimation processes with a pretrained 2D diffusion prior. Distinct from the\nBar-Tal et al.s' study which binds multiple denoised results to generate images\nfrom texts, our approach integrates the computation of scoring distillation\nlosses such as SDS loss and VSD loss which are essential techniques for the 3D\ncontent generation with 2D diffusion priors. We experimentally evaluated the\nproposed approach. The results show that the proposed approach can generate\nhigh-quality details compared to the baselines.\n", "date": "2023-07-30", "categories": []}
{"id": "2307.14918", "title": "GET3D--: Learning GET3D from Unconstrained Image Collections", "abstract": "  The demand for efficient 3D model generation techniques has grown\nexponentially, as manual creation of 3D models is time-consuming and requires\nspecialized expertise. While generative models have shown potential in creating\n3D textured shapes from 2D images, their applicability in 3D industries is\nlimited due to the lack of a well-defined camera distribution in real-world\nscenarios, resulting in low-quality shapes. To overcome this limitation, we\npropose GET3D--, the first method that directly generates textured 3D shapes\nfrom 2D images with unknown pose and scale. GET3D-- comprises a 3D shape\ngenerator and a learnable camera sampler that captures the 6D external changes\non the camera. In addition, We propose a novel training schedule to stably\noptimize both the shape generator and camera sampler in a unified framework. By\ncontrolling external variations using the learnable camera sampler, our method\ncan generate aligned shapes with clear textures. Extensive experiments\ndemonstrate the efficacy of GET3D--, which precisely fits the 6D camera pose\ndistribution and generates high-quality shapes on both synthetic and realistic\nunconstrained datasets.\n", "date": "2023-07-27", "categories": []}
{"id": "2307.13908", "title": "Points-to-3D: Bridging the Gap between Sparse Points and\n  Shape-Controllable Text-to-3D Generation", "abstract": "  Text-to-3D generation has recently garnered significant attention, fueled by\n2D diffusion models trained on billions of image-text pairs. Existing methods\nprimarily rely on score distillation to leverage the 2D diffusion priors to\nsupervise the generation of 3D models, e.g., NeRF. However, score distillation\nis prone to suffer the view inconsistency problem, and implicit NeRF modeling\ncan also lead to an arbitrary shape, thus leading to less realistic and\nuncontrollable 3D generation. In this work, we propose a flexible framework of\nPoints-to-3D to bridge the gap between sparse yet freely available 3D points\nand realistic shape-controllable 3D generation by distilling the knowledge from\nboth 2D and 3D diffusion models. The core idea of Points-to-3D is to introduce\ncontrollable sparse 3D points to guide the text-to-3D generation. Specifically,\nwe use the sparse point cloud generated from the 3D diffusion model, Point-E,\nas the geometric prior, conditioned on a single reference image. To better\nutilize the sparse 3D points, we propose an efficient point cloud guidance loss\nto adaptively drive the NeRF's geometry to align with the shape of the sparse\n3D points. In addition to controlling the geometry, we propose to optimize the\nNeRF for a more view-consistent appearance. To be specific, we perform score\ndistillation to the publicly available 2D image diffusion model ControlNet,\nconditioned on text as well as depth map of the learned compact geometry.\nQualitative and quantitative comparisons demonstrate that Points-to-3D improves\nview consistency and achieves good shape controllability for text-to-3D\ngeneration. Points-to-3D provides users with a new way to improve and control\ntext-to-3D generation.\n", "date": "2023-07-26", "categories": []}
{"id": "2302.01162", "title": "Get3DHuman: Lifting StyleGAN-Human into a 3D Generative Model using\n  Pixel-aligned Reconstruction Priors", "abstract": "  Fast generation of high-quality 3D digital humans is important to a vast\nnumber of applications ranging from entertainment to professional concerns.\nRecent advances in differentiable rendering have enabled the training of 3D\ngenerative models without requiring 3D ground truths. However, the quality of\nthe generated 3D humans still has much room to improve in terms of both\nfidelity and diversity. In this paper, we present Get3DHuman, a novel 3D human\nframework that can significantly boost the realism and diversity of the\ngenerated outcomes by only using a limited budget of 3D ground-truth data. Our\nkey observation is that the 3D generator can profit from human-related priors\nlearned through 2D human generators and 3D reconstructors. Specifically, we\nbridge the latent space of Get3DHuman with that of StyleGAN-Human via a\nspecially-designed prior network, where the input latent code is mapped to the\nshape and texture feature volumes spanned by the pixel-aligned 3D\nreconstructor. The outcomes of the prior network are then leveraged as the\nsupervisory signals for the main generator network. To ensure effective\ntraining, we further propose three tailored losses applied to the generated\nfeature volumes and the intermediate feature maps. Extensive experiments\ndemonstrate that Get3DHuman greatly outperforms the other state-of-the-art\napproaches and can support a wide range of applications including shape\ninterpolation, shape re-texturing, and single-view reconstruction through\nlatent inversion.\n", "date": "2023-02-02", "categories": []}
{"id": "2306.17843", "title": "Magic123: One Image to High-Quality 3D Object Generation Using Both 2D\n  and 3D Diffusion Priors", "abstract": "  We present Magic123, a two-stage coarse-to-fine approach for high-quality,\ntextured 3D meshes generation from a single unposed image in the wild using\nboth2D and 3D priors. In the first stage, we optimize a neural radiance field\nto produce a coarse geometry. In the second stage, we adopt a memory-efficient\ndifferentiable mesh representation to yield a high-resolution mesh with a\nvisually appealing texture. In both stages, the 3D content is learned through\nreference view supervision and novel views guided by a combination of 2D and 3D\ndiffusion priors. We introduce a single trade-off parameter between the 2D and\n3D priors to control exploration (more imaginative) and exploitation (more\nprecise) of the generated geometry. Additionally, we employ textual inversion\nand monocular depth regularization to encourage consistent appearances across\nviews and to prevent degenerate solutions, respectively. Magic123 demonstrates\na significant improvement over previous image-to-3D techniques, as validated\nthrough extensive experiments on synthetic benchmarks and diverse real-world\nimages. Our code, models, and generated 3D assets are available at\nhttps://github.com/guochengqian/Magic123.\n", "date": "2023-06-30", "categories": []}
{"id": "2307.12049", "title": "Patch-Wise Point Cloud Generation: A Divide-and-Conquer Approach", "abstract": "  A generative model for high-fidelity point clouds is of great importance in\nsynthesizing 3d environments for applications such as autonomous driving and\nrobotics. Despite the recent success of deep generative models for 2d images,\nit is non-trivial to generate 3d point clouds without a comprehensive\nunderstanding of both local and global geometric structures. In this paper, we\ndevise a new 3d point cloud generation framework using a divide-and-conquer\napproach, where the whole generation process can be divided into a set of\npatch-wise generation tasks. Specifically, all patch generators are based on\nlearnable priors, which aim to capture the information of geometry primitives.\nWe introduce point- and patch-wise transformers to enable the interactions\nbetween points and patches. Therefore, the proposed divide-and-conquer approach\ncontributes to a new understanding of point cloud generation from the geometry\nconstitution of 3d shapes. Experimental results on a variety of object\ncategories from the most popular point cloud dataset, ShapeNet, show the\neffectiveness of the proposed patch-wise point cloud generation, where it\nclearly outperforms recent state-of-the-art methods for high-fidelity point\ncloud generation.\n", "date": "2023-07-22", "categories": []}
{"id": "2307.05445", "title": "AutoDecoding Latent 3D Diffusion Models", "abstract": "  We present a novel approach to the generation of static and articulated 3D\nassets that has a 3D autodecoder at its core. The 3D autodecoder framework\nembeds properties learned from the target dataset in the latent space, which\ncan then be decoded into a volumetric representation for rendering\nview-consistent appearance and geometry. We then identify the appropriate\nintermediate volumetric latent space, and introduce robust normalization and\nde-normalization operations to learn a 3D diffusion from 2D images or monocular\nvideos of rigid or articulated objects. Our approach is flexible enough to use\neither existing camera supervision or no camera information at all -- instead\nefficiently learning it during training. Our evaluations demonstrate that our\ngeneration results outperform state-of-the-art alternatives on various\nbenchmark datasets and metrics, including multi-view image datasets of\nsynthetic objects, real in-the-wild videos of moving people, and a large-scale,\nreal video dataset of static objects.\n", "date": "2023-07-07", "categories": []}
{"id": "2307.05462", "title": "Efficient 3D Articulated Human Generation with Layered Surface Volumes", "abstract": "  Access to high-quality and diverse 3D articulated digital human assets is\ncrucial in various applications, ranging from virtual reality to social\nplatforms. Generative approaches, such as 3D generative adversarial networks\n(GANs), are rapidly replacing laborious manual content creation tools. However,\nexisting 3D GAN frameworks typically rely on scene representations that\nleverage either template meshes, which are fast but offer limited quality, or\nvolumes, which offer high capacity but are slow to render, thereby limiting the\n3D fidelity in GAN settings. In this work, we introduce layered surface volumes\n(LSVs) as a new 3D object representation for articulated digital humans. LSVs\nrepresent a human body using multiple textured mesh layers around a\nconventional template. These layers are rendered using alpha compositing with\nfast differentiable rasterization, and they can be interpreted as a volumetric\nrepresentation that allocates its capacity to a manifold of finite thickness\naround the template. Unlike conventional single-layer templates that struggle\nwith representing fine off-surface details like hair or accessories, our\nsurface volumes naturally capture such details. LSVs can be articulated, and\nthey exhibit exceptional efficiency in GAN settings, where a 2D generator\nlearns to synthesize the RGBA textures for the individual layers. Trained on\nunstructured, single-view 2D image datasets, our LSV-GAN generates high-quality\nand view-consistent 3D articulated digital humans without the need for\nview-inconsistent 2D upsampling networks.\n", "date": "2023-07-11", "categories": []}
{"id": "2307.03869", "title": "Sketch-A-Shape: Zero-Shot Sketch-to-3D Shape Generation", "abstract": "  Significant progress has recently been made in creative applications of large\npre-trained models for downstream tasks in 3D vision, such as text-to-shape\ngeneration. This motivates our investigation of how these pre-trained models\ncan be used effectively to generate 3D shapes from sketches, which has largely\nremained an open challenge due to the limited sketch-shape paired datasets and\nthe varying level of abstraction in the sketches. We discover that conditioning\na 3D generative model on the features (obtained from a frozen large pre-trained\nvision model) of synthetic renderings during training enables us to effectively\ngenerate 3D shapes from sketches at inference time. This suggests that the\nlarge pre-trained vision model features carry semantic signals that are\nresilient to domain shifts, i.e., allowing us to use only RGB renderings, but\ngeneralizing to sketches at inference time. We conduct a comprehensive set of\nexperiments investigating different design factors and demonstrate the\neffectiveness of our straightforward approach for generation of multiple 3D\nshapes per each input sketch regardless of their level of abstraction without\nrequiring any paired datasets during training.\n", "date": "2023-07-08", "categories": []}
{"id": "2307.01831", "title": "DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation", "abstract": "  Recent Diffusion Transformers (e.g., DiT) have demonstrated their powerful\neffectiveness in generating high-quality 2D images. However, it is still being\ndetermined whether the Transformer architecture performs equally well in 3D\nshape generation, as previous 3D diffusion methods mostly adopted the U-Net\narchitecture. To bridge this gap, we propose a novel Diffusion Transformer for\n3D shape generation, namely DiT-3D, which can directly operate the denoising\nprocess on voxelized point clouds using plain Transformers. Compared to\nexisting U-Net approaches, our DiT-3D is more scalable in model size and\nproduces much higher quality generations. Specifically, the DiT-3D adopts the\ndesign philosophy of DiT but modifies it by incorporating 3D positional and\npatch embeddings to adaptively aggregate input from voxelized point clouds. To\nreduce the computational cost of self-attention in 3D shape generation, we\nincorporate 3D window attention into Transformer blocks, as the increased 3D\ntoken length resulting from the additional dimension of voxels can lead to high\ncomputation. Finally, linear and devoxelization layers are used to predict the\ndenoised point clouds. In addition, our transformer architecture supports\nefficient fine-tuning from 2D to 3D, where the pre-trained DiT-2D checkpoint on\nImageNet can significantly improve DiT-3D on ShapeNet. Experimental results on\nthe ShapeNet dataset demonstrate that the proposed DiT-3D achieves\nstate-of-the-art performance in high-fidelity and diverse 3D point cloud\ngeneration. In particular, our DiT-3D decreases the 1-Nearest Neighbor Accuracy\nof the state-of-the-art method by 4.59 and increases the Coverage metric by\n3.51 when evaluated on Chamfer Distance.\n", "date": "2023-07-04", "categories": []}
{"id": "2307.01957", "title": "Hybrid Neural Diffeomorphic Flow for Shape Representation and Generation\n  via Triplane", "abstract": "  Deep Implicit Functions (DIFs) have gained popularity in 3D computer vision\ndue to their compactness and continuous representation capabilities. However,\naddressing dense correspondences and semantic relationships across DIF-encoded\nshapes remains a critical challenge, limiting their applications in texture\ntransfer and shape analysis. Moreover, recent endeavors in 3D shape generation\nusing DIFs often neglect correspondence and topology preservation. This paper\npresents HNDF (Hybrid Neural Diffeomorphic Flow), a method that implicitly\nlearns the underlying representation and decomposes intricate dense\ncorrespondences into explicitly axis-aligned triplane features. To avoid\nsuboptimal representations trapped in local minima, we propose hybrid\nsupervision that captures both local and global correspondences. Unlike\nconventional approaches that directly generate new 3D shapes, we further\nexplore the idea of shape generation with deformed template shape via\ndiffeomorphic flows, where the deformation is encoded by the generated triplane\nfeatures. Leveraging a pre-existing 2D diffusion model, we produce high-quality\nand diverse 3D diffeomorphic flows through generated triplanes features,\nensuring topological consistency with the template shape. Extensive experiments\non medical image organ segmentation datasets evaluate the effectiveness of HNDF\nin 3D shape representation and generation.\n", "date": "2023-07-04", "categories": []}
{"id": "2306.17115", "title": "Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text\n  Aligned Latent Representation", "abstract": "  We present a novel alignment-before-generation approach to tackle the\nchallenging task of generating general 3D shapes based on 2D images or texts.\nDirectly learning a conditional generative model from images or texts to 3D\nshapes is prone to producing inconsistent results with the conditions because\n3D shapes have an additional dimension whose distribution significantly differs\nfrom that of 2D images and texts. To bridge the domain gap among the three\nmodalities and facilitate multi-modal-conditioned 3D shape generation, we\nexplore representing 3D shapes in a shape-image-text-aligned space. Our\nframework comprises two models: a Shape-Image-Text-Aligned Variational\nAuto-Encoder (SITA-VAE) and a conditional Aligned Shape Latent Diffusion Model\n(ASLDM). The former model encodes the 3D shapes into the shape latent space\naligned to the image and text and reconstructs the fine-grained 3D neural\nfields corresponding to given shape embeddings via the transformer-based\ndecoder. The latter model learns a probabilistic mapping function from the\nimage or text space to the latent shape space. Our extensive experiments\ndemonstrate that our proposed approach can generate higher-quality and more\ndiverse 3D shapes that better semantically conform to the visual or textural\nconditional inputs, validating the effectiveness of the\nshape-image-text-aligned space for cross-modality 3D shape generation.\n", "date": "2023-06-29", "categories": []}
{"id": "2306.15762", "title": "Toward Mesh-Invariant 3D Generative Deep Learning with Geometric\n  Measures", "abstract": "  3D generative modeling is accelerating as the technology allowing the capture\nof geometric data is developing. However, the acquired data is often\ninconsistent, resulting in unregistered meshes or point clouds. Many generative\nlearning algorithms require correspondence between each point when comparing\nthe predicted shape and the target shape. We propose an architecture able to\ncope with different parameterizations, even during the training phase. In\nparticular, our loss function is built upon a kernel-based metric over a\nrepresentation of meshes using geometric measures such as currents and\nvarifolds. The latter allows to implement an efficient dissimilarity measure\nwith many desirable properties such as robustness to resampling of the mesh or\npoint cloud. We demonstrate the efficiency and resilience of our model with a\ngenerative learning task of human faces.\n", "date": "2023-06-27", "categories": []}
{"id": "2306.16329", "title": "DiffComplete: Diffusion-based Generative 3D Shape Completion", "abstract": "  We introduce a new diffusion-based approach for shape completion on 3D range\nscans. Compared with prior deterministic and probabilistic methods, we strike a\nbalance between realism, multi-modality, and high fidelity. We propose\nDiffComplete by casting shape completion as a generative task conditioned on\nthe incomplete shape. Our key designs are two-fold. First, we devise a\nhierarchical feature aggregation mechanism to inject conditional features in a\nspatially-consistent manner. So, we can capture both local details and broader\ncontexts of the conditional inputs to control the shape completion. Second, we\npropose an occupancy-aware fusion strategy in our model to enable the\ncompletion of multiple partial shapes and introduce higher flexibility on the\ninput conditions. DiffComplete sets a new SOTA performance (e.g., 40% decrease\non l_1 error) on two large-scale 3D shape completion benchmarks. Our completed\nshapes not only have a realistic outlook compared with the deterministic\nmethods but also exhibit high similarity to the ground truths compared with the\nprobabilistic alternatives. Further, DiffComplete has strong generalizability\non objects of entirely unseen classes for both synthetic and real data,\neliminating the need for model re-training in various applications.\n", "date": "2023-06-28", "categories": []}
{"id": "2306.08183", "title": "ZeroForge: Feedforward Text-to-Shape Without 3D Supervision", "abstract": "  Current state-of-the-art methods for text-to-shape generation either require\nsupervised training using a labeled dataset of pre-defined 3D shapes, or\nperform expensive inference-time optimization of implicit neural\nrepresentations. In this work, we present ZeroForge, an approach for zero-shot\ntext-to-shape generation that avoids both pitfalls. To achieve open-vocabulary\nshape generation, we require careful architectural adaptation of existing\nfeed-forward approaches, as well as a combination of data-free CLIP-loss and\ncontrastive losses to avoid mode collapse. Using these techniques, we are able\nto considerably expand the generative ability of existing feed-forward\ntext-to-shape models such as CLIP-Forge. We support our method via extensive\nqualitative and quantitative evaluations\n", "date": "2023-06-14", "categories": []}
{"id": "2306.09864", "title": "AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation", "abstract": "  We introduce AvatarBooth, a novel method for generating high-quality 3D\navatars using text prompts or specific images. Unlike previous approaches that\ncan only synthesize avatars based on simple text descriptions, our method\nenables the creation of personalized avatars from casually captured face or\nbody images, while still supporting text-based model generation and editing.\nOur key contribution is the precise avatar generation control by using dual\nfine-tuned diffusion models separately for the human face and body. This\nenables us to capture intricate details of facial appearance, clothing, and\naccessories, resulting in highly realistic avatar generations. Furthermore, we\nintroduce pose-consistent constraint to the optimization process to enhance the\nmulti-view consistency of synthesized head images from the diffusion model and\nthus eliminate interference from uncontrolled human poses. In addition, we\npresent a multi-resolution rendering strategy that facilitates coarse-to-fine\nsupervision of 3D avatar generation, thereby enhancing the performance of the\nproposed system. The resulting avatar model can be further edited using\nadditional text descriptions and driven by motion sequences. Experiments show\nthat AvatarBooth outperforms previous text-to-3D methods in terms of rendering\nand geometric quality from either text prompts or specific images. Please check\nour project website at https://zeng-yifei.github.io/avatarbooth_page/.\n", "date": "2023-06-16", "categories": []}
{"id": "2306.08226", "title": "CLIPXPlore: Coupled CLIP and Shape Spaces for 3D Shape Exploration", "abstract": "  This paper presents CLIPXPlore, a new framework that leverages a\nvision-language model to guide the exploration of the 3D shape space. Many\nrecent methods have been developed to encode 3D shapes into a learned latent\nshape space to enable generative design and modeling. Yet, existing methods\nlack effective exploration mechanisms, despite the rich information. To this\nend, we propose to leverage CLIP, a powerful pre-trained vision-language model,\nto aid the shape-space exploration. Our idea is threefold. First, we couple the\nCLIP and shape spaces by generating paired CLIP and shape codes through sketch\nimages and training a mapper network to connect the two spaces. Second, to\nexplore the space around a given shape, we formulate a co-optimization strategy\nto search for the CLIP code that better matches the geometry of the shape.\nThird, we design three exploration modes, binary-attribute-guided, text-guided,\nand sketch-guided, to locate suitable exploration trajectories in shape space\nand induce meaningful changes to the shape. We perform a series of experiments\nto quantitatively and visually compare CLIPXPlore with different baselines in\neach of the three exploration modes, showing that CLIPXPlore can produce many\nmeaningful exploration results that cannot be achieved by the existing\nsolutions.\n", "date": "2023-06-14", "categories": []}
{"id": "2306.09329", "title": "DreamHuman: Animatable 3D Avatars from Text", "abstract": "  We present DreamHuman, a method to generate realistic animatable 3D human\navatar models solely from textual descriptions. Recent text-to-3D methods have\nmade considerable strides in generation, but are still lacking in important\naspects. Control and often spatial resolution remain limited, existing methods\nproduce fixed rather than animated 3D human models, and anthropometric\nconsistency for complex structures like people remains a challenge. DreamHuman\nconnects large text-to-image synthesis models, neural radiance fields, and\nstatistical human body models in a novel modeling and optimization framework.\nThis makes it possible to generate dynamic 3D human avatars with high-quality\ntextures and learned, instance-specific, surface deformations. We demonstrate\nthat our method is capable to generate a wide variety of animatable, realistic\n3D human models from text. Our 3D models have diverse appearance, clothing,\nskin tones and body shapes, and significantly outperform both generic\ntext-to-3D approaches and previous text-based 3D avatar generators in visual\nfidelity. For more results and animations please check our website at\nhttps://dream-human.github.io.\n", "date": "2023-06-15", "categories": []}
{"id": "2306.07349", "title": "ATT3D: Amortized Text-to-3D Object Synthesis", "abstract": "  Text-to-3D modelling has seen exciting progress by combining generative\ntext-to-image models with image-to-3D methods like Neural Radiance Fields.\nDreamFusion recently achieved high-quality results but requires a lengthy,\nper-prompt optimization to create 3D objects. To address this, we amortize\noptimization over text prompts by training on many prompts simultaneously with\na unified model, instead of separately. With this, we share computation across\na prompt set, training in less time than per-prompt optimization. Our framework\n- Amortized text-to-3D (ATT3D) - enables knowledge-sharing between prompts to\ngeneralize to unseen setups and smooth interpolations between text for novel\nassets and simple animations.\n", "date": "2023-06-06", "categories": []}
{"id": "2306.02083", "title": "Efficient Text-Guided 3D-Aware Portrait Generation with Score\n  Distillation Sampling on Distribution", "abstract": "  Text-to-3D is an emerging task that allows users to create 3D content with\ninfinite possibilities. Existing works tackle the problem by optimizing a 3D\nrepresentation with guidance from pre-trained diffusion models. An apparent\ndrawback is that they need to optimize from scratch for each prompt, which is\ncomputationally expensive and often yields poor visual fidelity. In this paper,\nwe propose DreamPortrait, which aims to generate text-guided 3D-aware portraits\nin a single-forward pass for efficiency. To achieve this, we extend Score\nDistillation Sampling from datapoint to distribution formulation, which injects\nsemantic prior into a 3D distribution. However, the direct extension will lead\nto the mode collapse problem since the objective only pursues semantic\nalignment. Hence, we propose to optimize a distribution with hierarchical\ncondition adapters and GAN loss regularization. For better 3D modeling, we\nfurther design a 3D-aware gated cross-attention mechanism to explicitly let the\nmodel perceive the correspondence between the text and the 3D-aware space.\nThese elaborated designs enable our model to generate portraits with robust\nmulti-view semantic consistency, eliminating the need for optimization-based\nmethods. Extensive experiments demonstrate our model's highly competitive\nperformance and significant speed boost against existing methods.\n", "date": "2023-06-03", "categories": []}
{"id": "2305.19012", "title": "StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity\n  3D Avatar Generation", "abstract": "  The recent advancements in image-text diffusion models have stimulated\nresearch interest in large-scale 3D generative models. Nevertheless, the\nlimited availability of diverse 3D resources presents significant challenges to\nlearning. In this paper, we present a novel method for generating high-quality,\nstylized 3D avatars that utilizes pre-trained image-text diffusion models for\ndata generation and a Generative Adversarial Network (GAN)-based 3D generation\nnetwork for training. Our method leverages the comprehensive priors of\nappearance and geometry offered by image-text diffusion models to generate\nmulti-view images of avatars in various styles. During data generation, we\nemploy poses extracted from existing 3D models to guide the generation of\nmulti-view images. To address the misalignment between poses and images in\ndata, we investigate view-specific prompts and develop a coarse-to-fine\ndiscriminator for GAN training. We also delve into attribute-related prompts to\nincrease the diversity of the generated avatars. Additionally, we develop a\nlatent diffusion model within the style space of StyleGAN to enable the\ngeneration of avatars based on image inputs. Our approach demonstrates superior\nperformance over current state-of-the-art methods in terms of visual quality\nand diversity of the produced avatars.\n", "date": "2023-05-30", "categories": []}
{"id": "2305.15753", "title": "T2TD: Text-3D Generation Model based on Prior Knowledge Guidance", "abstract": "  In recent years, 3D models have been utilized in many applications, such as\nauto-driver, 3D reconstruction, VR, and AR. However, the scarcity of 3D model\ndata does not meet its practical demands. Thus, generating high-quality 3D\nmodels efficiently from textual descriptions is a promising but challenging way\nto solve this problem. In this paper, inspired by the ability of human beings\nto complement visual information details from ambiguous descriptions based on\ntheir own experience, we propose a novel text-3D generation model (T2TD), which\nintroduces the related shapes or textual information as the prior knowledge to\nimprove the performance of the 3D generation model. In this process, we first\nintroduce the text-3D knowledge graph to save the relationship between 3D\nmodels and textual semantic information, which can provide the related shapes\nto guide the target 3D model generation. Second, we integrate an effective\ncausal inference model to select useful feature information from these related\nshapes, which removes the unrelated shape information and only maintains\nfeature information that is strongly relevant to the textual description.\nMeanwhile, to effectively integrate multi-modal prior knowledge into textual\ninformation, we adopt a novel multi-layer transformer structure to\nprogressively fuse related shape and textual information, which can effectively\ncompensate for the lack of structural information in the text and enhance the\nfinal performance of the 3D generation model. The final experimental results\ndemonstrate that our approach significantly improves 3D model generation\nquality and outperforms the SOTA methods on the text2shape datasets.\n", "date": "2023-05-25", "categories": []}
{"id": "2305.16315", "title": "NAP: Neural 3D Articulation Prior", "abstract": "  We propose Neural 3D Articulation Prior (NAP), the first 3D deep generative\nmodel to synthesize 3D articulated object models. Despite the extensive\nresearch on generating 3D objects, compositions, or scenes, there remains a\nlack of focus on capturing the distribution of articulated objects, a common\nobject category for human and robot interaction. To generate articulated\nobjects, we first design a novel articulation tree/graph parameterization and\nthen apply a diffusion-denoising probabilistic model over this representation\nwhere articulated objects can be generated via denoising from random complete\ngraphs. In order to capture both the geometry and the motion structure whose\ndistribution will affect each other, we design a graph-attention denoising\nnetwork for learning the reverse diffusion process. We propose a novel distance\nthat adapts widely used 3D generation metrics to our novel task to evaluate\ngeneration quality, and experiments demonstrate our high performance in\narticulated object generation. We also demonstrate several conditioned\ngeneration applications, including Part2Motion, PartNet-Imagination,\nMotion2Part, and GAPart2Object.\n", "date": "2023-05-25", "categories": []}
{"id": "2211.01427", "title": "CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes\n  from Natural Language", "abstract": "  Recent works have demonstrated that natural language can be used to generate\nand edit 3D shapes. However, these methods generate shapes with limited\nfidelity and diversity. We introduce CLIP-Sculptor, a method to address these\nconstraints by producing high-fidelity and diverse 3D shapes without the need\nfor (text, shape) pairs during training. CLIP-Sculptor achieves this in a\nmulti-resolution approach that first generates in a low-dimensional latent\nspace and then upscales to a higher resolution for improved shape fidelity. For\nimproved shape diversity, we use a discrete latent space which is modeled using\na transformer conditioned on CLIP's image-text embedding space. We also present\na novel variant of classifier-free guidance, which improves the\naccuracy-diversity trade-off. Finally, we perform extensive experiments\ndemonstrating that CLIP-Sculptor outperforms state-of-the-art baselines. The\ncode is available at https://ivl.cs.brown.edu/#/projects/clip-sculptor.\n", "date": "2022-11-02", "categories": []}
{"id": "2303.16509", "title": "HoloDiffusion: Training a 3D Diffusion Model using 2D Images", "abstract": "  Diffusion models have emerged as the best approach for generative modeling of\n2D images. Part of their success is due to the possibility of training them on\nmillions if not billions of images with a stable learning objective. However,\nextending these models to 3D remains difficult for two reasons. First, finding\na large quantity of 3D training data is much more complex than for 2D images.\nSecond, while it is conceptually trivial to extend the models to operate on 3D\nrather than 2D grids, the associated cubic growth in memory and compute\ncomplexity makes this infeasible. We address the first challenge by introducing\na new diffusion setup that can be trained, end-to-end, with only posed 2D\nimages for supervision; and the second challenge by proposing an image\nformation model that decouples model memory from spatial memory. We evaluate\nour method on real-world data, using the CO3D dataset which has not been used\nto train 3D generative models before. We show that our diffusion models are\nscalable, train robustly, and are competitive in terms of sample quality and\nfidelity to existing approaches for 3D generative modeling.\n", "date": "2023-03-29", "categories": []}
{"id": "2305.11664", "title": "Few-shot 3D Shape Generation", "abstract": "  Realistic and diverse 3D shape generation is helpful for a wide variety of\napplications such as virtual reality, gaming, and animation. Modern generative\nmodels, such as GANs and diffusion models, learn from large-scale datasets and\ngenerate new samples following similar data distributions. However, when\ntraining data is limited, deep neural generative networks overfit and tend to\nreplicate training samples. Prior works focus on few-shot image generation to\nproduce high-quality and diverse results using a few target images.\nUnfortunately, abundant 3D shape data is typically hard to obtain as well. In\nthis work, we make the first attempt to realize few-shot 3D shape generation by\nadapting generative models pre-trained on large source domains to target\ndomains using limited data. To relieve overfitting and keep considerable\ndiversity, we propose to maintain the probability distributions of the pairwise\nrelative distances between adapted samples at feature-level and shape-level\nduring domain adaptation. Our approach only needs the silhouettes of few-shot\ntarget samples as training data to learn target geometry distributions and\nachieve generated shapes with diverse topology and textures. Moreover, we\nintroduce several metrics to evaluate the quality and diversity of few-shot 3D\nshape generation. The effectiveness of our approach is demonstrated\nqualitatively and quantitatively under a series of few-shot 3D shape adaptation\nsetups.\n", "date": "2023-05-19", "categories": []}
{"id": "2305.04461", "title": "Locally Attentional SDF Diffusion for Controllable 3D Shape Generation", "abstract": "  Although the recent rapid evolution of 3D generative neural networks greatly\nimproves 3D shape generation, it is still not convenient for ordinary users to\ncreate 3D shapes and control the local geometry of generated shapes. To address\nthese challenges, we propose a diffusion-based 3D generation framework --\nlocally attentional SDF diffusion, to model plausible 3D shapes, via 2D sketch\nimage input. Our method is built on a two-stage diffusion model. The first\nstage, named occupancy-diffusion, aims to generate a low-resolution occupancy\nfield to approximate the shape shell. The second stage, named SDF-diffusion,\nsynthesizes a high-resolution signed distance field within the occupied voxels\ndetermined by the first stage to extract fine geometry. Our model is empowered\nby a novel view-aware local attention mechanism for image-conditioned shape\ngeneration, which takes advantage of 2D image patch features to guide 3D voxel\nfeature learning, greatly improving local controllability and model\ngeneralizability. Through extensive experiments in sketch-conditioned and\ncategory-conditioned 3D shape generation tasks, we validate and demonstrate the\nability of our method to provide plausible and diverse 3D shapes, as well as\nits superior controllability and generalizability over existing work. Our code\nand trained models are available at\nhttps://zhengxinyang.github.io/projects/LAS-Diffusion.html\n", "date": "2023-05-08", "categories": []}
{"id": "2212.03293", "title": "Diffusion-SDF: Text-to-Shape via Voxelized Diffusion", "abstract": "  With the rising industrial attention to 3D virtual modeling technology,\ngenerating novel 3D content based on specified conditions (e.g. text) has\nbecome a hot issue. In this paper, we propose a new generative 3D modeling\nframework called Diffusion-SDF for the challenging task of text-to-shape\nsynthesis. Previous approaches lack flexibility in both 3D data representation\nand shape generation, thereby failing to generate highly diversified 3D shapes\nconforming to the given text descriptions. To address this, we propose a SDF\nautoencoder together with the Voxelized Diffusion model to learn and generate\nrepresentations for voxelized signed distance fields (SDFs) of 3D shapes.\nSpecifically, we design a novel UinU-Net architecture that implants a\nlocal-focused inner network inside the standard U-Net architecture, which\nenables better reconstruction of patch-independent SDF representations. We\nextend our approach to further text-to-shape tasks including text-conditioned\nshape completion and manipulation. Experimental results show that Diffusion-SDF\ngenerates both higher quality and more diversified 3D shapes that conform well\nto given text descriptions when compared to previous approaches. Code is\navailable at: https://github.com/ttlmh/Diffusion-SDF\n", "date": "2022-12-06", "categories": []}
{"id": "2305.03302", "title": "High-Fidelity 3D Face Generation from Natural Language Descriptions", "abstract": "  Synthesizing high-quality 3D face models from natural language descriptions\nis very valuable for many applications, including avatar creation, virtual\nreality, and telepresence. However, little research ever tapped into this task.\nWe argue the major obstacle lies in 1) the lack of high-quality 3D face data\nwith descriptive text annotation, and 2) the complex mapping relationship\nbetween descriptive language space and shape/appearance space. To solve these\nproblems, we build Describe3D dataset, the first large-scale dataset with\nfine-grained text descriptions for text-to-3D face generation task. Then we\npropose a two-stage framework to first generate a 3D face that matches the\nconcrete descriptions, then optimize the parameters in the 3D shape and texture\nspace with abstract description to refine the 3D face model. Extensive\nexperimental results show that our method can produce a faithful 3D face that\nconforms to the input descriptions with higher accuracy and quality than\nprevious methods. The code and Describe3D dataset are released at\nhttps://github.com/zhuhao-nju/describe3d .\n", "date": "2023-05-05", "categories": []}
{"id": "2305.02463", "title": "Shap-E: Generating Conditional 3D Implicit Functions", "abstract": "  We present Shap-E, a conditional generative model for 3D assets. Unlike\nrecent work on 3D generative models which produce a single output\nrepresentation, Shap-E directly generates the parameters of implicit functions\nthat can be rendered as both textured meshes and neural radiance fields. We\ntrain Shap-E in two stages: first, we train an encoder that deterministically\nmaps 3D assets into the parameters of an implicit function; second, we train a\nconditional diffusion model on outputs of the encoder. When trained on a large\ndataset of paired 3D and text data, our resulting models are capable of\ngenerating complex and diverse 3D assets in a matter of seconds. When compared\nto Point-E, an explicit generative model over point clouds, Shap-E converges\nfaster and reaches comparable or better sample quality despite modeling a\nhigher-dimensional, multi-representation output space. We release model\nweights, inference code, and samples at https://github.com/openai/shap-e.\n", "date": "2023-05-03", "categories": []}
{"id": "2305.02312", "title": "AG3D: Learning to Generate 3D Avatars from 2D Image Collections", "abstract": "  While progress in 2D generative models of human appearance has been rapid,\nmany applications require 3D avatars that can be animated and rendered.\nUnfortunately, most existing methods for learning generative models of 3D\nhumans with diverse shape and appearance require 3D training data, which is\nlimited and expensive to acquire. The key to progress is hence to learn\ngenerative models of 3D avatars from abundant unstructured 2D image\ncollections. However, learning realistic and complete 3D appearance and\ngeometry in this under-constrained setting remains challenging, especially in\nthe presence of loose clothing such as dresses. In this paper, we propose a new\nadversarial generative model of realistic 3D people from 2D images. Our method\ncaptures shape and deformation of the body and loose clothing by adopting a\nholistic 3D generator and integrating an efficient and flexible articulation\nmodule. To improve realism, we train our model using multiple discriminators\nwhile also integrating geometric cues in the form of predicted 2D normal maps.\nWe experimentally find that our method outperforms previous 3D- and\narticulation-aware methods in terms of geometry and appearance. We validate the\neffectiveness of our model and the importance of each component via systematic\nablation studies.\n", "date": "2023-05-03", "categories": []}
{"id": "2301.11445", "title": "3DShape2VecSet: A 3D Shape Representation for Neural Fields and\n  Generative Diffusion Models", "abstract": "  We introduce 3DShape2VecSet, a novel shape representation for neural fields\ndesigned for generative diffusion models. Our shape representation can encode\n3D shapes given as surface models or point clouds, and represents them as\nneural fields. The concept of neural fields has previously been combined with a\nglobal latent vector, a regular grid of latent vectors, or an irregular grid of\nlatent vectors. Our new representation encodes neural fields on top of a set of\nvectors. We draw from multiple concepts, such as the radial basis function\nrepresentation and the cross attention and self-attention function, to design a\nlearnable representation that is especially suitable for processing with\ntransformers. Our results show improved performance in 3D shape encoding and 3D\nshape generative modeling tasks. We demonstrate a wide variety of generative\napplications: unconditioned generation, category-conditioned generation,\ntext-conditioned generation, point-cloud completion, and image-conditioned\ngeneration.\n", "date": "2023-01-26", "categories": []}
{"id": "2304.13153", "title": "LumiGAN: Unconditional Generation of Relightable 3D Human Faces", "abstract": "  Unsupervised learning of 3D human faces from unstructured 2D image data is an\nactive research area. While recent works have achieved an impressive level of\nphotorealism, they commonly lack control of lighting, which prevents the\ngenerated assets from being deployed in novel environments. To this end, we\nintroduce LumiGAN, an unconditional Generative Adversarial Network (GAN) for 3D\nhuman faces with a physically based lighting module that enables relighting\nunder novel illumination at inference time. Unlike prior work, LumiGAN can\ncreate realistic shadow effects using an efficient visibility formulation that\nis learned in a self-supervised manner. LumiGAN generates plausible physical\nproperties for relightable faces, including surface normals, diffuse albedo,\nand specular tint without any ground truth data. In addition to relightability,\nwe demonstrate significantly improved geometry generation compared to\nstate-of-the-art non-relightable 3D GANs and notably better photorealism than\nexisting relightable GANs.\n", "date": "2023-04-25", "categories": []}
{"id": "2304.13348", "title": "TextDeformer: Geometry Manipulation using Text Guidance", "abstract": "  We present a technique for automatically producing a deformation of an input\ntriangle mesh, guided solely by a text prompt. Our framework is capable of\ndeformations that produce both large, low-frequency shape changes, and small\nhigh-frequency details. Our framework relies on differentiable rendering to\nconnect geometry to powerful pre-trained image encoders, such as CLIP and DINO.\nNotably, updating mesh geometry by taking gradient steps through differentiable\nrendering is notoriously challenging, commonly resulting in deformed meshes\nwith significant artifacts. These difficulties are amplified by noisy and\ninconsistent gradients from CLIP. To overcome this limitation, we opt to\nrepresent our mesh deformation through Jacobians, which updates deformations in\na global, smooth manner (rather than locally-sub-optimal steps). Our key\nobservation is that Jacobians are a representation that favors smoother, large\ndeformations, leading to a global relation between vertices and pixels, and\navoiding localized noisy gradients. Additionally, to ensure the resulting shape\nis coherent from all 3D viewpoints, we encourage the deep features computed on\nthe 2D encoding of the rendering to be consistent for a given vertex from all\nviewpoints. We demonstrate that our method is capable of smoothly-deforming a\nwide variety of source mesh and target text prompts, achieving both large\nmodifications to, e.g., body proportions of animals, as well as adding fine\nsemantic details, such as shoe laces on an army boot and fine details of a\nface.\n", "date": "2023-04-26", "categories": []}
{"id": "2304.12439", "title": "TextMesh: Generation of Realistic 3D Meshes From Text Prompts", "abstract": "  The ability to generate highly realistic 2D images from mere text prompts has\nrecently made huge progress in terms of speed and quality, thanks to the advent\nof image diffusion models. Naturally, the question arises if this can be also\nachieved in the generation of 3D content from such text prompts. To this end, a\nnew line of methods recently emerged trying to harness diffusion models,\ntrained on 2D images, for supervision of 3D model generation using view\ndependent prompts. While achieving impressive results, these methods, however,\nhave two major drawbacks. First, rather than commonly used 3D meshes, they\ninstead generate neural radiance fields (NeRFs), making them impractical for\nmost real applications. Second, these approaches tend to produce over-saturated\nmodels, giving the output a cartoonish looking effect. Therefore, in this work\nwe propose a novel method for generation of highly realistic-looking 3D meshes.\nTo this end, we extend NeRF to employ an SDF backbone, leading to improved 3D\nmesh extraction. In addition, we propose a novel way to finetune the mesh\ntexture, removing the effect of high saturation and improving the details of\nthe output 3D mesh.\n", "date": "2023-04-24", "categories": []}
{"id": "2212.01406", "title": "ClipFace: Text-guided Editing of Textured 3D Morphable Models", "abstract": "  We propose ClipFace, a novel self-supervised approach for text-guided editing\nof textured 3D morphable model of faces. Specifically, we employ user-friendly\nlanguage prompts to enable control of the expressions as well as appearance of\n3D faces. We leverage the geometric expressiveness of 3D morphable models,\nwhich inherently possess limited controllability and texture expressivity, and\ndevelop a self-supervised generative model to jointly synthesize expressive,\ntextured, and articulated faces in 3D. We enable high-quality texture\ngeneration for 3D faces by adversarial self-supervised training, guided by\ndifferentiable rendering against collections of real RGB images. Controllable\nediting and manipulation are given by language prompts to adapt texture and\nexpression of the 3D morphable model. To this end, we propose a neural network\nthat predicts both texture and expression latent codes of the morphable model.\nOur model is trained in a self-supervised fashion by exploiting differentiable\nrendering and losses based on a pre-trained CLIP model. Once trained, our model\njointly predicts face textures in UV-space, along with expression parameters to\ncapture both geometry and texture changes in facial expressions in a single\nforward pass. We further show the applicability of our method to generate\ntemporally changing textures for a given animation sequence.\n", "date": "2022-12-02", "categories": []}
{"id": "2304.10986", "title": "Attention-based Part Assembly for 3D Volumetric Shape Modeling", "abstract": "  Modeling a 3D volumetric shape as an assembly of decomposed shape parts is\nmuch more challenging, but semantically more valuable than direct\nreconstruction from a full shape representation. The neural network needs to\nimplicitly learn part relations coherently, which is typically performed by\ndedicated network layers that can generate transformation matrices for each\npart. In this paper, we propose a VoxAttention network architecture for\nattention-based part assembly. We further propose a variant of using\nchannel-wise part attention and show the advantages of this approach.\nExperimental results show that our method outperforms most state-of-the-art\nmethods for the part relation-aware 3D shape modeling task.\n", "date": "2023-04-17", "categories": []}
{"id": "2304.09787", "title": "NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion\n  Models", "abstract": "  Automatically generating high-quality real world 3D scenes is of enormous\ninterest for applications such as virtual reality and robotics simulation.\nTowards this goal, we introduce NeuralField-LDM, a generative model capable of\nsynthesizing complex 3D environments. We leverage Latent Diffusion Models that\nhave been successfully utilized for efficient high-quality 2D content creation.\nWe first train a scene auto-encoder to express a set of image and pose pairs as\na neural field, represented as density and feature voxel grids that can be\nprojected to produce novel views of the scene. To further compress this\nrepresentation, we train a latent-autoencoder that maps the voxel grids to a\nset of latent representations. A hierarchical diffusion model is then fit to\nthe latents to complete the scene generation pipeline. We achieve a substantial\nimprovement over existing state-of-the-art scene generation models.\nAdditionally, we show how NeuralField-LDM can be used for a variety of 3D\ncontent creation applications, including conditional scene generation, scene\ninpainting and scene style manipulation.\n", "date": "2023-04-19", "categories": []}
{"id": "2303.08133", "title": "MeshDiffusion: Score-based Generative 3D Mesh Modeling", "abstract": "  We consider the task of generating realistic 3D shapes, which is useful for a\nvariety of applications such as automatic scene generation and physical\nsimulation. Compared to other 3D representations like voxels and point clouds,\nmeshes are more desirable in practice, because (1) they enable easy and\narbitrary manipulation of shapes for relighting and simulation, and (2) they\ncan fully leverage the power of modern graphics pipelines which are mostly\noptimized for meshes. Previous scalable methods for generating meshes typically\nrely on sub-optimal post-processing, and they tend to produce overly-smooth or\nnoisy surfaces without fine-grained geometric details. To overcome these\nshortcomings, we take advantage of the graph structure of meshes and use a\nsimple yet very effective generative modeling method to generate 3D meshes.\nSpecifically, we represent meshes with deformable tetrahedral grids, and then\ntrain a diffusion model on this direct parametrization. We demonstrate the\neffectiveness of our model on multiple generative tasks.\n", "date": "2023-03-14", "categories": []}
{"id": "2207.09446", "title": "ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model", "abstract": "  We present ShapeCrafter, a neural network for recursive text-conditioned 3D\nshape generation. Existing methods to generate text-conditioned 3D shapes\nconsume an entire text prompt to generate a 3D shape in a single step. However,\nhumans tend to describe shapes recursively-we may start with an initial\ndescription and progressively add details based on intermediate results. To\ncapture this recursive process, we introduce a method to generate a 3D shape\ndistribution, conditioned on an initial phrase, that gradually evolves as more\nphrases are added. Since existing datasets are insufficient for training this\napproach, we present Text2Shape++, a large dataset of 369K shape-text pairs\nthat supports recursive shape generation. To capture local details that are\noften used to refine shape descriptions, we build on top of vector-quantized\ndeep implicit functions that generate a distribution of high-quality shapes.\nResults show that our method can generate shapes consistent with text\ndescriptions, and shapes evolve gradually as more phrases are added. Our method\nsupports shape editing, extrapolation, and can enable new applications in\nhuman-machine collaboration for creative design.\n", "date": "2022-07-19", "categories": []}
{"id": "2212.12952", "title": "Neural Shape Compiler: A Unified Framework for Transforming between\n  Text, Point Cloud, and Program", "abstract": "  3D shapes have complementary abstractions from low-level geometry to\npart-based hierarchies to languages, which convey different levels of\ninformation. This paper presents a unified framework to translate between pairs\nof shape abstractions: $\\textit{Text}$ $\\Longleftrightarrow$ $\\textit{Point\nCloud}$ $\\Longleftrightarrow$ $\\textit{Program}$. We propose $\\textbf{Neural\nShape Compiler}$ to model the abstraction transformation as a conditional\ngeneration process. It converts 3D shapes of three abstract types into unified\ndiscrete shape code, transforms each shape code into code of other abstract\ntypes through the proposed $\\textit{ShapeCode Transformer}$, and decodes them\nto output the target shape abstraction. Point Cloud code is obtained in a\nclass-agnostic way by the proposed $\\textit{Point}$VQVAE. On Text2Shape,\nShapeGlot, ABO, Genre, and Program Synthetic datasets, Neural Shape Compiler\nshows strengths in $\\textit{Text}$ $\\Longrightarrow$ $\\textit{Point Cloud}$,\n$\\textit{Point Cloud}$ $\\Longrightarrow$ $\\textit{Text}$, $\\textit{Point\nCloud}$ $\\Longrightarrow$ $\\textit{Program}$, and Point Cloud Completion tasks.\nAdditionally, Neural Shape Compiler benefits from jointly training on all\nheterogeneous data and tasks.\n", "date": "2022-12-25", "categories": []}
{"id": "2304.02827", "title": "DITTO-NeRF: Diffusion-based Iterative Text To Omni-directional 3D Model", "abstract": "  The increasing demand for high-quality 3D content creation has motivated the\ndevelopment of automated methods for creating 3D object models from a single\nimage and/or from a text prompt. However, the reconstructed 3D objects using\nstate-of-the-art image-to-3D methods still exhibit low correspondence to the\ngiven image and low multi-view consistency. Recent state-of-the-art text-to-3D\nmethods are also limited, yielding 3D samples with low diversity per prompt\nwith long synthesis time. To address these challenges, we propose DITTO-NeRF, a\nnovel pipeline to generate a high-quality 3D NeRF model from a text prompt or a\nsingle image. Our DITTO-NeRF consists of constructing high-quality partial 3D\nobject for limited in-boundary (IB) angles using the given or text-generated 2D\nimage from the frontal view and then iteratively reconstructing the remaining\n3D NeRF using inpainting latent diffusion model. We propose progressive 3D\nobject reconstruction schemes in terms of scales (low to high resolution),\nangles (IB angles initially to outer-boundary (OB) later), and masks (object to\nbackground boundary) in our DITTO-NeRF so that high-quality information on IB\ncan be propagated into OB. Our DITTO-NeRF outperforms state-of-the-art methods\nin terms of fidelity and diversity qualitatively and quantitatively with much\nfaster training times than prior arts on image/text-to-3D such as DreamFusion,\nand NeuralLift-360.\n", "date": "2023-04-06", "categories": []}
{"id": "2302.12798", "title": "3D Generative Model Latent Disentanglement via Local Eigenprojection", "abstract": "  Designing realistic digital humans is extremely complex. Most data-driven\ngenerative models used to simplify the creation of their underlying geometric\nshape do not offer control over the generation of local shape attributes. In\nthis paper, we overcome this limitation by introducing a novel loss function\ngrounded in spectral geometry and applicable to different neural-network-based\ngenerative models of 3D head and body meshes. Encouraging the latent variables\nof mesh variational autoencoders (VAEs) or generative adversarial networks\n(GANs) to follow the local eigenprojections of identity attributes, we improve\nlatent disentanglement and properly decouple the attribute creation.\nExperimental results show that our local eigenprojection disentangled (LED)\nmodels not only offer improved disentanglement with respect to the\nstate-of-the-art, but also maintain good generation capabilities with training\ntimes comparable to the vanilla implementations of the models.\n", "date": "2023-02-24", "categories": []}
{"id": "2211.16431", "title": "NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with\n  360{\\deg} Views", "abstract": "  Virtual reality and augmented reality (XR) bring increasing demand for 3D\ncontent. However, creating high-quality 3D content requires tedious work that a\nhuman expert must do. In this work, we study the challenging task of lifting a\nsingle image to a 3D object and, for the first time, demonstrate the ability to\ngenerate a plausible 3D object with 360{\\deg} views that correspond well with\nthe given reference image. By conditioning on the reference image, our model\ncan fulfill the everlasting curiosity for synthesizing novel views of objects\nfrom images. Our technique sheds light on a promising direction of easing the\nworkflows for 3D artists and XR designers. We propose a novel framework, dubbed\nNeuralLift-360, that utilizes a depth-aware neural radiance representation\n(NeRF) and learns to craft the scene guided by denoising diffusion models. By\nintroducing a ranking loss, our NeuralLift-360 can be guided with rough depth\nestimation in the wild. We also adopt a CLIP-guided sampling strategy for the\ndiffusion prior to provide coherent guidance. Extensive experiments demonstrate\nthat our NeuralLift-360 significantly outperforms existing state-of-the-art\nbaselines. Project page: https://vita-group.github.io/NeuralLift-360/\n", "date": "2022-11-29", "categories": []}
{"id": "2212.14704", "title": "Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and\n  Text-to-Image Diffusion Models", "abstract": "  Recent CLIP-guided 3D optimization methods, such as DreamFields and\nPureCLIPNeRF, have achieved impressive results in zero-shot text-to-3D\nsynthesis. However, due to scratch training and random initialization without\nprior knowledge, these methods often fail to generate accurate and faithful 3D\nstructures that conform to the input text. In this paper, we make the first\nattempt to introduce explicit 3D shape priors into the CLIP-guided 3D\noptimization process. Specifically, we first generate a high-quality 3D shape\nfrom the input text in the text-to-shape stage as a 3D shape prior. We then use\nit as the initialization of a neural radiance field and optimize it with the\nfull prompt. To address the challenging text-to-shape generation task, we\npresent a simple yet effective approach that directly bridges the text and\nimage modalities with a powerful text-to-image diffusion model. To narrow the\nstyle domain gap between the images synthesized by the text-to-image diffusion\nmodel and shape renderings used to train the image-to-shape generator, we\nfurther propose to jointly optimize a learnable text prompt and fine-tune the\ntext-to-image diffusion model for rendering-style image generation. Our method,\nDream3D, is capable of generating imaginative 3D content with superior visual\nquality and shape accuracy compared to state-of-the-art methods.\n", "date": "2022-12-28", "categories": []}
{"id": "2303.14184", "title": "Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion\n  Prior", "abstract": "  In this work, we investigate the problem of creating high-fidelity 3D content\nfrom only a single image. This is inherently challenging: it essentially\ninvolves estimating the underlying 3D geometry while simultaneously\nhallucinating unseen textures. To address this challenge, we leverage prior\nknowledge from a well-trained 2D diffusion model to act as 3D-aware supervision\nfor 3D creation. Our approach, Make-It-3D, employs a two-stage optimization\npipeline: the first stage optimizes a neural radiance field by incorporating\nconstraints from the reference image at the frontal view and diffusion prior at\nnovel views; the second stage transforms the coarse model into textured point\nclouds and further elevates the realism with diffusion prior while leveraging\nthe high-quality textures from the reference image. Extensive experiments\ndemonstrate that our method outperforms prior works by a large margin,\nresulting in faithful reconstructions and impressive visual quality. Our method\npresents the first attempt to achieve high-quality 3D creation from a single\nimage for general objects and enables various applications such as text-to-3D\ncreation and texture editing.\n", "date": "2023-03-24", "categories": []}
{"id": "2303.05371", "title": "3DGen: Triplane Latent Diffusion for Textured Mesh Generation", "abstract": "  Latent diffusion models for image generation have crossed a quality threshold\nwhich enabled them to achieve mass adoption. Recently, a series of works have\nmade advancements towards replicating this success in the 3D domain,\nintroducing techniques such as point cloud VAE, triplane representation, neural\nimplicit surfaces and differentiable rendering based training. We take another\nstep along this direction, combining these developments in a two-step pipeline\nconsisting of 1) a triplane VAE which can learn latent representations of\ntextured meshes and 2) a conditional diffusion model which generates the\ntriplane features. For the first time this architecture allows conditional and\nunconditional generation of high quality textured or untextured 3D meshes\nacross multiple diverse categories in a few seconds on a single GPU. It\noutperforms previous work substantially on image-conditioned and unconditional\ngeneration on mesh quality as well as texture generation. Furthermore, we\ndemonstrate the scalability of our model to large datasets for increased\nquality and diversity. We will release our code and trained models.\n", "date": "2023-03-09", "categories": []}
{"id": "2303.15805", "title": "StarNet: Style-Aware 3D Point Cloud Generation", "abstract": "  This paper investigates an open research task of reconstructing and\ngenerating 3D point clouds. Most existing works of 3D generative models\ndirectly take the Gaussian prior as input for the decoder to generate 3D point\nclouds, which fail to learn disentangled latent codes, leading noisy\ninterpolated results. Most of the GAN-based models fail to discriminate the\nlocal geometries, resulting in the point clouds generated not evenly\ndistributed at the object surface, hence degrading the point cloud generation\nquality. Moreover, prevailing methods adopt computation-intensive frameworks,\nsuch as flow-based models and Markov chains, which take plenty of time and\nresources in the training phase. To resolve these limitations, this paper\nproposes a unified style-aware network architecture combining both point-wise\ndistance loss and adversarial loss, StarNet which is able to reconstruct and\ngenerate high-fidelity and even 3D point clouds using a mapping network that\ncan effectively disentangle the Gaussian prior from input's high-level\nattributes in the mapped latent space to generate realistic interpolated\nobjects. Experimental results demonstrate that our framework achieves\ncomparable state-of-the-art performance on various metrics in the point cloud\nreconstruction and generation tasks, but is more lightweight in model size,\nrequires much fewer parameters and less time for model training.\n", "date": "2023-03-28", "categories": []}
{"id": "2303.15892", "title": "Head3D: Complete 3D Head Generation via Tri-plane Feature Distillation", "abstract": "  Head generation with diverse identities is an important task in computer\nvision and computer graphics, widely used in multimedia applications. However,\ncurrent full head generation methods require a large number of 3D scans or\nmulti-view images to train the model, resulting in expensive data acquisition\ncost. To address this issue, we propose Head3D, a method to generate full 3D\nheads with limited multi-view images. Specifically, our approach first extracts\nfacial priors represented by tri-planes learned in EG3D, a 3D-aware generative\nmodel, and then proposes feature distillation to deliver the 3D frontal faces\ninto complete heads without compromising head integrity. To mitigate the domain\ngap between the face and head models, we present dual-discriminators to guide\nthe frontal and back head generation, respectively. Our model achieves\ncost-efficient and diverse complete head generation with photo-realistic\nrenderings and high-quality geometry representations. Extensive experiments\ndemonstrate the effectiveness of our proposed Head3D, both qualitatively and\nquantitatively.\n", "date": "2023-03-28", "categories": []}
{"id": "2211.10440", "title": "Magic3D: High-Resolution Text-to-3D Content Creation", "abstract": "  DreamFusion has recently demonstrated the utility of a pre-trained\ntext-to-image diffusion model to optimize Neural Radiance Fields (NeRF),\nachieving remarkable text-to-3D synthesis results. However, the method has two\ninherent limitations: (a) extremely slow optimization of NeRF and (b)\nlow-resolution image space supervision on NeRF, leading to low-quality 3D\nmodels with a long processing time. In this paper, we address these limitations\nby utilizing a two-stage optimization framework. First, we obtain a coarse\nmodel using a low-resolution diffusion prior and accelerate with a sparse 3D\nhash grid structure. Using the coarse representation as the initialization, we\nfurther optimize a textured 3D mesh model with an efficient differentiable\nrenderer interacting with a high-resolution latent diffusion model. Our method,\ndubbed Magic3D, can create high quality 3D mesh models in 40 minutes, which is\n2x faster than DreamFusion (reportedly taking 1.5 hours on average), while also\nachieving higher resolution. User studies show 61.7% raters to prefer our\napproach over DreamFusion. Together with the image-conditioned generation\ncapabilities, we provide users with new ways to control 3D synthesis, opening\nup new avenues to various creative applications.\n", "date": "2022-11-18", "categories": []}
{"id": "2301.02700", "title": "3DAvatarGAN: Bridging Domains for Personalized Editable Avatars", "abstract": "  Modern 3D-GANs synthesize geometry and texture by training on large-scale\ndatasets with a consistent structure. Training such models on stylized,\nartistic data, with often unknown, highly variable geometry, and camera\ninformation has not yet been shown possible. Can we train a 3D GAN on such\nartistic data, while maintaining multi-view consistency and texture quality? To\nthis end, we propose an adaptation framework, where the source domain is a\npre-trained 3D-GAN, while the target domain is a 2D-GAN trained on artistic\ndatasets. We then distill the knowledge from a 2D generator to the source 3D\ngenerator. To do that, we first propose an optimization-based method to align\nthe distributions of camera parameters across domains. Second, we propose\nregularizations necessary to learn high-quality texture, while avoiding\ndegenerate geometric solutions, such as flat shapes. Third, we show a\ndeformation-based technique for modeling exaggerated geometry of artistic\ndomains, enabling -- as a byproduct -- personalized geometric editing. Finally,\nwe propose a novel inversion method for 3D-GANs linking the latent spaces of\nthe source and the target domains. Our contributions -- for the first time --\nallow for the generation, editing, and animation of personalized artistic 3D\navatars on artistic datasets.\n", "date": "2023-01-06", "categories": []}
{"id": "2303.13508", "title": "DreamBooth3D: Subject-Driven Text-to-3D Generation", "abstract": "  We present DreamBooth3D, an approach to personalize text-to-3D generative\nmodels from as few as 3-6 casually captured images of a subject. Our approach\ncombines recent advances in personalizing text-to-image models (DreamBooth)\nwith text-to-3D generation (DreamFusion). We find that naively combining these\nmethods fails to yield satisfactory subject-specific 3D assets due to\npersonalized text-to-image models overfitting to the input viewpoints of the\nsubject. We overcome this through a 3-stage optimization strategy where we\njointly leverage the 3D consistency of neural radiance fields together with the\npersonalization capability of text-to-image models. Our method can produce\nhigh-quality, subject-specific 3D assets with text-driven modifications such as\nnovel poses, colors and attributes that are not seen in any of the input images\nof the subject.\n", "date": "2023-03-23", "categories": []}
{"id": "2303.14700", "title": "Learning Versatile 3D Shape Generation with Improved AR Models", "abstract": "  Auto-Regressive (AR) models have achieved impressive results in 2D image\ngeneration by modeling joint distributions in the grid space. While this\napproach has been extended to the 3D domain for powerful shape generation, it\nstill has two limitations: expensive computations on volumetric grids and\nambiguous auto-regressive order along grid dimensions. To overcome these\nlimitations, we propose the Improved Auto-regressive Model (ImAM) for 3D shape\ngeneration, which applies discrete representation learning based on a latent\nvector instead of volumetric grids. Our approach not only reduces computational\ncosts but also preserves essential geometric details by learning the joint\ndistribution in a more tractable order. Moreover, thanks to the simplicity of\nour model architecture, we can naturally extend it from unconditional to\nconditional generation by concatenating various conditioning inputs, such as\npoint clouds, categories, images, and texts. Extensive experiments demonstrate\nthat ImAM can synthesize diverse and faithful shapes of multiple categories,\nachieving state-of-the-art performance.\n", "date": "2023-03-26", "categories": []}
{"id": "2303.13273", "title": "TAPS3D: Text-Guided 3D Textured Shape Generation from Pseudo Supervision", "abstract": "  In this paper, we investigate an open research task of generating\ncontrollable 3D textured shapes from the given textual descriptions. Previous\nworks either require ground truth caption labeling or extensive optimization\ntime. To resolve these issues, we present a novel framework, TAPS3D, to train a\ntext-guided 3D shape generator with pseudo captions. Specifically, based on\nrendered 2D images, we retrieve relevant words from the CLIP vocabulary and\nconstruct pseudo captions using templates. Our constructed captions provide\nhigh-level semantic supervision for generated 3D shapes. Further, in order to\nproduce fine-grained textures and increase geometry diversity, we propose to\nadopt low-level image regularization to enable fake-rendered images to align\nwith the real ones. During the inference phase, our proposed model can generate\n3D textured shapes from the given text without any additional optimization. We\nconduct extensive experiments to analyze each of our proposed components and\nshow the efficacy of our framework in generating high-fidelity 3D textured and\ntext-relevant shapes.\n", "date": "2023-03-23", "categories": []}
{"id": "2212.04493", "title": "SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation", "abstract": "  In this work, we present a novel framework built to simplify 3D asset\ngeneration for amateur users. To enable interactive generation, our method\nsupports a variety of input modalities that can be easily provided by a human,\nincluding images, text, partially observed shapes and combinations of these,\nfurther allowing to adjust the strength of each input. At the core of our\napproach is an encoder-decoder, compressing 3D shapes into a compact latent\nrepresentation, upon which a diffusion model is learned. To enable a variety of\nmulti-modal inputs, we employ task-specific encoders with dropout followed by a\ncross-attention mechanism. Due to its flexibility, our model naturally supports\na variety of tasks, outperforming prior works on shape completion, image-based\n3D reconstruction, and text-to-3D. Most interestingly, our model can combine\nall these tasks into one swiss-army-knife tool, enabling the user to perform\nshape generation using incomplete shapes, images, and textual descriptions at\nthe same time, providing the relative weights for each input and facilitating\ninteractivity. Despite our approach being shape-only, we further show an\nefficient method to texture the generated shape using large-scale text-to-image\nmodels.\n", "date": "2022-12-08", "categories": []}
{"id": "2303.09554", "title": "PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D\n  Supervision", "abstract": "  Impressive progress in generative models and implicit representations gave\nrise to methods that can generate 3D shapes of high quality. However, being\nable to locally control and edit shapes is another essential property that can\nunlock several content creation applications. Local control can be achieved\nwith part-aware models, but existing methods require 3D supervision and cannot\nproduce textures. In this work, we devise PartNeRF, a novel part-aware\ngenerative model for editable 3D shape synthesis that does not require any\nexplicit 3D supervision. Our model generates objects as a set of locally\ndefined NeRFs, augmented with an affine transformation. This enables several\nediting operations such as applying transformations on parts, mixing parts from\ndifferent objects etc. To ensure distinct, manipulable parts we enforce a hard\nassignment of rays to parts that makes sure that the color of each ray is only\ndetermined by a single NeRF. As a result, altering one part does not affect the\nappearance of the others. Evaluations on various ShapeNet categories\ndemonstrate the ability of our model to generate editable 3D objects of\nimproved fidelity, compared to previous part-based generative approaches that\nrequire 3D supervision or models relying on NeRFs.\n", "date": "2023-03-16", "categories": []}
{"id": "2303.10406", "title": "3DQD: Generalized Deep 3D Shape Prior via Part-Discretized Diffusion\n  Process", "abstract": "  We develop a generalized 3D shape generation prior model, tailored for\nmultiple 3D tasks including unconditional shape generation, point cloud\ncompletion, and cross-modality shape generation, etc. On one hand, to precisely\ncapture local fine detailed shape information, a vector quantized variational\nautoencoder (VQ-VAE) is utilized to index local geometry from a compactly\nlearned codebook based on a broad set of task training data. On the other hand,\na discrete diffusion generator is introduced to model the inherent structural\ndependencies among different tokens. In the meantime, a multi-frequency fusion\nmodule (MFM) is developed to suppress high-frequency shape feature\nfluctuations, guided by multi-frequency contextual information. The above\ndesigns jointly equip our proposed 3D shape prior model with high-fidelity,\ndiverse features as well as the capability of cross-modality alignment, and\nextensive experiments have demonstrated superior performances on various 3D\nshape generation tasks.\n", "date": "2023-03-18", "categories": []}
{"id": "2303.11235", "title": "FullFormer: Generating Shapes Inside Shapes", "abstract": "  Implicit generative models have been widely employed to model 3D data and\nhave recently proven to be successful in encoding and generating high-quality\n3D shapes. This work builds upon these models and alleviates current\nlimitations by presenting the first implicit generative model that facilitates\nthe generation of complex 3D shapes with rich internal geometric details. To\nachieve this, our model uses unsigned distance fields to represent nested 3D\nsurfaces allowing learning from non-watertight mesh data. We propose a\ntransformer-based autoregressive model for 3D shape generation that leverages\ncontext-rich tokens from vector quantized shape embeddings. The generated\ntokens are decoded into an unsigned distance field which is rendered into a\nnovel 3D shape exhibiting a rich internal structure. We demonstrate that our\nmodel achieves state-of-the-art point cloud generation results on popular\nclasses of 'Cars', 'Planes', and 'Chairs' of the ShapeNet dataset.\nAdditionally, we curate a dataset that exclusively comprises shapes with\nrealistic internal details from the `Cars' class of ShapeNet and demonstrate\nour method's efficacy in generating these shapes with internal geometry.\n", "date": "2023-03-20", "categories": []}
{"id": "2211.13757", "title": "Diffusion-SDF: Conditional Generative Modeling of Signed Distance\n  Functions", "abstract": "  Probabilistic diffusion models have achieved state-of-the-art results for\nimage synthesis, inpainting, and text-to-image tasks. However, they are still\nin the early stages of generating complex 3D shapes. This work proposes\nDiffusion-SDF, a generative model for shape completion, single-view\nreconstruction, and reconstruction of real-scanned point clouds. We use neural\nsigned distance functions (SDFs) as our 3D representation to parameterize the\ngeometry of various signals (e.g., point clouds, 2D images) through neural\nnetworks. Neural SDFs are implicit functions and diffusing them amounts to\nlearning the reversal of their neural network weights, which we solve using a\ncustom modulation module. Extensive experiments show that our method is capable\nof both realistic unconditional generation and conditional generation from\npartial inputs. This work expands the domain of diffusion models from learning\n2D, explicit representations, to 3D, implicit representations.\n", "date": "2022-11-24", "categories": []}
{"id": "2303.07938", "title": "Controllable Mesh Generation Through Sparse Latent Point Diffusion\n  Models", "abstract": "  Mesh generation is of great value in various applications involving computer\ngraphics and virtual content, yet designing generative models for meshes is\nchallenging due to their irregular data structure and inconsistent topology of\nmeshes in the same category. In this work, we design a novel sparse latent\npoint diffusion model for mesh generation. Our key insight is to regard point\nclouds as an intermediate representation of meshes, and model the distribution\nof point clouds instead. While meshes can be generated from point clouds via\ntechniques like Shape as Points (SAP), the challenges of directly generating\nmeshes can be effectively avoided. To boost the efficiency and controllability\nof our mesh generation method, we propose to further encode point clouds to a\nset of sparse latent points with point-wise semantic meaningful features, where\ntwo DDPMs are trained in the space of sparse latent points to respectively\nmodel the distribution of the latent point positions and features at these\nlatent points. We find that sampling in this latent space is faster than\ndirectly sampling dense point clouds. Moreover, the sparse latent points also\nenable us to explicitly control both the overall structures and local details\nof the generated meshes. Extensive experiments are conducted on the ShapeNet\ndataset, where our proposed sparse latent point diffusion model achieves\nsuperior performance in terms of generation quality and controllability when\ncompared to existing methods.\n", "date": "2023-03-14", "categories": []}
{"id": "2303.06821", "title": "SDF-3DGAN: A 3D Object Generative Method Based on Implicit Signed\n  Distance Function", "abstract": "  In this paper, we develop a new method, termed SDF-3DGAN, for 3D object\ngeneration and 3D-Aware image synthesis tasks, which introduce implicit Signed\nDistance Function (SDF) as the 3D object representation method in the\ngenerative field. We apply SDF for higher quality representation of 3D object\nin space and design a new SDF neural renderer, which has higher efficiency and\nhigher accuracy. To train only on 2D images, we first generate the objects,\nwhich are represented by SDF, from Gaussian distribution. Then we render them\nto 2D images and use them to apply GAN training method together with 2D images\nin the dataset. In the new rendering method, we relieve all the potential of\nSDF mathematical property to alleviate computation pressure in the previous SDF\nneural renderer. In specific, our new SDF neural renderer can solve the problem\nof sampling ambiguity when the number of sampling point is not enough, \\ie use\nthe less points to finish higher quality sampling task in the rendering\npipeline. And in this rendering pipeline, we can locate the surface easily.\nTherefore, we apply normal loss on it to control the smoothness of generated\nobject surface, which can make our method enjoy the much higher generation\nquality. Quantitative and qualitative experiments conducted on public\nbenchmarks demonstrate favorable performance against the state-of-the-art\nmethods in 3D object generation task and 3D-Aware image synthesis task. Our\ncodes will be released at https://github.com/lutao2021/SDF-3DGAN.\n", "date": "2023-03-13", "categories": []}
{"id": "2303.02426", "title": "Improving the quality of dental crown using a Transformer-based method", "abstract": "  Designing a synthetic crown is a time-consuming, inconsistent, and\nlabor-intensive process. In this work, we present a fully automatic method that\nnot only learns human design dental crowns, but also improves the consistency,\nfunctionality, and esthetic of the crowns. Following success in point cloud\ncompletion using the transformer-based network, we tackle the problem of the\ncrown generation as a point-cloud completion around a prepared tooth. To this\nend, we use a geometry-aware transformer to generate dental crowns. Our main\ncontribution is to add a margin line information to the network, as the\naccuracy of generating a precise margin line directly,determines whether the\ndesigned crown and prepared tooth are closely matched to\nallowappropriateadhesion.Using our ground truth crown, we can extract the\nmargin line as a spline and sample the spline into 1000 points. We feed the\nobtained margin line along with two neighbor teeth of the prepared tooth and\nthree closest teeth in the opposing jaw. We also add the margin line points to\nour ground truth crown to increase the resolution at the margin line. Our\nexperimental results show an improvement in the quality of the designed crown\nwhen considering the actual context composed of the prepared tooth along with\nthe margin line compared with a crown generated in an empty space as was done\nby other studies in the literature.\n", "date": "2023-03-04", "categories": []}
{"id": "2303.01416", "title": "3D generation on ImageNet", "abstract": "  Existing 3D-from-2D generators are typically designed for well-curated\nsingle-category datasets, where all the objects have (approximately) the same\nscale, 3D location, and orientation, and the camera always points to the center\nof the scene. This makes them inapplicable to diverse, in-the-wild datasets of\nnon-alignable scenes rendered from arbitrary camera poses. In this work, we\ndevelop a 3D generator with Generic Priors (3DGP): a 3D synthesis framework\nwith more general assumptions about the training data, and show that it scales\nto very challenging datasets, like ImageNet. Our model is based on three new\nideas. First, we incorporate an inaccurate off-the-shelf depth estimator into\n3D GAN training via a special depth adaptation module to handle the\nimprecision. Then, we create a flexible camera model and a regularization\nstrategy for it to learn its distribution parameters during training. Finally,\nwe extend the recent ideas of transferring knowledge from pre-trained\nclassifiers into GANs for patch-wise trained models by employing a simple\ndistillation-based technique on top of the discriminator. It achieves more\nstable training than the existing methods and speeds up the convergence by at\nleast 40%. We explore our model on four datasets: SDIP Dogs 256x256, SDIP\nElephants 256x256, LSUN Horses 256x256, and ImageNet 256x256, and demonstrate\nthat 3DGP outperforms the recent state-of-the-art in terms of both texture and\ngeometry quality. Code and visualizations:\nhttps://snap-research.github.io/3dgp.\n", "date": "2023-03-02", "categories": []}
{"id": "2303.00167", "title": "Sketch2Cloth: Sketch-based 3D Garment Generation with Unsigned Distance\n  Fields", "abstract": "  3D model reconstruction from a single image has achieved great progress with\nthe recent deep generative models. However, the conventional reconstruction\napproaches with template mesh deformation and implicit fields have difficulty\nin reconstructing non-watertight 3D mesh models, such as garments. In contrast\nto image-based modeling, the sketch-based approach can help users generate 3D\nmodels to meet the design intentions from hand-drawn sketches. In this study,\nwe propose Sketch2Cloth, a sketch-based 3D garment generation system using the\nunsigned distance fields from the user's sketch input. Sketch2Cloth first\nestimates the unsigned distance function of the target 3D model from the sketch\ninput, and extracts the mesh from the estimated field with Marching Cubes. We\nalso provide the model editing function to modify the generated mesh. We\nverified the proposed Sketch2Cloth with quantitative evaluations on garment\ngeneration and editing with a state-of-the-art approach.\n", "date": "2023-03-01", "categories": []}
{"id": "2302.13408", "title": "Generative Models for 3D Point Clouds", "abstract": "  Point clouds are rich geometric data structures, where their three\ndimensional structure offers an excellent domain for understanding the\nrepresentation learning and generative modeling in 3D space. In this work, we\naim to improve the performance of point cloud latent-space generative models by\nexperimenting with transformer encoders, latent-space flow models, and\nautoregressive decoders. We analyze and compare both generation and\nreconstruction performance of these models on various object types.\n", "date": "2023-02-26", "categories": []}
{"id": "2209.04145", "title": "ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation", "abstract": "  Text-guided 3D shape generation remains challenging due to the absence of\nlarge paired text-shape data, the substantial semantic gap between these two\nmodalities, and the structural complexity of 3D shapes. This paper presents a\nnew framework called Image as Stepping Stone (ISS) for the task by introducing\n2D image as a stepping stone to connect the two modalities and to eliminate the\nneed for paired text-shape data. Our key contribution is a two-stage\nfeature-space-alignment approach that maps CLIP features to shapes by\nharnessing a pre-trained single-view reconstruction (SVR) model with multi-view\nsupervisions: first map the CLIP image feature to the detail-rich shape space\nin the SVR model, then map the CLIP text feature to the shape space and\noptimize the mapping by encouraging CLIP consistency between the input text and\nthe rendered images. Further, we formulate a text-guided shape stylization\nmodule to dress up the output shapes with novel textures. Beyond existing works\non 3D shape generation from text, our new approach is general for creating\nshapes in a broad range of categories, without requiring paired text-shape\ndata. Experimental results manifest that our approach outperforms the\nstate-of-the-arts and our baselines in terms of fidelity and consistency with\ntext. Further, our approach can stylize the generated shapes with both\nrealistic and fantasy structures and textures.\n", "date": "2022-09-09", "categories": []}
{"id": "2302.09486", "title": "LC-NeRF: Local Controllable Face Generation in Neural Randiance Field", "abstract": "  3D face generation has achieved high visual quality and 3D consistency thanks\nto the development of neural radiance fields (NeRF). Recently, to generate and\nedit 3D faces with NeRF representation, some methods are proposed and achieve\ngood results in decoupling geometry and texture. The latent codes of these\ngenerative models affect the whole face, and hence modifications to these codes\ncause the entire face to change. However, users usually edit a local region\nwhen editing faces and do not want other regions to be affected. Since changes\nto the latent code affect global generation results, these methods do not allow\nfor fine-grained control of local facial regions. To improve local\ncontrollability in NeRF-based face editing, we propose LC-NeRF, which is\ncomposed of a Local Region Generators Module and a Spatial-Aware Fusion Module,\nallowing for local geometry and texture control of local facial regions.\nQualitative and quantitative evaluations show that our method provides better\nlocal editing than state-of-the-art face editing methods. Our method also\nperforms well in downstream tasks, such as text-driven facial image editing.\n", "date": "2023-02-19", "categories": []}
{"id": "2302.00190", "title": "Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and\n  Manipulation", "abstract": "  This paper presents a new approach for 3D shape generation, inversion, and\nmanipulation, through a direct generative modeling on a continuous implicit\nrepresentation in wavelet domain. Specifically, we propose a compact wavelet\nrepresentation with a pair of coarse and detail coefficient volumes to\nimplicitly represent 3D shapes via truncated signed distance functions and\nmulti-scale biorthogonal wavelets. Then, we design a pair of neural networks: a\ndiffusion-based generator to produce diverse shapes in the form of the coarse\ncoefficient volumes and a detail predictor to produce compatible detail\ncoefficient volumes for introducing fine structures and details. Further, we\nmay jointly train an encoder network to learn a latent space for inverting\nshapes, allowing us to enable a rich variety of whole-shape and region-aware\nshape manipulations. Both quantitative and qualitative experimental results\nmanifest the compelling shape generation, inversion, and manipulation\ncapabilities of our approach over the state-of-the-art methods.\n", "date": "2023-02-01", "categories": []}
{"id": "2301.06015", "title": "Diffusion-based Generation, Optimization, and Planning in 3D Scenes", "abstract": "  We introduce SceneDiffuser, a conditional generative model for 3D scene\nunderstanding. SceneDiffuser provides a unified model for solving\nscene-conditioned generation, optimization, and planning. In contrast to prior\nworks, SceneDiffuser is intrinsically scene-aware, physics-based, and\ngoal-oriented. With an iterative sampling strategy, SceneDiffuser jointly\nformulates the scene-aware generation, physics-based optimization, and\ngoal-oriented planning via a diffusion-based denoising process in a fully\ndifferentiable fashion. Such a design alleviates the discrepancies among\ndifferent modules and the posterior collapse of previous scene-conditioned\ngenerative models. We evaluate SceneDiffuser with various 3D scene\nunderstanding tasks, including human pose and motion generation, dexterous\ngrasp generation, path planning for 3D navigation, and motion planning for\nrobot arms. The results show significant improvements compared with previous\nmodels, demonstrating the tremendous potential of SceneDiffuser for the broad\ncommunity of 3D scene understanding.\n", "date": "2023-01-15", "categories": []}
{"id": "2207.12297", "title": "TreeSketchNet: From Sketch To 3D Tree Parameters Generation", "abstract": "  3D modeling of non-linear objects from stylized sketches is a challenge even\nfor experts in Computer Graphics (CG). The extrapolation of objects parameters\nfrom a stylized sketch is a very complex and cumbersome task. In the present\nstudy, we propose a broker system that mediates between the modeler and the 3D\nmodelling software and can transform a stylized sketch of a tree into a\ncomplete 3D model. The input sketches do not need to be accurate or detailed,\nand only need to represent a rudimentary outline of the tree that the modeler\nwishes to 3D-model. Our approach is based on a well-defined Deep Neural Network\n(DNN) architecture, we called TreeSketchNet (TSN), based on convolutions and\nable to generate Weber and Penn parameters that can be interpreted by the\nmodelling software to generate a 3D model of a tree starting from a simple\nsketch. The training dataset consists of Synthetically-Generated\n\\revision{(SG)} sketches that are associated with Weber-Penn parameters\ngenerated by a dedicated Blender modelling software add-on. The accuracy of the\nproposed method is demonstrated by testing the TSN with both synthetic and\nhand-made sketches. Finally, we provide a qualitative analysis of our results,\nby evaluating the coherence of the predicted parameters with several\ndistinguishing features.\n", "date": "2022-07-25", "categories": []}
{"id": "2212.08751", "title": "Point-E: A System for Generating 3D Point Clouds from Complex Prompts", "abstract": "  While recent work on text-conditional 3D object generation has shown\npromising results, the state-of-the-art methods typically require multiple\nGPU-hours to produce a single sample. This is in stark contrast to\nstate-of-the-art generative image models, which produce samples in a number of\nseconds or minutes. In this paper, we explore an alternative method for 3D\nobject generation which produces 3D models in only 1-2 minutes on a single GPU.\nOur method first generates a single synthetic view using a text-to-image\ndiffusion model, and then produces a 3D point cloud using a second diffusion\nmodel which conditions on the generated image. While our method still falls\nshort of the state-of-the-art in terms of sample quality, it is one to two\norders of magnitude faster to sample from, offering a practical trade-off for\nsome use cases. We release our pre-trained point cloud diffusion models, as\nwell as evaluation code and models, at https://github.com/openai/point-e.\n", "date": "2022-12-16", "categories": []}
{"id": "2208.02946", "title": "Learning to Generate 3D Shapes from a Single Example", "abstract": "  Existing generative models for 3D shapes are typically trained on a large 3D\ndataset, often of a specific object category. In this paper, we investigate the\ndeep generative model that learns from only a single reference 3D shape.\nSpecifically, we present a multi-scale GAN-based model designed to capture the\ninput shape's geometric features across a range of spatial scales. To avoid\nlarge memory and computational cost induced by operating on the 3D volume, we\nbuild our generator atop the tri-plane hybrid representation, which requires\nonly 2D convolutions. We train our generative model on a voxel pyramid of the\nreference shape, without the need of any external supervision or manual\nannotation. Once trained, our model can generate diverse and high-quality 3D\nshapes possibly of different sizes and aspect ratios. The resulting shapes\npresent variations across different scales, and at the same time retain the\nglobal structure of the reference shape. Through extensive evaluation, both\nqualitative and quantitative, we demonstrate that our model can generate 3D\nshapes of various types.\n", "date": "2022-08-05", "categories": []}
{"id": "2206.10535", "title": "EpiGRAF: Rethinking training of 3D GANs", "abstract": "  A very recent trend in generative modeling is building 3D-aware generators\nfrom 2D image collections. To induce the 3D bias, such models typically rely on\nvolumetric rendering, which is expensive to employ at high resolutions. During\nthe past months, there appeared more than 10 works that address this scaling\nissue by training a separate 2D decoder to upsample a low-resolution image (or\na feature tensor) produced from a pure 3D generator. But this solution comes at\na cost: not only does it break multi-view consistency (i.e. shape and texture\nchange when the camera moves), but it also learns the geometry in a low\nfidelity. In this work, we show that it is possible to obtain a high-resolution\n3D generator with SotA image quality by following a completely different route\nof simply training the model patch-wise. We revisit and improve this\noptimization scheme in two ways. First, we design a location- and scale-aware\ndiscriminator to work on patches of different proportions and spatial\npositions. Second, we modify the patch sampling strategy based on an annealed\nbeta distribution to stabilize training and accelerate the convergence. The\nresulted model, named EpiGRAF, is an efficient, high-resolution, pure 3D\ngenerator, and we test it on four datasets (two introduced in this work) at\n$256^2$ and $512^2$ resolutions. It obtains state-of-the-art image quality,\nhigh-fidelity geometry and trains ${\\approx} 2.5 \\times$ faster than the\nupsampler-based counterparts. Project website:\nhttps://universome.github.io/epigraf.\n", "date": "2022-06-21", "categories": []}
{"id": "2212.00842", "title": "3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models", "abstract": "  Diffusion models have shown great promise for image generation, beating GANs\nin terms of generation diversity, with comparable image quality. However, their\napplication to 3D shapes has been limited to point or voxel representations\nthat can in practice not accurately represent a 3D surface. We propose a\ndiffusion model for neural implicit representations of 3D shapes that operates\nin the latent space of an auto-decoder. This allows us to generate diverse and\nhigh quality 3D surfaces. We additionally show that we can condition our model\non images or text to enable image-to-3D generation and text-to-3D generation\nusing CLIP embeddings. Furthermore, adding noise to the latent codes of\nexisting shapes allows us to explore shape variations.\n", "date": "2022-12-01", "categories": []}
{"id": "2212.06135", "title": "Rodin: A Generative Model for Sculpting 3D Digital Avatars Using\n  Diffusion", "abstract": "  This paper presents a 3D generative model that uses diffusion models to\nautomatically generate 3D digital avatars represented as neural radiance\nfields. A significant challenge in generating such avatars is that the memory\nand processing costs in 3D are prohibitive for producing the rich details\nrequired for high-quality avatars. To tackle this problem we propose the\nroll-out diffusion network (Rodin), which represents a neural radiance field as\nmultiple 2D feature maps and rolls out these maps into a single 2D feature\nplane within which we perform 3D-aware diffusion. The Rodin model brings the\nmuch-needed computational efficiency while preserving the integrity of\ndiffusion in 3D by using 3D-aware convolution that attends to projected\nfeatures in the 2D feature plane according to their original relationship in\n3D. We also use latent conditioning to orchestrate the feature generation for\nglobal coherence, leading to high-fidelity avatars and enabling their semantic\nediting based on text prompts. Finally, we use hierarchical synthesis to\nfurther enhance details. The 3D avatars generated by our model compare\nfavorably with those produced by existing generative techniques. We can\ngenerate highly detailed avatars with realistic hairstyles and facial hair like\nbeards. We also demonstrate 3D avatar generation from image or text as well as\ntext-guided editability.\n", "date": "2022-12-12", "categories": []}
{"id": "2212.01747", "title": "Fast Point Cloud Generation with Straight Flows", "abstract": "  Diffusion models have emerged as a powerful tool for point cloud generation.\nA key component that drives the impressive performance for generating\nhigh-quality samples from noise is iteratively denoise for thousands of steps.\nWhile beneficial, the complexity of learning steps has limited its applications\nto many 3D real-world. To address this limitation, we propose Point Straight\nFlow (PSF), a model that exhibits impressive performance using one step. Our\nidea is based on the reformulation of the standard diffusion model, which\noptimizes the curvy learning trajectory into a straight path. Further, we\ndevelop a distillation strategy to shorten the straight path into one step\nwithout a performance loss, enabling applications to 3D real-world with latency\nconstraints. We perform evaluations on multiple 3D tasks and find that our PSF\nperforms comparably to the standard diffusion model, outperforming other\nefficient 3D point cloud generation methods. On real-world applications such as\npoint cloud completion and training-free text-guided generation in a\nlow-latency setup, PSF performs favorably.\n", "date": "2022-12-04", "categories": []}
{"id": "2212.00774", "title": "Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D\n  Generation", "abstract": "  A diffusion model learns to predict a vector field of gradients. We propose\nto apply chain rule on the learned gradients, and back-propagate the score of a\ndiffusion model through the Jacobian of a differentiable renderer, which we\ninstantiate to be a voxel radiance field. This setup aggregates 2D scores at\nmultiple camera viewpoints into a 3D score, and repurposes a pretrained 2D\nmodel for 3D data generation. We identify a technical challenge of distribution\nmismatch that arises in this application, and propose a novel estimation\nmechanism to resolve it. We run our algorithm on several off-the-shelf\ndiffusion image generative models, including the recently released Stable\nDiffusion trained on the large-scale LAION dataset.\n", "date": "2022-12-01", "categories": []}
{"id": "2211.16677", "title": "3D Neural Field Generation using Triplane Diffusion", "abstract": "  Diffusion models have emerged as the state-of-the-art for image generation,\namong other tasks. Here, we present an efficient diffusion-based model for\n3D-aware generation of neural fields. Our approach pre-processes training data,\nsuch as ShapeNet meshes, by converting them to continuous occupancy fields and\nfactoring them into a set of axis-aligned triplane feature representations.\nThus, our 3D training scenes are all represented by 2D feature planes, and we\ncan directly train existing 2D diffusion models on these representations to\ngenerate 3D neural fields with high quality and diversity, outperforming\nalternative approaches to 3D-aware generation. Our approach requires essential\nmodifications to existing triplane factorization pipelines to make the\nresulting features easy to learn for the diffusion model. We demonstrate\nstate-of-the-art results on 3D generation on several object classes from\nShapeNet.\n", "date": "2022-11-30", "categories": []}
{"id": "2211.09770", "title": "3DLatNav: Navigating Generative Latent Spaces for Semantic-Aware 3D\n  Object Manipulation", "abstract": "  3D generative models have been recently successful in generating realistic 3D\nobjects in the form of point clouds. However, most models do not offer\ncontrollability to manipulate the shape semantics of component object parts\nwithout extensive semantic attribute labels or other reference point clouds.\nMoreover, beyond the ability to perform simple latent vector arithmetic or\ninterpolations, there is a lack of understanding of how part-level semantics of\n3D shapes are encoded in their corresponding generative latent spaces. In this\npaper, we propose 3DLatNav; a novel approach to navigating pretrained\ngenerative latent spaces to enable controlled part-level semantic manipulation\nof 3D objects. First, we propose a part-level weakly-supervised shape semantics\nidentification mechanism using latent representations of 3D shapes. Then, we\ntransfer that knowledge to a pretrained 3D object generative latent space to\nunravel disentangled embeddings to represent different shape semantics of\ncomponent parts of an object in the form of linear subspaces, despite the\nunavailability of part-level labels during the training. Finally, we utilize\nthose identified subspaces to show that controllable 3D object part\nmanipulation can be achieved by applying the proposed framework to any\npretrained 3D generative model. With two novel quantitative metrics to evaluate\nthe consistency and localization accuracy of part-level manipulations, we show\nthat 3DLatNav outperforms existing unsupervised latent disentanglement methods\nin identifying latent directions that encode part-level shape semantics of 3D\nobjects. With multiple ablation studies and testing on state-of-the-art\ngenerative models, we show that 3DLatNav can implement controlled part-level\nsemantic manipulations on an input point cloud while preserving other features\nand the realistic nature of the object.\n", "date": "2022-11-17", "categories": []}
{"id": "2211.07600", "title": "Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures", "abstract": "  Text-guided image generation has progressed rapidly in recent years,\ninspiring major breakthroughs in text-guided shape generation. Recently, it has\nbeen shown that using score distillation, one can successfully text-guide a\nNeRF model to generate a 3D object. We adapt the score distillation to the\npublicly available, and computationally efficient, Latent Diffusion Models,\nwhich apply the entire diffusion process in a compact latent space of a\npretrained autoencoder. As NeRFs operate in image space, a naive solution for\nguiding them with latent score distillation would require encoding to the\nlatent space at each guidance step. Instead, we propose to bring the NeRF to\nthe latent space, resulting in a Latent-NeRF. Analyzing our Latent-NeRF, we\nshow that while Text-to-3D models can generate impressive results, they are\ninherently unconstrained and may lack the ability to guide or enforce a\nspecific 3D structure. To assist and direct the 3D generation, we propose to\nguide our Latent-NeRF using a Sketch-Shape: an abstract geometry that defines\nthe coarse structure of the desired object. Then, we present means to integrate\nsuch a constraint directly into a Latent-NeRF. This unique combination of text\nand shape guidance allows for increased control over the generation process. We\nalso show that latent score distillation can be successfully applied directly\non 3D meshes. This allows for generating high-quality textures on a given\ngeometry. Our experiments validate the power of our different forms of guidance\nand the efficiency of using latent rendering. Implementation is available at\nhttps://github.com/eladrich/latent-nerf\n", "date": "2022-11-14", "categories": []}
{"id": "2210.17344", "title": "gCoRF: Generative Compositional Radiance Fields", "abstract": "  3D generative models of objects enable photorealistic image synthesis with 3D\ncontrol. Existing methods model the scene as a global scene representation,\nignoring the compositional aspect of the scene. Compositional reasoning can\nenable a wide variety of editing applications, in addition to enabling\ngeneralizable 3D reasoning. In this paper, we present a compositional\ngenerative model, where each semantic part of the object is represented as an\nindependent 3D representation learned from only in-the-wild 2D data. We start\nwith a global generative model (GAN) and learn to decompose it into different\nsemantic parts using supervision from 2D segmentation masks. We then learn to\ncomposite independently sampled parts in order to create coherent global\nscenes. Different parts can be independently sampled while keeping the rest of\nthe object fixed. We evaluate our method on a wide variety of objects and parts\nand demonstrate editing applications.\n", "date": "2022-10-31", "categories": []}
{"id": "2209.04300", "title": "Towards Confidence-guided Shape Completion for Robotic Applications", "abstract": "  Many robotic tasks involving some form of 3D visual perception greatly\nbenefit from a complete knowledge of the working environment. However, robots\noften have to tackle unstructured environments and their onboard visual sensors\ncan only provide incomplete information due to limited workspaces, clutter or\nobject self-occlusion. In recent years, deep learning architectures for shape\ncompletion have begun taking traction as effective means of inferring a\ncomplete 3D object representation from partial visual data. Nevertheless, most\nof the existing state-of-the-art approaches provide a fixed output resolution\nin the form of voxel grids, strictly related to the size of the neural network\noutput stage. While this is enough for some tasks, e.g. obstacle avoidance in\nnavigation, grasping and manipulation require finer resolutions and simply\nscaling up the neural network outputs is computationally expensive. In this\npaper, we address this limitation by proposing an object shape completion\nmethod based on an implicit 3D representation providing a confidence value for\neach reconstructed point. As a second contribution, we propose a gradient-based\nmethod for efficiently sampling such implicit function at an arbitrary\nresolution, tunable at inference time. We experimentally validate our approach\nby comparing reconstructed shapes with ground truths, and by deploying our\nshape completion algorithm in a robotic grasping pipeline. In both cases, we\ncompare results with a state-of-the-art shape completion approach.\n", "date": "2022-09-09", "categories": []}
{"id": "2209.05434", "title": "3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation", "abstract": "  In contrast to the traditional avatar creation pipeline which is a costly\nprocess, contemporary generative approaches directly learn the data\ndistribution from photographs. While plenty of works extend unconditional\ngenerative models and achieve some levels of controllability, it is still\nchallenging to ensure multi-view consistency, especially in large poses. In\nthis work, we propose a network that generates 3D-aware portraits while being\ncontrollable according to semantic parameters regarding pose, identity,\nexpression and illumination. Our network uses neural scene representation to\nmodel 3D-aware portraits, whose generation is guided by a parametric face model\nthat supports explicit control. While the latent disentanglement can be further\nenhanced by contrasting images with partially different attributes, there still\nexists noticeable inconsistency in non-face areas, e.g., hair and background,\nwhen animating expressions. Wesolve this by proposing a volume blending\nstrategy in which we form a composite output by blending dynamic and static\nareas, with two parts segmented from the jointly learned semantic field. Our\nmethod outperforms prior arts in extensive experiments, producing realistic\nportraits with vivid expression in natural lighting when viewed from free\nviewpoints. It also demonstrates generalization ability to real images as well\nas out-of-domain data, showing great promise in real applications.\n", "date": "2022-09-12", "categories": []}
{"id": "2210.06978", "title": "LION: Latent Point Diffusion Models for 3D Shape Generation", "abstract": "  Denoising diffusion models (DDMs) have shown promising results in 3D point\ncloud synthesis. To advance 3D DDMs and make them useful for digital artists,\nwe require (i) high generation quality, (ii) flexibility for manipulation and\napplications such as conditional synthesis and shape interpolation, and (iii)\nthe ability to output smooth surfaces or meshes. To this end, we introduce the\nhierarchical Latent Point Diffusion Model (LION) for 3D shape generation. LION\nis set up as a variational autoencoder (VAE) with a hierarchical latent space\nthat combines a global shape latent representation with a point-structured\nlatent space. For generation, we train two hierarchical DDMs in these latent\nspaces. The hierarchical VAE approach boosts performance compared to DDMs that\noperate on point clouds directly, while the point-structured latents are still\nideally suited for DDM-based modeling. Experimentally, LION achieves\nstate-of-the-art generation performance on multiple ShapeNet benchmarks.\nFurthermore, our VAE framework allows us to easily use LION for different\nrelevant tasks: LION excels at multimodal shape denoising and voxel-conditioned\nsynthesis, and it can be adapted for text- and image-driven 3D generation. We\nalso demonstrate shape autoencoding and latent shape interpolation, and we\naugment LION with modern surface reconstruction techniques to generate smooth\n3D meshes. We hope that LION provides a powerful tool for artists working with\n3D shapes due to its high-quality generation, flexibility, and surface\nreconstruction. Project page and code: https://nv-tlabs.github.io/LION.\n", "date": "2022-10-12", "categories": []}
{"id": "2210.05735", "title": "TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation", "abstract": "  We present TetGAN, a convolutional neural network designed to generate\ntetrahedral meshes. We represent shapes using an irregular tetrahedral grid\nwhich encodes an occupancy and displacement field. Our formulation enables\ndefining tetrahedral convolution, pooling, and upsampling operations to\nsynthesize explicit mesh connectivity with variable topological genus. The\nproposed neural network layers learn deep features over each tetrahedron and\nlearn to extract patterns within spatial regions across multiple scales. We\nillustrate the capabilities of our technique to encode tetrahedral meshes into\na semantically meaningful latent-space which can be used for shape editing and\nsynthesis. Our project page is at https://threedle.github.io/tetGAN/.\n", "date": "2022-10-11", "categories": []}
{"id": "2210.04072", "title": "Flow-based GAN for 3D Point Cloud Generation from a Single Image", "abstract": "  Generating a 3D point cloud from a single 2D image is of great importance for\n3D scene understanding applications. To reconstruct the whole 3D shape of the\nobject shown in the image, the existing deep learning based approaches use\neither explicit or implicit generative modeling of point clouds, which,\nhowever, suffer from limited quality. In this work, we aim to alleviate this\nissue by introducing a hybrid explicit-implicit generative modeling scheme,\nwhich inherits the flow-based explicit generative models for sampling point\nclouds with arbitrary resolutions while improving the detailed 3D structures of\npoint clouds by leveraging the implicit generative adversarial networks (GANs).\nWe evaluate on the large-scale synthetic dataset ShapeNet, with the\nexperimental results demonstrating the superior performance of the proposed\nmethod. In addition, the generalization ability of our method is demonstrated\nby performing on cross-category synthetic images as well as by testing on real\nimages from PASCAL3D+ dataset.\n", "date": "2022-10-08", "categories": []}
{"id": "2210.03158", "title": "Neural Volumetric Mesh Generator", "abstract": "  Deep generative models have shown success in generating 3D shapes with\ndifferent representations. In this work, we propose Neural Volumetric Mesh\nGenerator(NVMG) which can generate novel and high-quality volumetric meshes.\nUnlike the previous 3D generative model for point cloud, voxel, and implicit\nsurface, the volumetric mesh representation is a ready-to-use representation in\nindustry with details on both the surface and interior. Generating this such\nhighly-structured data thus brings a significant challenge. We first propose a\ndiffusion-based generative model to tackle this problem by generating voxelized\nshapes with close-to-reality outlines and structures. We can simply obtain a\ntetrahedral mesh as a template with the voxelized shape. Further, we use a\nvoxel-conditional neural network to predict the smooth implicit surface\nconditioned on the voxels, and progressively project the tetrahedral mesh to\nthe predicted surface under regularizations. The regularization terms are\ncarefully designed so that they can (1) get rid of the defects like flipping\nand high distortion; (2) force the regularity of the interior and surface\nstructure during the deformation procedure for a high-quality final mesh. As\nshown in the experiments, our pipeline can generate high-quality artifact-free\nvolumetric and surface meshes from random noise or a reference image without\nany post-processing. Compared with the state-of-the-art voxel-to-mesh\ndeformation method, we show more robustness and better performance when taking\ngenerated voxels as input.\n", "date": "2022-10-06", "categories": []}
{"id": "2210.03007", "title": "XDGAN: Multi-Modal 3D Shape Generation in 2D Space", "abstract": "  Generative models for 2D images has recently seen tremendous progress in\nquality, resolution and speed as a result of the efficiency of 2D convolutional\narchitectures. However it is difficult to extend this progress into the 3D\ndomain since most current 3D representations rely on custom network components.\nThis paper addresses a central question: Is it possible to directly leverage 2D\nimage generative models to generate 3D shapes instead? To answer this, we\npropose XDGAN, an effective and fast method for applying 2D image GAN\narchitectures to the generation of 3D object geometry combined with additional\nsurface attributes, like color textures and normals. Specifically, we propose a\nnovel method to convert 3D shapes into compact 1-channel geometry images and\nleverage StyleGAN3 and image-to-image translation networks to generate 3D\nobjects in 2D space. The generated geometry images are quick to convert to 3D\nmeshes, enabling real-time 3D object synthesis, visualization and interactive\nediting. Moreover, the use of standard 2D architectures can help bring more 2D\nadvances into the 3D realm. We show both quantitatively and qualitatively that\nour method is highly effective at various tasks such as 3D shape generation,\nsingle view reconstruction and shape manipulation, while being significantly\nfaster and more flexible compared to recent 3D generative models.\n", "date": "2022-10-06", "categories": []}
{"id": "2209.15172", "title": "Understanding Pure CLIP Guidance for Voxel Grid NeRF Models", "abstract": "  We explore the task of text to 3D object generation using CLIP. Specifically,\nwe use CLIP for guidance without access to any datasets, a setting we refer to\nas pure CLIP guidance. While prior work has adopted this setting, there is no\nsystematic study of mechanics for preventing adversarial generations within\nCLIP. We illustrate how different image-based augmentations prevent the\nadversarial generation problem, and how the generated results are impacted. We\ntest different CLIP model architectures and show that ensembling different\nmodels for guidance can prevent adversarial generations within bigger models\nand generate sharper results. Furthermore, we implement an implicit voxel grid\nmodel to show how neural networks provide an additional layer of\nregularization, resulting in better geometrical structure and coherency of\ngenerated objects. Compared to prior work, we achieve more coherent results\nwith higher memory efficiency and faster training speeds.\n", "date": "2022-09-30", "categories": []}
{"id": "2209.14988", "title": "DreamFusion: Text-to-3D using 2D Diffusion", "abstract": "  Recent breakthroughs in text-to-image synthesis have been driven by diffusion\nmodels trained on billions of image-text pairs. Adapting this approach to 3D\nsynthesis would require large-scale datasets of labeled 3D data and efficient\narchitectures for denoising 3D data, neither of which currently exist. In this\nwork, we circumvent these limitations by using a pretrained 2D text-to-image\ndiffusion model to perform text-to-3D synthesis. We introduce a loss based on\nprobability density distillation that enables the use of a 2D diffusion model\nas a prior for optimization of a parametric image generator. Using this loss in\na DeepDream-like procedure, we optimize a randomly-initialized 3D model (a\nNeural Radiance Field, or NeRF) via gradient descent such that its 2D\nrenderings from random angles achieve a low loss. The resulting 3D model of the\ngiven text can be viewed from any angle, relit by arbitrary illumination, or\ncomposited into any 3D environment. Our approach requires no 3D training data\nand no modifications to the image diffusion model, demonstrating the\neffectiveness of pretrained image diffusion models as priors.\n", "date": "2022-09-29", "categories": []}
{"id": "2209.11163", "title": "GET3D: A Generative Model of High Quality 3D Textured Shapes Learned\n  from Images", "abstract": "  As several industries are moving towards modeling massive 3D virtual worlds,\nthe need for content creation tools that can scale in terms of the quantity,\nquality, and diversity of 3D content is becoming evident. In our work, we aim\nto train performant 3D generative models that synthesize textured meshes which\ncan be directly consumed by 3D rendering engines, thus immediately usable in\ndownstream applications. Prior works on 3D generative modeling either lack\ngeometric details, are limited in the mesh topology they can produce, typically\ndo not support textures, or utilize neural renderers in the synthesis process,\nwhich makes their use in common 3D software non-trivial. In this work, we\nintroduce GET3D, a Generative model that directly generates Explicit Textured\n3D meshes with complex topology, rich geometric details, and high-fidelity\ntextures. We bridge recent success in the differentiable surface modeling,\ndifferentiable rendering as well as 2D Generative Adversarial Networks to train\nour model from 2D image collections. GET3D is able to generate high-quality 3D\ntextured meshes, ranging from cars, chairs, animals, motorbikes and human\ncharacters to buildings, achieving significant improvements over previous\nmethods.\n", "date": "2022-09-22", "categories": []}
{"id": "2209.08725", "title": "Neural Wavelet-domain Diffusion for 3D Shape Generation", "abstract": "  This paper presents a new approach for 3D shape generation, enabling direct\ngenerative modeling on a continuous implicit representation in wavelet domain.\nSpecifically, we propose a compact wavelet representation with a pair of coarse\nand detail coefficient volumes to implicitly represent 3D shapes via truncated\nsigned distance functions and multi-scale biorthogonal wavelets, and formulate\na pair of neural networks: a generator based on the diffusion model to produce\ndiverse shapes in the form of coarse coefficient volumes; and a detail\npredictor to further produce compatible detail coefficient volumes for\nenriching the generated shapes with fine structures and details. Both\nquantitative and qualitative experimental results manifest the superiority of\nour approach in generating diverse and high-quality shapes with complex\ntopology and structures, clean surfaces, and fine details, exceeding the 3D\ngeneration capabilities of the state-of-the-art models.\n", "date": "2022-09-19", "categories": []}
{"id": "2209.05710", "title": "MDM: Molecular Diffusion Model for 3D Molecule Generation", "abstract": "  Molecule generation, especially generating 3D molecular geometries from\nscratch (i.e., 3D \\textit{de novo} generation), has become a fundamental task\nin drug designs. Existing diffusion-based 3D molecule generation methods could\nsuffer from unsatisfactory performances, especially when generating large\nmolecules. At the same time, the generated molecules lack enough diversity.\nThis paper proposes a novel diffusion model to address those two challenges.\n  First, interatomic relations are not in molecules' 3D point cloud\nrepresentations. Thus, it is difficult for existing generative models to\ncapture the potential interatomic forces and abundant local constraints. To\ntackle this challenge, we propose to augment the potential interatomic forces\nand further involve dual equivariant encoders to encode interatomic forces of\ndifferent strengths. Second, existing diffusion-based models essentially shift\nelements in geometry along the gradient of data density. Such a process lacks\nenough exploration in the intermediate steps of the Langevin dynamics. To\naddress this issue, we introduce a distributional controlling variable in each\ndiffusion/reverse step to enforce thorough explorations and further improve\ngeneration diversity.\n  Extensive experiments on multiple benchmarks demonstrate that the proposed\nmodel significantly outperforms existing methods for both unconditional and\nconditional generation tasks. We also conduct case studies to help understand\nthe physicochemical properties of the generated molecules.\n", "date": "2022-09-13", "categories": []}
{"id": "2208.00561", "title": "AvatarGen: a 3D Generative Model for Animatable Human Avatars", "abstract": "  Unsupervised generation of clothed virtual humans with various appearance and\nanimatable poses is important for creating 3D human avatars and other AR/VR\napplications. Existing methods are either limited to rigid object modeling, or\nnot generative and thus unable to synthesize high-quality virtual humans and\nanimate them. In this work, we propose AvatarGen, the first method that enables\nnot only non-rigid human generation with diverse appearance but also full\ncontrol over poses and viewpoints, while only requiring 2D images for training.\nSpecifically, it extends the recent 3D GANs to clothed human generation by\nutilizing a coarse human body model as a proxy to warp the observation space\ninto a standard avatar under a canonical space. To model non-rigid dynamics, it\nintroduces a deformation network to learn pose-dependent deformations in the\ncanonical space. To improve geometry quality of the generated human avatars, it\nleverages signed distance field as geometric representation, which allows more\ndirect regularization from the body model on the geometry learning. Benefiting\nfrom these designs, our method can generate animatable human avatars with\nhigh-quality appearance and geometry modeling, significantly outperforming\nprevious 3D GANs. Furthermore, it is competent for many applications, e.g.,\nsingle-view reconstruction, reanimation, and text-guided synthesis. Code and\npre-trained model will be available.\n", "date": "2022-08-01", "categories": []}
{"id": "2207.11795", "title": "Cross-Modal 3D Shape Generation and Manipulation", "abstract": "  Creating and editing the shape and color of 3D objects require tremendous\nhuman effort and expertise. Compared to direct manipulation in 3D interfaces,\n2D interactions such as sketches and scribbles are usually much more natural\nand intuitive for the users. In this paper, we propose a generic multi-modal\ngenerative model that couples the 2D modalities and implicit 3D representations\nthrough shared latent spaces. With the proposed model, versatile 3D generation\nand manipulation are enabled by simply propagating the editing from a specific\n2D controlling modality through the latent spaces. For example, editing the 3D\nshape by drawing a sketch, re-colorizing the 3D surface via painting color\nscribbles on the 2D rendering, or generating 3D shapes of a certain category\ngiven one or a few reference images. Unlike prior works, our model does not\nrequire re-training or fine-tuning per editing task and is also conceptually\nsimple, easy to implement, robust to input domain shifts, and flexible to\ndiverse reconstruction on partial 2D inputs. We evaluate our framework on two\nrepresentative 2D modalities of grayscale line sketches and rendered color\nimages, and demonstrate that our method enables various shape manipulation and\ngeneration tasks with these 2D modalities.\n", "date": "2022-07-24", "categories": []}
{"id": "2207.04632", "title": "SkexGen: Autoregressive Generation of CAD Construction Sequences with\n  Disentangled Codebooks", "abstract": "  We present SkexGen, a novel autoregressive generative model for\ncomputer-aided design (CAD) construction sequences containing\nsketch-and-extrude modeling operations. Our model utilizes distinct Transformer\narchitectures to encode topological, geometric, and extrusion variations of\nconstruction sequences into disentangled codebooks. Autoregressive Transformer\ndecoders generate CAD construction sequences sharing certain properties\nspecified by the codebook vectors. Extensive experiments demonstrate that our\ndisentangled codebook representation generates diverse and high-quality CAD\nmodels, enhances user control, and enables efficient exploration of the design\nspace. The code is available at https://samxuxiang.github.io/skexgen.\n", "date": "2022-07-11", "categories": []}
{"id": "2206.12675", "title": "Learning to Infer 3D Shape Programs with Differentiable Renderer", "abstract": "  Given everyday artifacts, such as tables and chairs, humans recognize\nhigh-level regularities within them, such as the symmetries of a table, the\nrepetition of its legs, while possessing low-level priors of their geometries,\ne.g., surfaces are smooth and edges are sharp. This kind of knowledge\nconstitutes an important part of human perceptual understanding and reasoning.\nRepresentations of and how to reason in such knowledge, and the acquisition\nthereof, are still open questions in artificial intelligence (AI) and cognitive\nscience. Building on the previous proposal of the \\emph{3D shape programs}\nrepresentation alone with the accompanying neural generator and executor from\n\\citet{tian2019learning}, we propose an analytical yet differentiable executor\nthat is more faithful and controllable in interpreting shape programs\n(particularly in extrapolation) and more sample efficient (requires no\ntraining). These facilitate the generator's learning when ground truth programs\nare not available, and should be especially useful when new shape-program\ncomponents are enrolled either by human designers or -- in the context of\nlibrary learning -- algorithms themselves. Preliminary experiments on using it\nfor adaptation illustrate the aforesaid advantages of the proposed module,\nencouraging similar methods being explored in building machines that learn to\nreason with the kind of knowledge described above, and even learn this\nknowledge itself.\n", "date": "2022-06-25", "categories": []}
{"id": "2206.12055", "title": "SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation", "abstract": "  We present a StyleGAN2-based deep learning approach for 3D shape generation,\ncalled SDF-StyleGAN, with the aim of reducing visual and geometric\ndissimilarity between generated shapes and a shape collection. We extend\nStyleGAN2 to 3D generation and utilize the implicit signed distance function\n(SDF) as the 3D shape representation, and introduce two novel global and local\nshape discriminators that distinguish real and fake SDF values and gradients to\nsignificantly improve shape geometry and visual quality. We further complement\nthe evaluation metrics of 3D generative models with the shading-image-based\nFr\\'echet inception distance (FID) scores to better assess visual quality and\nshape distribution of the generated shapes. Experiments on shape generation\ndemonstrate the superior performance of SDF-StyleGAN over the state-of-the-art.\nWe further demonstrate the efficacy of SDF-StyleGAN in various tasks based on\nGAN inversion, including shape reconstruction, shape completion from partial\npoint clouds, single-view image-based shape generation, and shape style\nediting. Extensive ablation studies justify the efficacy of our framework\ndesign. Our code and trained models are available at\nhttps://github.com/Zhengxinyang/SDF-StyleGAN.\n", "date": "2022-06-24", "categories": []}
{"id": "2206.04942", "title": "Neural Template: Topology-aware Reconstruction and Disentangled\n  Generation of 3D Meshes", "abstract": "  This paper introduces a novel framework called DTNet for 3D mesh\nreconstruction and generation via Disentangled Topology. Beyond previous works,\nwe learn a topology-aware neural template specific to each input then deform\nthe template to reconstruct a detailed mesh while preserving the learned\ntopology. One key insight is to decouple the complex mesh reconstruction into\ntwo sub-tasks: topology formulation and shape deformation. Thanks to the\ndecoupling, DT-Net implicitly learns a disentangled representation for the\ntopology and shape in the latent space. Hence, it can enable novel disentangled\ncontrols for supporting various shape generation applications, e.g., remix the\ntopologies of 3D objects, that are not achievable by previous reconstruction\nworks. Extensive experimental results demonstrate that our method is able to\nproduce high-quality meshes, particularly with diverse topologies, as compared\nwith the state-of-the-art methods.\n", "date": "2022-06-10", "categories": []}
{"id": "2606.23489", "title": "MeshFlow: Mesh Generation with Equivariant Flow Matching", "abstract": "Meshes are among the most common 3D scene representations, but directly generating meshes is challenging because the representation contains important symmetries, including permutation invariance of faces and vertices. MeshFlow learns to generate triangle meshes directly as triangle soups, avoiding the need to serialize meshes into long autoregressive sequences. We adopt equivariant optimal-transport flow matching models that respect the key symmetries of triangle soups: arbitrary permutations of faces and permutations of the vertices within each face.\n  Toward this goal, we propose a simple yet effective modification to the Diffusion Transformer architecture, resulting in a scalable network capable of modeling a velocity field while maintaining the desired equivariance. We further introduce an optimal-transport-based training objective that improves convergence by eliminating supervision signals that violate these symmetries. MeshFlow achieves mesh quality comparable to state-of-the-art autoregressive mesh generators while providing about an 18$\\times$ speedup during inference. Project page is at https://qiisun.github.io/MeshFlow/.", "date": "2026-06-22", "categories": []}
{"id": "2606.23514", "title": "Arbor: Explicit Geometric Conditioning for Controllable 3D Asset Generation", "abstract": "Text and image conditioned 3D models now generate convincing assets, but they still offer little direct control over the space an object should occupy or avoid. In authoring, this spatial intent is often known before generation starts. A chair should fit a seating envelope, a prop should leave clearance for motion, or a part should expose a contact surface. Prompts and image views are poor carriers for such constraints, requiring the need for an explicit control interface.\n  We present Arbor, a trainable attachment for text conditioned latent 3D generation. Arbor introduces constraint meshes as a native 3D control interface. The interface uses hull regions where geometry should exist, avoidance regions that should remain empty, and touch regions the object should contact. Unlike completion or whole object scaffold control, these meshes are not target evidence. They are local typed requirements and can include regions where no surface should appear. Arbor keeps this signal as geometry by converting constraint meshes into tokens and learning a routed attachment inside a frozen denoiser. Each latent region can therefore receive the part of the constraint that matters for its spatial location.\n  We evaluate Arbor on automatic and artist curated control benchmarks with hull, avoidance, and touch constraints, and compare the metric trends to a user preference study. Even without dedicated compliance losses, Arbor improves constraint obedience while preserving object quality and variation under fixed constraints.", "date": "2026-06-22", "categories": []}
{"id": "2606.24138", "title": "Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image", "abstract": "Generating explicit 3D city assets from a single satellite image is important for digital twins, urban simulation, and geospatial intelligence. Unlike satellite-to-street-view synthesis, the task requires a reusable textured mesh with plausible geometry and controllable appearance rather than a 3D proxy optimized only for rendering a small set of images or videos. The ICCV Sat2City framework made a first step by conditioning cascaded sparse-voxel latent diffusion on satellite-derived height maps, but its appearance was random, its training data were synthetic, and its task-specific VAE did not scale well to noisy real-world reconstructions. We present Sat2City v2, a journal extension that adapts a pretrained native structured-latent 3D foundation model to weakly aligned satellite images and textured meshes. We build a real-world dataset with 16,241 satellite-mesh pairs across 24 regions in 9 cities. Instead of learning a 3D representation from noisy city meshes, Sat2City v2 encodes each mesh into a pretrained native 3D latent space, fine-tunes a satellite-conditioned geometry flow, and uses the decoded shape to anchor satellite-conditioned texturing. This retains Sat2City's geometry-to-appearance cascade while enabling appearance-controllable generation from the satellite input. Experiments on metric-scale DSM reconstruction and generative city-asset benchmarks for geometry and appearance show that Sat2City v2 achieves the best overall performance among evaluated baselines. Overall, Sat2City v2 advances satellite-to-city generation from rendering-oriented 3D proxies to explicit textured mesh assets, supported by, to the best of our knowledge, the first documented satellite-mesh paired dataset collected from matched geographic crops for this asset-level task. Project page: https://ai4city-hkust.github.io/Sat2City-v2/", "date": "2026-06-23", "categories": []}
{"id": "2606.24206", "title": "Inclusive Interactive Collisions for Multi-View Consistent Compositional 3D Generation", "abstract": "Recent breakthroughs in 3D generation have advanced notably with the development of text-to-image diffusion model. However, existing methods remain two practical challenges: (1) They primarily generate single 3D object, but struggle to generate multi-object compositional 3D assets due to the lack of the modeling for Gaussian primitives in reasonable interactions. (2) They often suffer from cross-view inconsistency during 3D optimization, as Score Distillation Sampling inherently performs on each single view, inevitably resulting in cross-view hallucinations. To solve above issues, we propose I2C-3D, a novel optimization-based method to generate multi-view consistent compositional 3D assets with reasonable interactions. Specifically, we propose an Inclusive Interactive Collisions strategy to guide Gaussian primitives appearing in reasonable interaction regions naturally, thereby ensuring objects in the compositional scene interact in a physically plausible and visually coherent way. Additionally, to enhance multi-view consistency, Multi-View Adaptive Score Distillation Sampling is devised to distill multi-view consistency prior and layout prior from pre-trained diffusion model by modulating attention map of instance token and spatial token across viewpoints. Benefiting from above elaborate designs, I2C-3D not only generates high-fidelity multi-view consistent compositional 3D assets but also supports 3D editing flexibly, facilitating complex scene generation. Extensive experiments demonstrate our I2C-3D outperforms existing methods in generation quality and multi-view consistency.", "date": "2026-06-23", "categories": []}
{"id": "2606.24257", "title": "3DCarGen: Scalable 3D Car Generation via 3D-consistent Multi-view Synthesis", "abstract": "High-quality 3D vehicle assets are essential for autonomous driving simulation. Although multi-view diffusion-based paradigms enable controllable single-image reconstruction, they typically produce limited viewpoints and exhibit cross-view geometric inconsistencies, thereby reducing reconstruction fidelity in real-world scenarios. In this work, we introduce 3DCarGen, a scalable single-view 3D car generation framework designed for real-world images by synthesizing an arbitrary number of 3D-consistent multi-view images. Specifically, given a single image as input, we first synthesize a set of images from fixed viewpoints. These images are then fed into a feed-forward reconstruction model, resulting in a coarse 3D representation based on 3D Gaussian Splatting. Conditioned on this explicit 3D prior, our multi-view diffusion model generates 3D-consistent images from arbitrary camera viewpoints. We further extend a fast mesh reconstruction algorithm by incorporating color-normal joint optimization to recover detailed and coherent 3D vehicle models from the synthesized dense views. Extensive experiments on synthetic and real-world datasets demonstrate that our approach achieves robust geometric consistency and reconstruction fidelity compared to existing methods. Project page: https://honglixiao.github.io/3dcargen.github.io/.", "date": "2026-06-23", "categories": []}
{"id": "2606.24301", "title": "MM-TRELLIS: Point-Cloud Guided Multi-Modal 3D Vehicle Generation in Autonomous Driving", "abstract": "Recovering realistic 3D vehicle models from autonomous driving scenes is crucial for synthesizing training data and building simulation environment. However, most existing vehicle generation methods fail to fully exploit multimodal sensors i.e. multi-view images and LiDAR point clouds) and rely on neural rendering based reconstruction, leading to low-quality mesh. Recently, native 3D generative models have made significant progress, yet they are not built for arbitrary multi-view inputs and often struggle with in-the-wild driving images. In this work, we present MM-TRELLIS, a multi-modal version of TRELLIS for in-the-wild 3D vehicle generation that integrates LiDAR and image sensors from autonomous driving datasets into native 3D generative models. Specifically, multi-view images are cycled as conditioning inputs, while LiDAR point clouds provide test-time guidance to ensure geometric accuracy and cross-view consistency. During denoising, we first align the guidance point cloud with the model priors, then enforce consistency between the generated geometry and the guidance point cloud. Finally, we introduce a voxel filtering strategy based on the opacity of 3D Gaussian Splatting to suppress floaters and produce clean meshes. Comprehensive experiments on Waymo dataset demonstrate our method outperforms existing methods in high-fidelity 3D vehicle generation. Code is available at https://github.com/HongliXiao/MM-TRELLIS.", "date": "2026-06-23", "categories": []}
{"id": "2606.24874", "title": "FLUX3D: High-Fidelity 3D Gaussian Generation with Diffusion-Aligned Sparse Representation", "abstract": "Sparse voxel representation has emerged as a scalable foundation for image-to-3D Gaussian Splatting (3DGS) generation, yet current methods struggle to preserve high-frequency visual details of input images due to two structural bottlenecks. First, they adopt discriminative 2D features optimized for semantic abstraction to construct sparse voxel latents, which suppress reconstructive cues and induce a representation bottleneck. Second, in the generation stage, standard diffusion transformers lack effective mechanisms to align dense 2D image tokens with sparse 3D voxel latents, resulting in a cross-modal correspondence bottleneck. To address these issues, we propose FLUX3D, a scalable image-to-3DGS framework that boosts both representation learning and cross-modal alignment during generation. We first revisit 2D feature selection for sparse-voxel-based 3D representation learning, propose Diffusion-Aligned Structured Latents (DA-SLAT) and couple it with a decoder-only architecture to improve 3DGS reconstruction fidelity. We also design a sparse-structure-aware diffusion framework, which integrates the Sparse-structure Multimodal Diffusion Transformer (SMDiT) and Modal-Aware Rotary Positional Embedding (MARoPE) to achieve geometry-agnostic 2D-3D alignment. Extensive benchmark experiments demonstrate that FLUX3D yields substantial improvements in appearance fidelity and significantly outperforms all state-of-the-art (SOTA) methods in generating high-quality 3DGS assets.", "date": "2026-06-23", "categories": []}
{"id": "2606.25300", "title": "HiFiVe: High-Fidelity Vehicle Generation Leveraging Auto-Regressive 2D Generative Priors", "abstract": "Existing 3D vehicle generation methods often suffer from low geometric fidelity and blurry textures, hindering their downstream applications. While recent works adopt multi-view diffusion models for high-fidelity texture, they are often constrained by fixed viewpoints, limited resolution, and a reliance on costly fine-tuning to achieve cross-view consistency. In this paper, we propose HiFiVe, a training-free framework for high-fidelity vehicle modeling through joint texture and geometry enhancement by imposing 3D geometric constraints to anchor 2D generative priors. Specifically, we propose an auto-regressive texture refinement pipeline that progressively synthesizes high-resolution textures from arbitrary viewpoints. To ensure cross-view consistency, the coarse geometry serves as a synchronization prior, conditioning each generation step on previously synthesized frames via depth-based warping and multi-view texture fusion. Moreover, the inherent symmetry of vehicles is exploited to mitigate error accumulation. Finally, high-frequency surface details are recovered by refining the mesh geometry using normal maps estimated from the enhanced textures. Extensive experiments on synthetic and real-world vehicle datasets demonstrate that our method significantly improves both geometric detail and texture quality compared to state-of-the-art baselines. Project page: https://honglixiao.github.io/hifive.github.io/.", "date": "2026-06-24", "categories": []}
{"id": "2606.30673", "title": "PolyFlow: Continuous Topology Embedding Flow Matching for Artist-style Mesh Generation", "abstract": "Autoregressive Transformers dominate high-quality mesh generation by producing artist-worthy topologies, yet their inherent sequential decoding induces substantial computational overhead, falling orders of magnitude slower than parallel generative models. On the other hand, while continuous diffusion and flow-matching methods support efficient parallel synthesis across a variety of domains, they cannot be directly applied to meshes: mesh connectivity is inherently discrete and incompatible with standard continuous noise injection and denoising operations. To resolve this fundamental incompatibility, we introduce a compact topology embedder that projects discrete mesh vertex positions and normals into continuous per-vertex embeddings, where the original discrete adjacency information can be faithfully recovered via spacetime distance thresholding. After pretraining and freezing this embedder, any raw mesh can be fully converted into a continuous per-vertex state space unifying position, normal, and implicit topological attributes. Built upon this novel continuous mesh representation, we present PolyFlow, a Transformer-based flow-matching framework that achieves fully parallel vertex state denoising conditioned on extracted point-cloud features. During inference, our model completes generation rapidly via an ODE solver, and supports explicit, precise control over output mesh resolution by directly specifying the target vertex count. Extensive evaluations on the Toys4K benchmark demonstrate that PolyFlow surpasses state-of-the-art autoregressive baselines in both Chamfer Distance and Hausdorff Distance.", "date": "2026-06-25", "categories": []}
{"id": "2606.27923", "title": "Home3D 1.0: A High-Fidelity Image-to-3D Asset Generation System for Interior Design", "abstract": "We present Home3D 1.0, a modular image-to-3D generation system that produces high-quality 3D assets from a single reference image, targeting interior design and e-commerce applications. Given a photograph of a furniture or decor item, the system outputs a mesh with physically-based rendering (PBR) materials, and the mesh can be decomposed into material-specific components. The pipeline is organized into four tightly coupled modules: Geometry reconstructs a watertight mesh through latent SDF modelling with a geometry VAE and a coarse-to-fine flow-matching DiT; Texture predicts multiview albedo observations, reprojects them onto the mesh, and completes unseen surface regions with a 3D texture field; Material uses MatWeaver to obtain component masks through video-based segmentation and UV-space voting, then retrieves and bakes PBR maps from a curated material library through hierarchical multi-modal matching; and Parts generates material-editable semantic part meshes with a PartVAE and PartDiT, decoding multi-head part-specific SDF fields in one pass. Each module is evaluated independently with dedicated metrics, highlighting both the current system capability and the remaining gaps toward broader deployment.", "date": "2026-06-26", "categories": []}
{"id": "2606.29964", "title": "Variance Reduction on the Camera Axis: Multi-View Score Distillation for 3D", "abstract": "Score distillation turns a pretrained 2D diffusion model into a 3D generator, but the per-step gradient is estimated from a single randomly chosen view: it is high-variance and blind to global shape consistency. Prior work addresses this by retraining the diffusion prior on multi-view data; this improves consistency but makes the sampling contribution inseparable from prior quality. We instead isolate the sampling axis. The per-step gradient is one noisy sample of an expectation over views; aggregating K samples per step at a fixed total UNet budget reduces variance without touching the prior. We introduce Multi-View Aggregated Score Distillation (MV-SDI), which aggregates gradients from K views per step via gradient accumulation, keeping peak memory unchanged and the 2D prior frozen, and draws views as antithetic antipodal pairs, a prior-independent geometric property, for balanced angular coverage. At a fixed 10,000-UNet-call budget, K=2 raises CLIP R-Precision from 74.8% to 83.8% and CLIP score from 0.297 to 0.312, with consistent gains on HPSv2 and ImageReward and a 0.0% divergence rate on the 43-prompt benchmark; optimization steps halve as a consequence. K=4 gives a fourfold step reduction at R-Precision 86.9% and CLIP 0.307, still well above the single-view baseline on every alignment metric. MV-SDI is compatible with gradient-based score-distillation pipelines, including Score Distillation via Inversion, and requires no retraining and no multi-view data.", "date": "2026-06-29", "categories": []}
{"id": "2606.31293", "title": "Deep Spectral Models for Robust Dental Shape Generation", "abstract": "Accurate modeling of dental crown morphology is fundamental for diagnosis, orthodontic planning, and computer-aided restoration design. However, datasets suitable for training such models are typically limited in size. We present ToothForge, a deep spectral generative framework that models dental crown geometries from compact, intrinsic representations. By operating in the spectral domain, ToothForge learns a latent manifold of 3D tooth shapes through synchronized spectral embeddings, ensuring consistent modeling across samples with varying connectivity. Spectral synchronization mitigates the instability of Laplace-Beltrami eigenbases and enables efficient learning in a low-dimensional space. The framework is thoroughly evaluated through robustness analysis, ablation studies, and benchmarking against PCA-based statistical shape models and point-based generative frameworks. Results show that synchronized spectral modeling achieves reconstruction and generative performance comparable to or exceeding spatial approaches, while maintaining compactness and geometric interpretability. Together, the compact synchronized coefficients and low-dimensional learning space make the framework particularly suitable for limited datasets, as often encountered in dental and medical domains, and applicable in real-world scenarios where guaranteeing consistent connectivity across shapes from various clinics is unrealistic.", "date": "2026-06-30", "categories": []}
{"id": "2606.31777", "title": "Mesh BDF: Barycentric Dominance Field for 3D Native Mesh Generation", "abstract": "Autoregressive (AR) modeling has recently achieved remarkable progress in native 3D mesh generation, largely due to its natural ability to handle variable-length, discrete data structures. However, the inherent constraints of the AR paradigm severely restrict the generated meshes, leading to limited face counts, bounded vertex resolutions, and difficulties in supporting textures. To overcome these bottlenecks, we propose the Barycentric Dominance Field (BDF), a continuous representation defined on triangular mesh surfaces that elegantly encodes vertex topological connectivity. BDF bridges the fundamental gap between discrete mesh topology and continuous diffusion-based generative modeling by transforming connectivity into a continuous surface signal. As an intrinsic mesh property, BDF shares strong similarities with texture maps, enabling its seamless integration into existing 3D diffusion pipelines without requiring architectural modifications. Extensive experiments demonstrate that BDF empowers diffusion models to generate native meshes with significantly higher quality, greater scalability, and stronger robustness compared to state-of-the-art autoregressive methods.", "date": "2026-06-30", "categories": []}