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+![](images/d9493e5344a09471e7e977fb9637e40893ef78aa9f922bbb67169fe260270b31.jpg)
+
+# MagicMotion: Controllable Video Generation with Dense-to-Sparse Trajectory Guidance
+
+Quanhao Li $^{1,2*}$
+
+Zhen Xing $^{1,2*}$
+
+Rui Wang $^{1,2}$
+
+Hui Zhang $^{1,2}$
+
+Qi Dai<sup>3</sup>
+
+Zuxuan Wu $^{1,2\dagger}$
+
+<sup>1</sup>Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University
+
+$^{2}$ Shanghai Collaborative Innovation Center of Intelligent Visual Computing
+
+<sup>3</sup> Microsoft Research Asia
+
+https://quanhaol.github.io/magicmotion-site/
+
+![](images/a32a487e53822fdc24d9b084a1d24633b01dea4a261219956023352c401e2207.jpg)  
+Figure 1. Example videos generated by MagicMotion. MagicMotion consists of three stages, each supporting a different level of control from dense to sparse: mask, box, and sparse box. Given an input image and any form of trajectory, MagicMotion can generate high-quality videos, animating objects in the image to move along the user-specified path.
+
+# Abstract
+
+Recent advances in video generation have led to remarkable improvements in visual quality and temporal coherence. Upon this, trajectory-controllable video generation has emerged to enable precise object motion control through explicitly defined spatial paths. However, existing methods struggle with complex object movements and multi-object motion control, resulting in imprecise trajectory adherence, poor object consistency, and compromised
+
+visual quality. Furthermore, these methods only support trajectory control in a single format, limiting their applicability in diverse scenarios. Additionally, there is no publicly available dataset or benchmark specifically tailored for trajectory-controllable video generation, hindering robust training and systematic evaluation. To address these challenges, we introduce MagicMotion, a novel image-to-video generation framework that enables trajectory control through three levels of conditions from dense to sparse: masks, bounding boxes, and sparse boxes. Given an input image and trajectories, MagicMotion seamlessly animates objects along defined trajectories while maintaining object consistency and visual quality. Furthermore,
+
+we present MagicData, a large-scale trajectory-controlled video dataset, along with an automated pipeline for annotation and filtering. We also introduce MagicBench, a comprehensive benchmark that assesses both video quality and trajectory control accuracy across different numbers of objects. Extensive experiments demonstrate that MagicMotion outperforms previous methods across various metrics.
+
+# 1. Introduction
+
+With the rapid development of diffusion models, video generation has witnessed significant progress in recent years. In contrast to images, videos [53, 63, 64, 73] capture dynamic temporal changes and offer a richer representation of semantic content. Earlier video generation approaches, such as AnimateDiff [16] and SVD [3], primarily rely on the UNet [46] structure, which results in videos with limited length and quality. Sora [4] has demonstrated the powerful capabilities of the DiT [37] architecture in text-to-video (T2V) generation. Following this breakthrough, subsequent models leveraging the DiT architecture [28, 82, 93] have achieved higher quality outputs and extended video duration.
+
+While DiT-based models [28, 82, 93] excel at producing high-quality and longer videos, many text-to-video approaches [16, 75, 77] lack precise control over attributes like object movement and camera motion [16, 75, 82]. Fine-grained trajectory-controllable video generation emerges as a solution, which is especially critical for generating controllable videos in real-world scenarios.
+
+Previous trajectory-controllable video generation methods can be categorized based on the type of control signals they use. These include points-based control [14, 59, 66, 94], optical flow-based control [5, 30, 48, 84, 91], bounding boxes-based control [22, 35, 39, 60, 67, 69, 81], masks-based control [83], and 3D trajectories-based control [13, 15, 62]. However, these methods exhibit several limitations. First, the trajectory control conditions used by these methods are unitary. Each method only supports a single type of control signal. However, sparse trajectories (e.g., points and optical flow) result in imprecise control over object shape and size, while dense trajectories (e.g., masks and 3D trajectories) are challenging for users to provide. Second, a publicly available large-scale dataset for trajectory-controllable video generation is lacking. Existing VOS (Video Object Segmentation) datasets suffer from short video lengths [33, 34, 80], small scales [9, 10, 20, 38], or few foreground objects [12]. Third, a unified benchmark for evaluating different methods is absent. Besides, previous work focuses solely on video quality and trajectory accuracy while neglecting the impact of the number of moving objects. We argue that controlling fewer or more objects presents different challenges, making it essential to include
+
+this factor in the evaluation metrics.
+
+To address these issues, we propose MagicMotion, a controllable video generation framework with dense-to-sparse trajectory guidance. To inject trajectory conditions into the generation process, we utilize an architecture similar to ControlNet [89] called Trajectory ControlNet to encode the trajectory information, which is later added to the original DiT model through a zero-initialized convolution layer. MagicMotion supports three types of trajectory conditions: masks, boxes, and sparse boxes using a progressive training strategy. Experiments show that the model can leverage the knowledge learned in the previous stage to achieve better performance than training from scratch. Additionally, we propose a novel latent segment loss that helps the video generation model better understand the fine-grained shape of objects with minimal computation.
+
+We also construct MagicData, a high-quality public dataset comprising 23K video samples, each annotated with a <video, text, trajectory> triplet. We design a data pipeline that uses a large language model [52] to extract the main moving objects in the video, and Segment Anything Model (SAM2) [43, 44] to annotate the segmentation masks and bounding boxes of these objects. Furthermore, we introduce MagicBench, a large-scale comprehensive trajectory-controllable video generation benchmark. It categorizes all videos into 6 classes based on the number of foreground objects and evaluates models separately for each category in terms of both video generation quality and trajectory control accuracy.
+
+In conclusion, the main contributions of our work are summarized as follows:
+
+- We present MagicMotion, a trajectory-controllable image-to-video generation model that supports three types of control signals: masks, boxes, and sparse boxes.  
+- We introduce a data curation and filtering mechanism, and construct MagicData, the first public dataset for trajectory-controlled video generation.  
+- We propose MagicBench, a comprehensive benchmark for evaluating trajectory-controllable video generation models for both video quality and trajectory control accuracy across different numbers of controlled objects.
+
+# 2. Related Works
+
+Video Diffusion Models Diffusion models [18, 31, 49, 50] have made great progress in image generation [8, 36, 40, 41, 45, 47, 61, 85-88, 90], which has led to the rapid development of video generation [3, 11, 16, 19, 21, 25, 28, 51, 54-57, 70, 72, 74, 76, 77, 82, 93]. VDM [19] is the first to apply diffusion models to video generation. Early works likeAnimateDiff [16] and SimDA [75] attempt to insert temporal layers into pretrained T2I model for video generation. Subsequently, VideoCrafter [6] and SVD [3] use large-scale and high-quality data for training, achieving
+
+better performance. However, these methods have trouble on generating long videos with high quality, mainly due to the inherent limitations of the UNet architecture. The emergence of Sora [4] is a significant success, demonstrating the potential of DiT [37] models to generate high-quality videos with tens of seconds. Recent video generation methods [28, 82, 93] are mainly based on DiT architecture, and have achieved great success in the open-source community. However, these methods rely solely on text or image guidance for video generation, lacking precise control over object or camera trajectory, which is crucial for high-quality video generation.
+
+Trajectory Controllable Video Generation Trajectory Controllable Video Generation has recently garnered significant attention for its ability to precisely control object and camera trajectories during video synthesis. Previous methods [30, 48, 84, 91] integrate optical flow maps into video generation through a trajectory encoder. Recent works [66, 94] suggest using point maps as a form of guidance. MotionCtrl [66] processes point maps with a Gaussian filter and employs trainable encoders to encode object trajectories. Trackgo [94] represents objects using a few key points and injects this information via an encoder and a custom-designed adapter structure. Other works [22, 35, 39, 60] employ bounding box to control object trajectories. Boximator [60] employs a trainable self-attention layer to fuse box and visual tokens inspired by GLIGEN [29]. Some training-free methods [22, 35, 39] purpose to modify attention layers or initial noised video latents to inject box signals. Additionally, certain methods [13, 15, 59, 62] explore the potential of 3D trajectories to achieve more sophisticated motion control. LeViTor [59] employs keypoint trajectory maps enriched with depth information, while others [13, 15, 62] construct custom 3D trajectories to represent object movements. However, sparse trajectories lead to imprecise control on objects shape and size, while dense trajectories are difficult for users to provide. In contrast, MagicMotion can control both dense and sparse trajectories, providing users with more flexible control over video generation.
+
+# 3. Method
+
+# 3.1. Overview
+
+Our work mainly focuses on trajectory-controllable video generation. Given an input image  $I \in R^{H \times W \times 3}$ , and several trajectory maps  $C \in R^{T \times H \times W \times 3}$ , the model can generate a video  $V \in R^{T \times H \times W \times 3}$  in line with the provided trajectories, where  $T$  denotes the length of generated video. In the following sections, we first provide a detailed explanation of our model architecture in Section 3.2. Next, we outline our progressive training procedure in Section 3.3. In Section 3.4, we introduce the Latent Segmentation Loss and
+
+demonstrate how it enhances the model capabilities on fine-grained object shape. We then describe our dataset curation and filtering pipeline in Section 3.5. Finally, we present an in-depth overview of MagicBench in Section 3.6.
+
+# 3.2. Model Architecture
+
+Base I2V generation model We utilize CogVideoX-5B-I2V [82] and Wan2.1 1.3B [58] as our base image to video model. The models are built upon a DiT (Diffusion Transformer) architecture, incorporating 3D-Full Attention to generate high-quality videos. As shown in Fig. 2, the model takes an input image  $I \in R^{H \times W \times 3}$  and a corresponding video  $V \in R^{T \times H \times W \times 3}$  and encodes them into latent representations  $Z_{image}, Z_{video} \in R^{\frac{T}{4} \times \frac{H}{8} \times \frac{W}{8} \times 16}$  using a pretrained 3D VAE [26]. Later,  $Z_{image}$  is zeropadded to  $T$  frames and concatenated with a noised version of  $Z_{video}$  and then fed into the Diffusion Transformer, where a series of Transformer blocks iteratively denoise it over a predefined number of steps. Finally, the denoised latent is decoded by a 3D VAE decoder to get the output video  $V_{out} \in R^{T \times H \times W \times 3}$ .
+
+Trajectory ControlNet To ensure that the generated video follows the motion patterns given by the input trajectory maps  $C \in R^{T \times H \times W \times 3}$ , we adopt a design similar to ControlNet [89] to inject trajectory condition. As shown in Fig. 2, we employ the 3D VAE encoder to encode the trajectory maps into  $Z_{\text{trajectory}} \in R^{\frac{T}{4} \times \frac{H}{8} \times \frac{W}{8} \times 16}$ , which is then concatenated with the encoded video  $Z_{\text{video}}$  and serves as input to Trajectory ControlNet. Specifically, Trajectory ControlNet is constructed with a trainable copy of all pre-trained DiT blocks to encode the user-provided trajectory information. The output of each Trajectory ControlNet block is then processed through a zero-initialized convolution layer and added to the corresponding DiT block in the base model to provide trajectory guidance.
+
+# 3.3. Dense-to-Sparse Training Procedure
+
+Dense trajectory conditions, such as segmentation masks, offer more precise control than sparse conditions like bounding boxes but are less user-friendly. To address this, MagicMotion employs a progressive training procedure, where each stage initializes its model with the weights from the previous stage. This enables three types of trajectory control ranging from dense to sparse. We found that this progressive training strategy helps the model achieve better performance compared to training from scratch with sparse conditions. Specifically, we adopt the following trajectory conditions across stages: stage1 uses segmentation masks, stage2 uses bounding boxes, and stage3 uses sparse bounding boxes, where fewer than 10 frames have box annotations. Additionally, we always set the first frame of the trajectory condition as a segmentation mask to specify the foreground objects that should be moving.
+
+![](images/ce03cc00e790838b861392b345e3ba56afae65187bba3e8afbaf57d69834affc.jpg)  
+Figure 2. Overview of MagicMotion Architecture (text prompt and encoder are omitted for simplicity). MagicMotion employs a pretrained 3D VAE to encode the input trajectory, first-frame image, and training video into latent space. It has two separate branches: the video branch processes video and image tokens, and the trajectory branch uses Trajectory ControlNet to fuse trajectory and image tokens, which is later integrated to the video branch through a zero-initialized convolution layer. Besides, diffusion features from DiT blocks are concatenated and processed by a trainable segment head to predict latent segmentation masks, which contribute to our latent segment loss.
+
+Our model uses velocity prediction following [82]. Let  $x_0$  be the initial video latents,  $\epsilon$  be the gaussian noise,  $x_t = \sqrt{\alpha_t} * x_0 + \sqrt{1 - \alpha_t} * \epsilon$  be the noised video latents, and  $v_\theta$  be the model output. The diffusion loss can be written as:
+
+$$
+\mathcal {L} _ {\text {d i f f u s i o n}} = \mathbb {E} _ {t, \epsilon \sim \mathcal {N} (0, I), x _ {0}} \left[ \left\| x _ {0} - \left(\sqrt {\alpha_ {t}} x _ {t} - \sqrt {1 - \alpha_ {t}} v _ {\theta}\right) \right\| _ {2} ^ {2} \right] \tag {1}
+$$
+
+# 3.4. Latent Segmentation Loss
+
+Bounding box-based trajectory is able to control an object's position and size but lacks fine-grained shape perception. To address this, we propose Latent Segmentation Loss, which introduces segmentation mask information during model training and enhances the model's ability to perceive fine-grained object shapes.
+
+Previous works [2, 68, 79, 92] have leveraged diffusion generation models for perception tasks, demonstrating that the features extracted by diffusion models contain rich semantic information. However, these models generally operate in the pixel space, which leads to extensive computational time and substantial GPU memory.
+
+To incorporate dense trajectory information while keeping computational costs within a reasonable range, we propose utilizing a lightweight segmentation head to predict segmentation masks directly in the latent space, eliminating the need for decoding operations. Specifically, our segmentation head takes a list of diffusion features  $Z_{\text{feature}} \in R^{\frac{T}{4} * \frac{H}{16} * \frac{W}{16} * 3072}$  from each DiT block, and outputs a latent segmentation mask  $Z_{\text{segment}} \in R^{\frac{T}{4} * \frac{H}{8} * \frac{W}{8} * 16}$ . We use a light-weight architecture inspired by Panoptic FPN [27]. Each diffusion feature first passes through a convolution
+
+layer to extract visual features  $P \in R^{\frac{T}{4} *\frac{H}{16} *\frac{W}{16} *64}$ . The resulting features are then concatenated and processed by another convolution layer followed by an upsampling layer to generate the final latent segmentation mask. We compute the latent segment loss as the Euclidean distance between  $Z_{segment}$  and the ground truth mask trajectory latents  $Z_{mask}$ , which can be written as,
+
+$$
+\mathcal {L} _ {s e g} = \mathbb {E} _ {t, \epsilon \sim \mathcal {N} (0, I), z _ {0}} [ \| Z _ {s e g m e n t} - Z _ {m a s k} \| _ {2} ^ {2} ] \quad (2)
+$$
+
+In practice,  $\mathcal{L}_{seg}$  is only used in stage2 and stage3, providing the model with dense conditions information when trained with sparse conditions. In detail, we set the weight of  $\mathcal{L}_{seg}$  as 0.5, and the original diffusion loss as 1. In total, our final loss function can be written as,
+
+$$
+\mathcal {L} = \mathcal {L} _ {\text {d i f f u s i o n}} + \lambda * \mathcal {L} _ {\text {s e g}} \tag {3}
+$$
+
+where  $\lambda$  is set to 0 in stage1, and 0.5 in stage2 and stage3.
+
+# 3.5. Data Pipeline
+
+Trajectory controllable video generation requires a video dataset with trajectory annotations. However, existing large-scale video datasets [1, 7, 24] only provide text annotations and lack trajectory data. Moreover, almost all previous works [30, 66, 84, 91, 94] use privately curated datasets, which are not publicly available.
+
+We present a comprehensive and general data pipeline for generating high-quality video data with both dense (mask) and sparse (bounding box) annotations. As shown in Fig. 3, the pipeline consists of two main stages: the Curation Pipeline and the Filtering Pipeline. The Curation
+
+![](images/0f6ad988d2ad9a862d0c9f1a3099da9176ce69f632148a1fc4e59c98862af247.jpg)  
+Figure 3. Overview of the Dataset Pipeline. The Curation Pipeline is used to construct trajectory annotations, while the Filtering Pipeline filters out unsuitable videos for training.
+
+Pipeline is responsible for constructing trajectory information from a video-text dataset, while the Filtering Pipeline ensures that unsuitable videos are removed before training.
+
+Curation Pipeline. We begin our dataset curation process with Pexels [23], a large-scale video-text dataset containing 396K video clips with text annotations. It encompasses videos featuring diverse subjects, various scenes, and a wide range of movements. We utilize Llama3.1 [52] to extract the foreground moving objects from the textual annotations of each video. As shown in Fig. 3, we input the video's caption into the language model and prompt it to identify the main foreground objects mentioned in the sentence. If the model determines that the sentence does not contain any foreground objects, it simply returns "empty" and such videos are filtered out. Next, we utilize GroundedSAM2 [43, 44], a grounded segmentation model that takes a video along with its main objects as input and generates segmentation masks for each primary object. Each object is consistently annotated with a unique color. Finally, bounding boxes are extracted from each segmentation mask using the coordinates of the top-left and bottom-right corners to draw the corresponding boxes. The color of the bounding box for each object remains consistent with its masks.
+
+Filtering Pipeline. Many videos contain only static scenes, which are not beneficial for training trajectory-controlled video generation models. To address this, we use optical flow scores to filter out videos with little motion and dynamics. Specifically, we utilize UniMatch [78] to extract optical flow maps between frames and compute the mean absolute value of these flow maps as the optical flow score, representing the video's motion intensity. However, videos with background movement but static foreground can still have high motion scores. To address this, we further use UniMatch to extract optical flow scores for foreground objects based on segmentation masks and bounding boxes.
+
+Videos with low foreground optical flow scores are filtered out, ensuring MagicData includes only videos with moving foreground objects.
+
+The trajectory annotations generated by the curation pipeline require further refinement. As shown in Fig. 3, some videos contain too many foreground objects annotations, or their sizes may be too large or too small. To address this, we regulate these factors within a reasonable range and filter out videos that fall outside the acceptable range.
+
+Specifically, based on extensive manual evaluation, we empirically set the optical flow score threshold to 2.0, limit the number of foreground object annotations from 1 to 3, and constrain the annotated area ratio to a range of 0.008 to 0.83. The whole data curation and filtering pipeline yields us with MagicData, a high quality dataset for trajectory controllable video generation containing 23K videos with both dense and sparse trajectory annotations.
+
+# 3.6. MagicBench
+
+Previous works on trajectory-controlled video generation [30, 35, 48, 65, 71, 91, 94] have primarily been validated on DAVIS (which has a relatively small dataset size), VIPSeg (where the annotated frames per video are insufficient), or privately constructed test sets. Thus there is an urgent need for a large-scale, publicly available benchmark to enable fair comparisons across different models in this field. To merge this gap, we use the data pipeline mentioned in Sec. 3.5 to construct MagicBench, a large-scale open benchmark consisting of 600 videos with corresponding trajectory annotations. MagicBench evaluates not only video quality and trajectory accuracy but also takes the number of controlled objects as a key evaluation factor. Specifically, it is categorized into 6 groups based on the number of controlled objects, ranging from 1 to 5 objects and more than 5 objects, with each category containing 100 high-quality videos.
+
+Metrics. For evaluation metrics, we adopt FVD [42] to assess video quality and FID [17] to evaluate image quality, following [59, 66, 71, 94]. To quantify motion control accuracy, we use Mask_IoU and Box_IoU, which measure the accuracy of masks and bounding boxes, respectively. Specifically, given a generated video  $V_{gen}$ , we use the groundtruth masks of the first frame  $M_{gt}(0)$  as input to SAM2 [43] to predict the masks  $M_{gen}$  of the foreground objects in  $V_{gen}$ . For each foreground object, we compute the Intersection over Union (IoU) between  $M_{gen}(i)$  and groundtruth masks  $M_{gt}(i)$  in each frame, then average these values to obtain Mask_IoU. Similarly, we compute the IoU between the predicted and groundtruth bounding boxes for each foreground object in each frame and take the average as Box_IoU.
+
+<table><tr><td rowspan="2">Method</td><td colspan="4">MagicBench</td><td colspan="4">DAVIS</td></tr><tr><td>FID(↓)</td><td>FVD(↓)</td><td>M_IoU%(↑)</td><td>B_IoU%(↑)</td><td>FID(↓)</td><td>FVD(↓)</td><td>M_IoU%(↑)</td><td>B_IoU%(↑)</td></tr><tr><td>Motion-I2V [48]</td><td>39.31</td><td>354.10</td><td>56.19</td><td>60.66</td><td>94.14</td><td>1558.32</td><td>32.00</td><td>42.61</td></tr><tr><td>ImageConductor [30]</td><td>49.95</td><td>331.76</td><td>51.76</td><td>52.90</td><td>91.71</td><td>1155.08</td><td>34.39</td><td>43.41</td></tr><tr><td>DragAnything [71]</td><td>31.36</td><td>253.40</td><td>66.30</td><td>70.85</td><td>70.70</td><td>1166.22</td><td>40.13</td><td>53.60</td></tr><tr><td>LeViTor [59]</td><td>38.32</td><td>194.53</td><td>39.96</td><td>46.36</td><td>97.98</td><td>922.68</td><td>25.24</td><td>31.42</td></tr><tr><td>DragNUWA [84]</td><td>39.73</td><td>185.52</td><td>66.88</td><td>69.21</td><td>84.61</td><td>1079.89</td><td>41.22</td><td>52.61</td></tr><tr><td>SG-I2V [35]</td><td>32.60</td><td>168.82</td><td>68.78</td><td>74.39</td><td>90.93</td><td>1170.60</td><td>37.36</td><td>50.96</td></tr><tr><td>Tora [91]</td><td>26.27</td><td>245.23</td><td>58.95</td><td>64.03</td><td>51.75</td><td>766.76</td><td>37.98</td><td>50.90</td></tr><tr><td>Ours (Stage1-Wan1.3B)</td><td>17.74</td><td>129.79</td><td>83.79</td><td>80.96</td><td>46.65</td><td>548.56</td><td>42.83</td><td>53.67</td></tr><tr><td>Ours (Stage2-Wan1.3B)</td><td>19.33</td><td>143.07</td><td>68.89</td><td>71.74</td><td>48.33</td><td>662.82</td><td>44.75</td><td>61.88</td></tr><tr><td>Ours (Stage1-CogVideoX)</td><td>15.06</td><td>112.69</td><td>91.57</td><td>87.75</td><td>45.06</td><td>579.94</td><td>81.33</td><td>84.97</td></tr><tr><td>Ours (Stage2-CogVideoX)</td><td>15.17</td><td>107.21</td><td>76.61</td><td>81.45</td><td>50.36</td><td>760.95</td><td>53.94</td><td>72.84</td></tr></table>
+
+![](images/8815955d60a7d3b3bf38a39fe165d589fd5ae5e07f9943664acd638cf0405ef3.jpg)  
+Table 1. Quantitative Comparison results on MagicBench and DAVIS. "M_IoU" and "B_IoU" refer to Mask_IoU and Box_IoU, respectively.  
+Figure 4. Comparison results of different object number on MagicBench. To present the results more clearly, we have negated the FVD and FID scores.
+
+# 4. Experiment
+
+# 4.1. Experiment Settings
+
+Implementation details. We employ CogVideoX 5B [82] and Wan2.1 1.3B [58] as our base image-to-video model, which is trained to generate video based on an input image. Each stage of MagicMotion was trained on MagicData for one epoch. The training process consists of three stages. stage1 trains Trajectory ControlNet from scratch. In stage2, Trajectory ControlNet is further refined using the weights from stage1, while Segment Head is trained from scratch. Finally, in stage3, both Trajectory ControlNet and Segment Head continue training initialized with the weights from stage2. All training experiments were conducted on 4 NVIDIA A100-80G GPUs. We employed AdamW [32] as the optimizer, training with a learning rate of  $1e - 5$  and a batch size of 1 on each GPU. During inference, we set steps to 50, the guidance scale to 6, and the weight of Trajectory ControlNet to 1.0 by default.
+
+Datasets. During training, we use MagicData as our training set. MagicData is annotated with dense to sparse trajec
+
+tory information using the data pipeline described in Section 3.5. It comprises a total of 23K video samples, annotated with text and trajectory. For evaluation, we evaluate all methods on both MagicBench and DAVIS [38], using the comparison metrics illustrated in Sec 3.6.
+
+# 4.2. Comparison with Other Approaches
+
+For thorough and fair comparisons, we compare our methods against 7 public trajectory controllable I2V methods [30, 35, 48, 59, 71, 84, 91]. Quantitative comparison and qualitative comparison results are shown below.
+
+Quantitative comparison. To compare MagicMotion with previous works, we use the first 49 frames of each video from DAVIS and MagicBench as the ground truth video. Since some methods [30, 35, 48, 59, 71, 84] do not support video generation up to 49 frames in length, we uniformly sample N frames from these 49 frames for evaluation, where N represents the video length that each method supports. We leverage the mask and box annotations from these selected frames as trajectory inputs for mask or box-based methods. The center point of each frame's
+
+![](images/eff7090cdb7bb8d523ec884a837abcc3090f10a75e628c84db7316c41025af69.jpg)  
+Figure 5. MagicMotion successfully controls the main objects moving along the provided trajectory, while all other methods exhibit significant defects marked with the orange box.
+
+mask is extracted as input for point or flow-based methods [30, 48, 59, 71, 84, 91].
+
+As shown in Table 1, our method outperforms all previous approaches across all metrics both on MagicBench and DAVIS, demonstrating its ability to produce higher-quality videos and more precise trajectory control. Additionally, we evaluate each method's performance on MagicBench based on the number of controlled objects. As shown in Fig. 4, our method achieves the best results across all object number categories, demonstrating the superiority of our approach.
+
+Qualitative comparison. Qualitative comparison results are shown in Fig. 5, with the input image, prompt and trajectory provided. As shown in Fig. 5, Tora [91] accurately controls the motion trajectory but struggles to maintain the shape of the objects. While DragAnything [71], ImageConductor [30], and MotionI2V [48] have difficulty preserving the consistency of the original subject, resulting in substantial deformation in subsequent frames. Meanwhile, DragNUWA [84], LeviTor [59], and SG-I2V [35] frequently produce artifacts and inconsistencies in fine details. In contrast,
+
+MagicMotion allows moving objects to follow the specified trajectory smoothly while preserving high video quality.
+
+# 4.3. Ablation Studies
+
+In this section, we present ablation studies to validate the effectiveness of our MagicData dataset. Additionally, we demonstrate how our progressive training procedure and latent segment loss enhance the model's understanding of precise object shapes under sparse control conditions, thereby improving trajectory control accuracy.
+
+Ablations on Dataset. To verify the effectiveness of MagicData, we constructed an ablation dataset by combining two public VOS datasets, MeViS [9] and MOSE [10]. For a fair comparison, we trained MagicMotion stage2 for one epoch using either MagicData or the ablation dataset as the training set, both initialized with the same stage1 weights. We then evaluated the models on both MagicBench and DAVIS.
+
+As shown in Table 2, the model trained on MagicData
+
+outperforms the one trained on the ablation dataset across all metrics. Qualitative comparisons are shown in Fig. 6. In this case, we aim to gradually move the boy in the lower right corner to the center of the image. However, not using MagicData results to an unexpected child appears next to the boy. In contrast, the model trained with MagicData performs well, moving the boy along the specified trajectory while maintaining video quality.
+
+<table><tr><td rowspan="2">Method</td><td colspan="4">MagicBench/DAVIS</td></tr><tr><td>FID(↓)</td><td>FVD(↓)</td><td>M_IoU%(↑)</td><td>B_IoU%(↑)</td></tr><tr><td>w/o MagicData</td><td>24.24/50.91</td><td>125.19/768.27</td><td>73.39/50.95</td><td>78.66/70.05</td></tr><tr><td>Ours</td><td>15.17/50.35</td><td>107.21/760.95</td><td>76.61/53.94</td><td>81.45/72.84</td></tr></table>
+
+![](images/156b7312e42b51dfae7c9326a39192478870b973262fed741fc289a40668035f.jpg)  
+Figure 6. Ablation Study on MagicData. Not using MagicData causes the model to generate an unexpected child.
+
+Ablations on Progressive Training Procedure. Progressive Training Procedure allows the model to leverage the weights learned in the previous stage, incorporating dense trajectory control information when trained with sparse trajectory conditions. To validate the effectiveness of this approach, we train the model from scratch for one epoch using bounding boxes as trajectory conditions. We then compare its performance with MagicMotion stage2.
+
+As shown in Table 3, excluding Progressive Training Procedure weakens the model's ability to perceive object shapes, ultimately reducing the accuracy of trajectory control. Qualitative comparisons in Fig. 7 further illustrate these effects, where the model trained without Progressive Training Procedure turns the woman's head entirely into hair.
+
+Ablations on Latent Segment Loss. Latent Segment Loss makes the model predict dense segmentation masks while training with sparse trajectories, enhancing its ability to perceive fine-grained object shapes under sparse conditions. To evaluate the effectiveness of this technique, we train the model from stage1 for one epoch using bounding
+
+Table 2. Ablation Study on MagicData. The model trained with MagicData outperforms the one trained without it across all metrics.  
+
+<table><tr><td rowspan="2">Method</td><td colspan="2">MagicBench</td><td colspan="2">DAVIS</td></tr><tr><td>M_IoU%(↑)</td><td>B_IoU%(↑)</td><td>M_IoU%(↑)</td><td>B_IoU%(↑)</td></tr><tr><td>w/o LSL</td><td>74.65</td><td>78.02</td><td>49.19</td><td>64.19</td></tr><tr><td>w/o PT</td><td>74.61</td><td>79.49</td><td>49.22</td><td>67.34</td></tr><tr><td>Ours</td><td>76.61</td><td>81.45</td><td>53.94</td><td>72.84</td></tr></table>
+
+Table 3. Ablation Study on Progressive Training (PT) and Latent Segment Loss (LSL). Experimental results demonstrate that these techniques enhance the model with better comprehension on fine-grained object shapes.
+
+![](images/27d61328ec27478b85eefd4aedf4e98232949957e706a5b7864f44b56216bddb.jpg)  
+Figure 7. Ablation study on the progressive training procedure. Without it, the generated head shapes become noticeably distorted.
+
+boxes as trajectory conditions and compare its performance with MagicMotion stage2. Table 3 shows that the absence of Latent Segment Loss reduces the model's ability on object shapes, leading to less precise trajectory control.
+
+# 5. Conclusion
+
+In this paper, we proposed MagicMotion, a trajectory-controlled image-to-video generation method that uses a ControlNet-like architecture to integrate trajectory information into the diffusion transformer. We employed a progressive training strategy, allowing MagicMotion to support three levels of trajectory control: dense masks, bounding boxes and sparse boxes. We also utilized Latent Segment Loss to enhance the ability of the model to perceive fine-grained object shapes when only provided with sparse trajectory conditions. Additionally, we presented MagicData, a high-quality annotated dataset for trajectory-controlled video generation, created through a robust data pipeline. Finally, we introduced MagicBench, a large-scale benchmark for evaluating trajectory-controlled video generation. MagicBench not only assessed video quality and trajectory accuracy but also took the number of controlled objects into account. Extensive experiments on both MagicBench and DAVIS demonstrated the superiority of MagicMotion compared to previous works.
+
+Acknowledgement This work was supported in part by the National Natural Science Foundation of China (Grant 62032006 and Grant 62472098).
+
+# References
+
+[1] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, 2021. 4  
+[2] Aditya Bhat, Rupak Bose, Chinedu Innocent Nwoye, and Nicolas Padoy. Simgen: A diffusion-based framework for simultaneous surgical image and segmentation mask generation. arXiv preprint arXiv:2501.09008, 2025. 4  
+[3] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023. 2  
+[4] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh. Video generation models as world simulators. https://openai.com/research/video-generation-models-as-world-simulators, 2024. 2, 3  
+[5] Ryan Burgert, Yuancheng Xu, Wenqi Xian, Oliver Pilarski, Pascal Clausen, Mingming He, Li Ma, Yitong Deng, Lingxiao Li, Mohsen Mousavi, et al. Go-with-the-flow: Motion-controllable video diffusion models using real-time warped noise. In CVPR, 2025. 2  
+[6] Haoxin Chen, Menghan Xia, Yingqing He, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Yaofang Liu, Qifeng Chen, Xintao Wang, et al. Videocrafter1: Open diffusion models for high-quality video generation. arXiv preprint arXiv:2310.19512, 2023. 2  
+[7] Tsai-Shien Chen, Aliaksandr Siarohin, Willi Menapace, Ekaterina Deyneka, Hsiang-wei Chao, Byung Eun Jeon, Yuwei Fang, Hsin-Ying Lee, Jian Ren, Ming-Hsuan Yang, et al. Panda-70m: Captioning 70m videos with multiple cross-modality teachers. In CVPR, 2024. 4  
+[8] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. NeurIPS, 2021. 2  
+[9] Henghui Ding, Chang Liu, Shuting He, Xudong Jiang, and Chen Change Loy. MeViS: A large-scale benchmark for video segmentation with motion expressions. In ICCV, 2023. 2, 7  
+[10] Henghui Ding, Chang Liu, Shuting He, Xudong Jiang, Philip HS Torr, and Song Bai. MOSE: A new dataset for video object segmentation in complex scenes. In ICCV, 2023. 2, 7  
+[11] Zhiying Du and Zhen Xing. Challenge report: Track 2 of multimodal perception and comprehension of corner cases in autonomous driving. In ECCV Workshop, 2024. 2  
+[12] Heng Fan, Hexin Bai, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Harshit, Mingzhen Huang, Juehuan Liu, et al. Lasot: A high-quality large-scale single object tracking benchmark. IJCV, 2021. 2  
+[13] Xiao Fu, Xian Liu, Xintao Wang, Sida Peng, Menghan Xia, Xiaoyu Shi, Ziyang Yuan, Pengfei Wan, Di Zhang, and Dahua Lin. 3dtrajmaster: Mastering 3d trajectory for multi-entity motion in video generation. In ICLR, 2025. 2, 3  
+[14] Daniel Geng, Charles Herrmann, Junhwa Hur, Forrester Cole, Serena Zhang, Tobias Pfaff, Tatiana Lopez-Guevara,
+
+Carl Doersch, Yusuf Aytar, Michael Rubinstein, Chen Sun, Oliver Wang, Andrew Owens, and Deqing Sun. Motion prompting: Controlling video generation with motion trajectories. arXiv preprint arXiv:2412.02700, 2024. 2  
+[15] Zekai Gu, Rui Yan, Jiahao Lu, Peng Li, Zhiyang Dou, Chenyang Si, Zhen Dong, Qifeng Liu, Cheng Lin, Ziwei Liu, Wenping Wang, and Yuan Liu. Diffusion as shader: 3d-aware video diffusion for versatile video generation control. arXiv preprint arXiv:2501.03847, 2025. 2, 3  
+[16] Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. ICLR, 2024. 2  
+[17] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. NeurIPS, 2017. 5  
+[18] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NeurIPS, 2020. 2  
+[19] Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. NeurIPS, 35, 2022. 2  
+[20] Lingyi Hong, Wenchao Chen, Zhongying Liu, Wei Zhang, Pinxue Guo, Zhaoyu Chen, and Wenqiang Zhang. Lvos: A benchmark for long-term video object segmentation. In ICCV, 2023. 2  
+[21] Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, and Jie Tang. Cogvideo: Large-scale pretraining for text-to-video generation via transformers. arXiv preprint arXiv:2205.15868, 2022. 2  
+[22] Yash Jain, Anshul Nasery, Vibhav Vineet, and Harkirat Behl. Peekaboo: Interactive video generation via masked-diffusion. arXiv preprint arXiv:2312.07509, 2023. 2, 3  
+[23] JovianZM. Pexels-400k dataset. https://huggingface.co/datasets/jovianzm/Pexels-400k, 2024.5  
+[24] Xuan Ju, Yiming Gao, Zhaoyang Zhang, Ziyang Yuan, Xintao Wang, Ailing Zeng, Yu Xiong, Qiang Xu, and Ying Shan. Miradata: A large-scale video dataset with long durations and structured captions. NeurIPS, 2024. 4  
+[25] Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, Zhangyang Wang, Shant Navasardyan, and Humphrey Shi. Text2video-zero: Text-to-image diffusion models are zero-shot video generators. arXiv preprint arXiv:2303.13439, 2023. 2  
+[26] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 3  
+[27] Alexander Kirillov, Ross Girshick, Kaiming He, and Piotr Dollár. Panoptic feature pyramid networks. In CVPR, 2019. 4  
+[28] Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03603, 2024. 2, 3
+
+[29] Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. Gligen: Open-set grounded text-to-image generation. CVPR, 2023. 3  
+[30] Yaowei Li, Xintao Wang, Zhaoyang Zhang, Zhouxia Wang, Ziyang Yuan, Liangbin Xie, Ying Shan, and Yuexian Zou. Image conductor: Precision control for interactive video synthesis. In AAAI, 2025. 2, 3, 4, 5, 6, 7  
+[31] Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003, 2022. 2  
+[32] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6  
+[33] Jiaxu Miao, Yunchao Wei, Yu Wu, Chen Liang, Guangrui Li, and Yi Yang. Vspw: A large-scale dataset for video scene parsing in the wild. In CVPR, 2021. 2  
+[34] Jiaxu Miao, Xiaohan Wang, Yu Wu, Wei Li, Xu Zhang, Yunchao Wei, and Yi Yang. Large-scale video panoptic segmentation in the wild: A benchmark. In CVPR, 2022. 2  
+[35] Koichi Namekata, Sherwin Bahmani, Ziyi Wu, Yash Kant, Igor Gilitschenski, and David B. Lindell. Sg-i2v: Self-guided trajectory control in image-to-video generation. In ICLR, 2025. 2, 3, 5, 6, 7  
+[36] Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741, 2021. 2  
+[37] William Peebles and Saining Xie. Scalable diffusion models with transformers. arXiv preprint arXiv:2212.09748, 2022. 2, 3  
+[38] F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung. A benchmark dataset and evaluation methodology for video object segmentation. In CVPR, 2016. 2, 6  
+[39] Haonan Qiu, Zhaoxi Chen, Zhouxia Wang, Yingqing He, Menghan Xia, and Ziwei Liu. Freetraj: Tuning-free trajectory control in video diffusion models. arXiv preprint arXiv:2406.16863, 2024. 2, 3  
+[40] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In ICML, 2021. 2  
+[41] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022. 2  
+[42] René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. TPAMI, 2020. 5  
+[43] Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714, 2024. 2, 5  
+[44] Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng
+
+Yan, et al. Grounded sam: Assembling open-world models for diverse visual tasks. arXiv preprint arXiv:2401.14159, 2024. 2, 5  
+[45] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 2  
+[46] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, 2015. 2  
+[47] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. NeurIPS, 2022. 2  
+[48] Xiaoyu Shi, Zhaoyang Huang, Fu-Yun Wang, Weikang Bian, Dasong Li, Yi Zhang, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, et al. Motion-i2v: Consistent and controllable image-to-video generation with explicit motion modeling. SIGGRAPH, 2024. 2, 3, 5, 6, 7  
+[49] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 2  
+[50] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020. 2  
+[51] Rui Tian, Mingfei Gao, Mingze Xu, Jiaming Hu, Jiasen Lu, Zuxuan Wu, Yinfei Yang, and Afshin Dehghan. Unigen: Enhanced training & test-time strategies for unified multimodal understanding and generation. arXiv preprint arXiv:2505.14682, 2025. 2  
+[52] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothee Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 2, 5  
+[53] Shuyuan Tu, Qi Dai, Zuxuan Wu, Zhi-Qi Cheng, Han Hu, and Yu-Gang Jiang. Implicit temporal modeling with learnable alignment for video recognition. In ICCV, 2023. 2  
+[54] Shuyuan Tu, Qi Dai, Zhi-Qi Cheng, Han Hu, Xintong Han, Zuxuan Wu, and Yu-Gang Jiang. Motioneditor: Editing video motion via content-aware diffusion. In CVPR, 2024. 2  
+[55] Shuyuan Tu, Qi Dai, Zihao Zhang, Sicheng Xie, Zhi-Qi Cheng, Chong Luo, Xintong Han, Zuxuan Wu, and Yu-Gang Jiang. Motionfollower: Editing video motion via lightweight score-guided diffusion. arXiv preprint arXiv:2405.20325, 2024.  
+[56] Shuyuan Tu, Zhen Xing, Xintong Han, Zhi-Qi Cheng, Qi Dai, Chong Luo, and Zuxuan Wu. Stableanimator: High-quality identity-preserving human image animation. In CVPR, 2025.  
+[57] Shuyuan Tu, Zhen Xing, Xintong Han, Zhi-Qi Cheng, Qi Dai, Chong Luo, Zuxuan Wu, and Yu-Gang Jiang. Stableimator++: Overcoming pose misalignment and face distortion for human image animation. arXiv preprint arXiv:2507.15064, 2025. 2
+
+[58] Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang, Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng, Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang, Tianyi Gui, Tingyu Weng, Tong Shen, Wei Lin, Wei Wang, Wei Wang, Wenmeng Zhou, Wente Wang, Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang, Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu, and Ziyu Liu. Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314, 2025. 3, 6  
+[59] Hanlin Wang, Hao Ouyang, Qiuyu Wang, Wen Wang, Ka Leong Cheng, Qifeng Chen, Yujun Shen, and Limin Wang. Levitor: 3d trajectory oriented image-to-video synthesis. In CVPR, pages 12490-12500, 2025. 2, 3, 5, 6, 7  
+[60] Jiawei Wang, Yuchen Zhang, Jiaxin Zou, Yan Zeng, Guoqiang Wei, Liping Yuan, and Hang Li. Boximator: Generating rich and controllable motions for video synthesis. arXiv preprint arXiv:2402.01566, 2024. 2, 3  
+[61] Junke Wang, Zhi Tian, Xun Wang, Xinyu Zhang, Weilin Huang, Zuxuan Wu, and Yu-Gang Jiang. Simplear: Pushing the frontier of autoregressive visual generation through pretraining, sft, and rl. arXiv preprint arXiv:2504.11455, 2025. 2  
+[62] Qinghe Wang, Yawen Luo, Xiaoyu Shi, Xu Jia, Huchuan Lu, Tianfan Xue, Xintao Wang, Pengfei Wan, Di Zhang, and Kun Gai. Cinemaster: A 3d-aware and controllable framework for cinematic text-to-video generation. In SIGGRAPH, 2025. 2, 3  
+[63] Rui Wang, Dongdong Chen, Zuxuan Wu, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Yu-Gang Jiang, Luowei Zhou, and Lu Yuan. Bevt: Bert pretraining of video transformers. In CVPR, 2022. 2  
+[64] Rui Wang, Dongdong Chen, Zuxuan Wu, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Lu Yuan, and Yu-Gang Jiang. Masked video distillation: Rethinking masked feature modeling for self-supervised video representation learning. In CVPR, 2023. 2  
+[65] Zhouxia Wang, Yushi Lan, Shangchen Zhou, and Chen Change Loy. ObjCtrl-2.5D: Training-free object control with camera poses. In arXiv preprint arXiv:2412.07721, 2024. 5  
+[66] Zhouxia Wang, Ziyang Yuan, Xintao Wang, Yaowei Li, Tianshui Chen, Menghan Xia, Ping Luo, and Ying Shan. Motionctrl: A unified and flexible motion controller for video generation. In SIGGRAPH, 2024. 2, 3, 4, 5  
+[67] Yujie Wei, Shiwei Zhang, Hangjie Yuan, Xiang Wang, Haonan Qiu, Rui Zhao, Yutong Feng, Feng Liu, Zhizhong Huang, Jiaxin Ye, et al. Dreamvideo-2: Zero-shot subject-driven video customization with precise motion control. arXiv preprint arXiv:2410.13830, 2024. 2  
+[68] Zejia Weng, Xitong Yang, Zhen Xing, Zuxuan Wu, and Yu
+
+Gang Jiang. Genrec: Unifying video generation and recognition with diffusion models. NeurIPS, 2024. 4  
+[69] Jianzong Wu, Xiangtai Li, Yanhong Zeng, Jiangning Zhang, Qianyu Zhou, Yining Li, Yunhai Tong, and Kai Chen. Motionbooth: Motion-aware customized text-to-video generation. NeurIPS, 2024. 2  
+[70] Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation. In ICCV, 2023. 2  
+[71] Weijia Wu, Zhuang Li, Yuchao Gu, Rui Zhao, Yefei He, David Junhao Zhang, Mike Zheng Shou, Yan Li, Tingting Gao, and Di Zhang. Draganything: Motion control for anything using entity representation. In ECCV. Springer, 2024. 5, 6, 7  
+[72] Sicheng Xie, Haidong Cao, Zejia Weng, Zhen Xing, Shiwei Shen, Jiaqi Leng, Xipeng Qiu, Yanwei Fu, Zuxuan Wu, and Yu-Gang Jiang. Human2robot: Learning robot actions from paired human-robot videos. arXiv preprint arXiv:2502.16587, 2025. 2  
+[73] Zhen Xing, Qi Dai, Han Hu, Jingjing Chen, Zuxuan Wu, and Yu-Gang Jiang. Svformer: Semi-supervised video transformer for action recognition. In CVPR, 2023. 2  
+[74] Zhen Xing, Qi Dai, Zihao Zhang, Hui Zhang, Han Hu, Zuxuan Wu, and Yu-Gang Jiang. Vidiff: Translating videos via multi-modal instructions with diffusion models. arXiv preprint arXiv:2311.18837, 2023. 2  
+[75] Zhen Xing, Qi Dai, Han Hu, Zuxuan Wu, and Yu-Gang Jiang. Simda: Simple diffusion adapter for efficient video generation. In CVPR, 2024. 2  
+[76] Zhen Xing, Qi Dai, Zejia Weng, Zuxuan Wu, and YuGang Jiang. Aid: Adapting image2video diffusion models for instruction-guided video prediction. arXiv preprint arXiv:2406.06465, 2024. 2  
+[77] Zhen Xing, Qijun Feng, Haoran Chen, Qi Dai, Han Hu, Hang Xu, Zuxuan Wu, and Yu-Gang Jiang. A survey on video diffusion models. ACM Computing Surveys, 2024. 2  
+[78] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, and Andreas Geiger. Unifying flow, stereo and depth estimation. IEEE TPAMI, 2023. 5  
+[79] Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, and Shalini De Mello. Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models. arXiv preprint arXiv:2303.04803, 2023. 4  
+[80] Ning Xu, Linjie Yang, Yuchen Fan, Dingcheng Yue, Yuchen Liang, Jianchao Yang, and Thomas Huang. Youtube-vos: A large-scale video object segmentation benchmark. arXiv preprint arXiv:1809.03327, 2018. 2  
+[81] Shiuyuan Yang, Liang Hou, Haibin Huang, Chongyang Ma, Pengfei Wan, Di Zhang, Xiaodong Chen, and Jing Liao. Direct-a-video: Customized video generation with user-directed camera movement and object motion. In SIGGRAPH, 2024. 2  
+[82] Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video
+
+diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072, 2024. 2,3,4,6  
+[83] Guy Yariv, Yuval Kirstain, Amit Zohar, Shelly Sheynin, Yaniv Taigman, Yossi Adi, Sagie Benaim, and Adam Polyak. Through-the-mask: Mask-based motion trajectories for image-to-video generation. In CVPR, 2025. 2  
+[84] Shengming Yin, Chenfei Wu, Jian Liang, Jie Shi, Houqiang Li, Gong Ming, and Nan Duan. Dragnuwa: Fine-grained control in video generation by integrating text, image, and trajectory. arXiv preprint arXiv:2308.08089, 2023. 2, 3, 4, 6, 7  
+[85] Hui Zhang, Zuxuan Wu, Zhen Xing, Jie Shao, and Yu-Gang Jiang. Adadiff: Adaptive step selection for fast diffusion. arXiv preprint arXiv:2311.14768, 2023. 2  
+[86] Hui Zhang, Dexiang Hong, Tingwei Gao, Yitong Wang, Jie Shao, Xinglong Wu, Zuxuan Wu, and Yu-Gang Jiang. Creatilayout: Siamese multimodal diffusion transformer for creative layout-to-image generation. arXiv preprint arXiv:2412.03859, 2024.  
+[87] Hui Zhang, Tingwei Gao, Jie Shao, and Zuxuan Wu. Blockdance: Reuse structurally similar spatio-temporal features to accelerate diffusion transformers. In CVPR, 2025.  
+[88] Hui Zhang, Dexiang Hong, Maoke Yang, Yutao Chen, Zhao Zhang, Jie Shao, Xinglong Wu, Zuxuan Wu, and Yu-Gang Jiang. Creatidesign: A unified multi-conditional diffusion transformer for creative graphic design. arXiv preprint arXiv:2505.19114, 2025. 2  
+[89] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In ICCV, 2023. 2, 3  
+[90] Yu Zhang, Yunqi Li, Yifan Yang, Rui Wang, Yuqing Yang, Dai Qi, Jianmin Bao, Dongdong Chen, Chong Luo, and Lili Qiu. Reasongen-r1: Cot for autoregressive image generation models through sft and rl. arXiv preprint arXiv:2505.24875, 2025. 2  
+[91] Zhenghao Zhang, Junchao Liao, Menghao Li, Zuozhuo Dai, Bingxue Qiu, Siyu Zhu, Long Qin, and Weizhi Wang. Tora: Trajectory-oriented diffusion transformer for video generation. In CVPR, 2025. 2, 3, 4, 5, 6, 7  
+[92] Wenliang Zhao, Yongming Rao, Zuyan Liu, Benlin Liu, Jie Zhou, and Jiwen Lu. Unleashing text-to-image diffusion models for visual perception. ICCV, 2023. 4  
+[93] Zangwei Zheng, Xiangyu Peng, Tianji Yang, Chenhui Shen, Shenggui Li, Hongxin Liu, Yukun Zhou, Tianyi Li, and Yang You. Open-sora: Democratizing efficient video production for all. arXiv preprint arXiv:2412.20404, 2024. 2, 3  
+[94] Haitao Zhou, Chuang Wang, Rui Nie, Jinlin Liu, Dongdong Yu, Qian Yu, and Changhu Wang. Trackgo: A flexible and efficient method for controllable video generation. In AAAI, 2025. 2, 3, 4, 5

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+# Make Me Happier: Evoking Emotions Through Image Diffusion Models
+
+Qing Lin $^{1}$  Jingfeng Zhang $^{2,3}$  Yew-Soon Ong $^{1}$  Mengmi Zhang $^{1,*}$
+
+$^{1}$ Nanyang Technological University, Singapore
+
+2The University of Auckland, New Zealand
+
+$^{3}$ RIKEN AIP, Tokyo, Japan
+
+* Address correspondence to mengmi.zhang@ntu.edu.sg
+
+![](images/42512f02f40ae0a4c94ce6486f48385def17ab0b5656e4a927e26a5cb26526a6.jpg)  
+From anger to amusement  
+AIF 22.82  
+Figure 1. The generated images evoke a sense of happiness in viewers, contrasting with the negative emotions elicited by the source images. Given a source image that triggers negative emotions (framed in green), our method (Ours) synthesizes a new image that elicits the given positive target emotions (in red), while maintaining the essential elements and structures of the scene. For instance, our method replaces the anger-inducing flames in the source image with cute-shaped lamps to evoke the target emotion of amusement. While in an outdoor setting, the raging fire is substituted with a tranquil, lush meadow to inspire a sense of awe. For comparisons, we include other competitive methods. The blue number below each image represents its CAM-based ESMI score, with higher values being better.
+
+![](images/60c15160c4f1851ca5a6c8be744d74c68518bcff5a1b00d81e0d2360373e9d1b.jpg)  
+EmoEdit 70.57
+
+![](images/8438fb3df864212f985c2f69e9373a21c1660a9b951e9d0fc87dbd3c525f070f.jpg)  
+LMS 65.03
+
+![](images/4181cc21ff8b34b6392806509c0452ffc59287bc489bf74bf8f4c1fbd44e634f.jpg)  
+Ours 78.45  
+From anger to awe
+
+![](images/58a2ed4ccc5353a9ea80d4816b6baa11fb886e02a03ac4ac0885583a3f12f8a5.jpg)  
+Source Image
+
+![](images/e93f86aeaec54a154adbc0d5e76887965f2bcc89f2734dcc3bb59dc837388505.jpg)  
+CT 38.10
+
+![](images/6484775f028078f971a5c687b4cca3f3aca77c4496caac5585bc48f3efd9a092.jpg)  
+NST 64.56
+
+![](images/dc11b2e63630a7ab8a76667a52e0efda64848e6fa2d3ce775f84593301a45935.jpg)  
+AIF 32.97
+
+![](images/f3d260e6d519a60f12c118573713db5d27c9ed2dbd35bc280e471747f6a4fbd4.jpg)  
+EmoEdit 31.25
+
+![](images/4836d8eabf624ce1d9b37b008a96eb81555f2cc314ae4ca136bb5708282405a6.jpg)  
+LMS 10.27
+
+![](images/bef723c8194101f09d92492a224b30a4725b900a022adf3989ab1cdbf0c934f6.jpg)  
+Ours 68.12
+
+# Abstract
+
+Despite the rapid progress in image generation, emotional image editing remains under-explored. The semantics, context, and structure of an image can evoke emotional responses, making emotional image editing techniques valuable for various real-world applications, including treatment of psychological disorders, commercialization of products, and artistic design. First, we present a novel challenge of emotion-evoked image generation, aiming to synthesize images that evoke target emotions while retaining the semantics and structures of the original scenes. To address this challenge, we propose a diffusion model capable of effectively understanding and editing source images to convey desired emotions and sentiments. Moreover, due to the lack of emotion editing datasets, we provide a unique dataset consisting of 340,000 pairs of images and their emotion annotations. Furthermore, we conduct human psychophysics experiments and introduce a new evaluation metric to systematically benchmark all the methods. Experimental results demonstrate that our method
+
+surpasses all competitive baselines. Our diffusion model is capable of identifying emotional cues from original images, editing images that elicit desired emotions, and meanwhile, preserving the semantic structure of the original images. All code, model, and dataset are available at GitHub.
+
+# 1. Introduction
+
+"I am feeling down. Bring some excitement to my room."
+
+What we perceive not only entails useful visual information but also evokes profound emotional reactions. Incorporating visual cues that elicit emotions into images holds practical significance in various domains. In commercialization, it enhances brand impact and captures consumer attention. In augmented or virtual reality, it supports therapeutic interventions for conditions such as autism and schizophrenia. Despite recent advances in image generation [5, 27, 35], emotion conditioning remains largely underexplored. We introduce the novel task of emotion-evoked image generation: given a source image and a target emotion, the goal is to generate a new image that evokes the specified emotion while preserving the semantics and structure of the original scene. This task is
+
+challenging, requiring both recognition of subtle emotional cues in the source image and the ability to modify them meaningfully. For example, as shown in Figure 1, clusters of fire flames on the water intensify the emotion of anger. In the generated image, they are replaced with lamps designed in cute shapes, transforming the overall emotional response to amusement.
+
+Studies have shown that both global and local factors influence emotional responses in images, such as overall color tones, local facial expressions, and emotion-related objects like graveyards (sadness) or balloons (amusement) [30, 31]. As shown in Figure 2, inserting fearful elements, such as fire, into a peaceful scene can evoke negative emotions, as they trigger instinctive responses to threats. However, some elements, such as the sky and plants, remain emotionally neutral. Given that humans are generally more sensitive to negative than positive stimuli [2, 10], simply adjusting global attributes like brightness may not be enough to alter negative emotions. Traditional methods such as color transfer and style transfer [6, 20, 28] attempt to modify emotional tone through global appearance changes. However, they often fail to elicit desired emotions, as they cannot identify and edit the specific local regions responsible for emotional impact. In Figure 2, while darkening the scene may enhance fear (left panel), brightening a scene of a burning hillside does not effectively convey awe (right panel). In contrast, transforming anger into awe requires editing key emotional cues, for example, removing the fire and adding vibrant clouds to create a tranquil atmosphere. Emotion-evoked image generation thus requires not just global color or style adjustments, but also semantic understanding and purposeful local modification.
+
+Drawing inspiration from these emotion-associated factors, we propose our model, EmoEditor, for emotion-evoked image generation. We develop EmoEditor based on state-of-the-art stable diffusion models and introduce three technical novelties. (1) EmoEditor employs a novel dual-branch architecture in the reverse diffusion process that integrates emotion-conditioned global context and local emotional cues from the source images to ensure coherence with the original content while reflecting the desired emotional outcome. (2) During training, the network's behaviors are guided by aligning model creativity with human expectations in the neuro-symbolic space, implicitly learning the mapping from target emotions to human-annotated text instructions. (3) During inference, iterative emotion discrimination mechanisms are employed to select emotionally coherent images autonomously.
+
+With the rapid advancements in diffusion models, various effective image editing techniques have emerged [3, 11, 23]. However, these methods are not specifically tailored for understanding and identifying
+
+![](images/58a327cf3913cfc87c91f225b080b61c7d629c920d677def7dbbee6cb0f711b5.jpg)  
+Figure 2. Emotions are influenced by both global and local cues from the source images. We present examples of transitions from positive to negative emotions (blue shading) and from negative to positive emotions (orange shading). In each example, the target emotions are in red, and source images are framed in green.
+
+![](images/565c14960020b2ea3bc9d1ce8c7927000d31b12f057a5226fd2dcd4c03483f32.jpg)
+
+emotion-associated elements within images. To address this gap, we propose a competitive baseline called Large Model Series (LMS), concatenating multiple large language and vision models. This baseline first understands source images through image captioning, then generates emotion-prompted instructions using language models, and finally edits images prompted by these instructions with diffusion models. Upon comparison with existing methods through a series of human psychophysics experiments and a new quantitative evaluation, our end-to-end trained EmoEditor achieves outstanding image generation results. It effectively preserves the structural coherence and semantic consistency with the source images while eliciting the target emotion of human viewers. To obtain training data for this task, we establish a dataset curation pipeline and introduce the first large-scale EmoPair dataset, comprising approximately 340,000 pairs of images annotated with emotion-conditioned editing instructions. Unlike existing datasets on visual emotion analysis [17, 19, 31], which assign emotion labels to individual images for classification, our dataset provides paired images annotated with source and target emotion labels.
+
+The main contributions of this paper are highlighted below: (1) We study the problem of emotion-evoked image generation, which focuses on modifying visual content to trigger intended emotional responses while preserving scene structures and contextual coherence. To benchmark all methods, we establish a comprehensive framework for evaluating model performance, incorporating human psychophysics experiments and introducing a new metric. (2) We propose EmoEditor, an emotion-evoked diffusion model, which employs a novel two-branch architecture to integrate global context with local semantic regions of source images that evoke emotional responses. (3) EmoEditor learns emotional cues during end-to-end training, eliminating the need for additional emotion reference images. During inference, EmoEditor generates emotion-evoked images without hand-crafted emotion-conditioned text instructions, while preserving context and structural coherence with the source image. Its performance is superior among all the competitive methods. (4) We curate the large-scale EmoPair dataset, containing 340,000 image pairs with emotion annotations.
+
+# 2. Related Work
+
+# 2.1. Visual Emotion Analysis
+
+Visual Emotion Analysis (VEA) seeks to understand and predict human emotional responses from visual data. In psychology, Categorical Emotion States (CES) underpin classification models such as the 8-category Mikels model [17]. Early work relied on handcrafted features like color [15], while recent methods leverage deep learning for more accurate emotion prediction [30, 33]. However, generating images to evoke specific emotions remains underexplored. To address this, we introduce EmoEditor, a two-branch diffusion model that jointly learns emotion recognition and image generation.
+
+Existing VEA datasets [19, 34] focus on emotion classification, including EmoSet [31], which has 120k images with emotion labels and attributes. However, they lack image pairs with annotated emotions and text editing instructions essential for emotion-evoked image generation. Thus, we introduce the EmoPair dataset, comprising 340k image pairs with differing source and target emotions while ensuring consistent scene semantics and structure.
+
+# 2.2. Image Editing
+
+Diffusion-based Image Editing. Earlier studies used GANs [7] and CLIP models [21] for image manipulation based on detailed text descriptions [1]. Diffusion models have advanced, generating high-quality images from various prompts [8, 23]. Methods like SDEdit [16] use stochastic differential equations for editing. Ip2p [3] allows image editing based on human instructions. PnP [25] achieves fine-grained control of generated structures by manipulating spatial features and self-attention. CtrlNet [36] adds conditional control for better precision. Despite these advances, current diffusion models often overlook emotional cues needed for effective emotion editing.
+
+Emotion Editing. Color and style transfer methods like PDF-Transfer [20], Neural Style Transfer (NST) [6], and CLIP-Styler (Csty) [13] manipulate images using reference images but struggle with local adjustments, limiting their versatility in generating emotion-evoking images. AIF [28] reflects emotions from text to images by incorporating affective content into the image generation process. It relies heavily on detailed textual prompts and cannot edit image regions locally. EmoEdit [32] maps input images and target emotions into an embedding guided by a set of fixed learned queries, restricting emotional cues and diversity in generation to the representational capacity of these queries. In contrast, our EmoEditor uses end-to-end training to implicitly understand and edit emotional cues locally and globally without relying on external references.
+
+# 3. Method
+
+We formulate emotion-evoked image generation as a supervised learning task. First, we curate the EmoPair dataset with emotion-labeled image pairs and editing instructions. Next, EmoEditor is trained on this dataset. During inference, it takes source images and target emotions, producing generated images without the need for text instructions or emotion reference images.
+
+# 3.1. Curating Our EmoPair Dataset
+
+Given the unavailability of a paired image dataset for emotion editing, we curate EmoPair. It consists of two subsets: EmoPair-Annotated Subset (EPAS), containing 331,595 image pairs from Ip2p [3] annotated with emotion labels; and EmoPair-Generated Subset (EPGS), consisting of 6,949 pairs generated based on text instructions given target emotions. All images from EmoPair are of size  $224 \times 224$ . Figure 3 illustrates the pipeline for curating our EmoPair dataset. See Sec S1 for more details.
+
+Emotion Predictor  $\mathcal{P}$ . To construct our dataset, we use the emotion predictor  $\mathcal{P}$  proposed in [31], which was trained on the largest VEA dataset, EmoSet. All images are classified into 8 emotion categories, encompassing four positive (amusement, awe, contentment, excitement) and four negative (anger, disgust, fear, sadness) emotions.
+
+EmoPair-Annotated Subset (EPAS). The original Ip2p dataset lacks emotion labels, consisting only of image pairs with text instructions for editing. To address this, we use  $\mathcal{P}$  to classify the source and target images from the Ip2p dataset into eight distinct emotions. We augment the Ip2p dataset with emotion labels, resulting in 331,595 image pairs. Notably, the text instructions with these image pairs serve as human expectations to guide EmoEditor's training but are not utilized during inference.
+
+EmoPair-Generated Subset (EPGS). To address the lack of emotional cues in the Ip2p dataset, we create the EPGS subset using emotionally rich images from EmoSet [31]. However, EmoSet lacks paired images with consistent scenes and target emotions, and it does not include text editing instructions. Single-word prompts like "[Target Emotion]" prove ineffective in existing text-to-image models due to their limited emotional reasoning. Single-emotion terms lack specificity, resulting in varied interpretations depending on the context of source images. For instance, enhancing an image for excitement can vary significantly based on context, such as having a happy smile on a person or adding fireworks to the sky for a landscape. Thus, to generate accurate image pairs, we develop 50 general instructions for transitioning to desired emotions of 8 categories using GPT-3 [4]. Human annotators rank these instructions, keeping the top ten per emotion. The pre-trained Ip2p model then uses these instructions to manipulate EmoSet source images, resulting
+
+![](images/b09b54c7912bd4be87f7774cbd540bdaf7d046ee61f1ab1c3450c674e514261d.jpg)  
+Figure 3. Pipeline for Curating Our EmoPair Dataset. The dataset comprises two subsets: EmoPair-Annotated Subset (EPAS, left blue box) and EmoPair-Generated Subset (EPGS, right orange box). Each subset includes schematics depicting the creation, selection, and labeling of image pairs in the upper quadrants, with two example pairs in the lower quadrants. Each example pair comprises a source image (framed in green) and a target image. The classified source and target emotion labels (highlighted in red) and target-emotion-driven text instructions for image editing are provided. Note that these instructions are used during model training to guide the learning of rich neural-symbolic embeddings, but are not used during model inference.
+
+![](images/8621c88c28144a44983dc37d3b253d2cfda61d182e27bb5d48576a14199c82d4.jpg)
+
+![](images/ce8c7bfbd7922a448e04208d3c234a5e0dbf26382d6f45e1a8743fd759843a0b.jpg)  
+Figure 4. Architecture of Our Proposed EmoEditor. EmoEditor is an image diffusion model with local (shaded in green) and global (shaded in blue) branches. The pre-trained VAE's encoder  $\mathcal{E}$  and decoder  $\mathcal{D}$  are fixed during training and inference. Exclusively employed during inference, the fixed emotion predictor  $\mathcal{P}$  predicts emotions on generated images for iterative emotion inference.
+
+in 6,949 high-quality image pairs after quality control measures. To ensure the quality of the generated images, we have human annotators providing corrections for some results (see Sec S1).
+
+# 3.2. Our Diffusion Model - EmoEditor
+
+Our EmoEditor, in Figure 4, takes a source image  $I_{s}$  and a target emotion  $y$  to generate an image evoking the desired emotion. It employs the latent diffusion model (LDM) [22] with three key novelties.
+
+Integration of Global and Local Emotion Cues. In the global branch of our EmoEditor, we convert the target emotion  $y$  into a binary one-hot vector  $e_{oh}$ , where the corresponding entry to  $y$  in  $e_{oh}$  is set to 1 and the rest are set to 0. This simplifies model design, enhancing comprehension and generation of complex emotional
+
+images, as one-hot encoding can capture intricate emotion combinations through a multi-label representation. We use the pre-trained emotion predictor  $\mathcal{P}$  to predict the source image's emotional state  $e_s$ , representing the probability distribution of emotions for each category. EmoEditor avoids using emotion labels for  $I_s$  for two reasons: (1) Obtaining emotion labels for source images is often impractical in real-world scenarios. (2) Source images can be complex, conveying multiple emotions simultaneously. Utilizing a stereotypical source emotion as input potentially introduces biases and limits the model's ability to generate diverse outputs.
+
+Next, we compute the emotion editing direction  $e_{dir}$  using the target emotion code  $e_{oh}$  and the source emotional state  $e_s$ :  $e_{dir} = e_{oh} - e_s$ . This allows the model to adaptively adjust the editing direction based on the initial state of different source images. To map the emotion editing direction into the latent space, we introduce an emotion encoder  $\tau_{\theta}$ , trained from scratch to learn neural-symbolic embeddings. It first encodes  $e_{dir}$  into an emotion embedding  $e$  through fully connected layers:  $e = \tau_{\theta}(e_{dir})$  (See Sec S2.1 for details). Later on, we introduce novel losses to regularize the behaviors of  $\tau_{\theta}$ . Notably, we intentionally avoid using explicit text editing instructions as conditional inputs to the model due to the varied interpretations of target emotions, where multiple solutions can elicit the same emotions. Including explicit text instructions may hinder the model's creativity, restricting its capacity to produce diverse sets of images conveying the same emotions.
+
+In the local branch of our EmoEditor, we introduce an image encoder  $\mathcal{E}$  to extract visual features of the source image, acquiring  $z$  for  $I_{s}$ :  $z = \mathcal{E}(I_s)$ . Similar to the LDM [22],  $\mathcal{E}$  is based on a pre-trained VAE [12]. The forward diffusion process gradually adds noise to  $z$ , generating a noisy latent  $z_{t}$ , where the noise level increases over  $t$  time steps  $(t\in T)$ . Specifically, we expressed  $z_{t} = \sqrt{\alpha_{t}}\cdot z_{t - 1} + \sqrt{1 - \alpha_{t}}\cdot \epsilon$ , where  $\alpha_{t}$  is the diffusion coefficient controlling the rate of noise increase, and  $\epsilon$  is a random noise sampled from a normal distribution.
+
+In the reverse diffusion process, the denoising network  $\epsilon_{\theta}$  predicts noise added to the noisy latent  $z_{t}$  using cross-attention with the target emotion condition  $e$  and source image condition  $z$ . The features in  $z$  and  $z_{t}$  enhance understanding of intrinsic information in the image, leading to emotion-specific content for  $I_{s}$ . After  $T - 1$  denoising steps, decoder  $\mathcal{D}$  produces an emotion-evoked image. We minimize the latent diffusion loss, which is the expected error between the predicted noise by  $\epsilon_{\theta}$  and the actual noise  $\epsilon$  sampled during training:  $L_{noise} = E_{t,y,z,\epsilon (z_t),\epsilon \sim N(0,1)}\| \epsilon -\epsilon_{\theta}(z_t,t,z,\tau_{\theta}(y))\|^2$ .
+
+Alignment Loss between Model and Human Judgements. A single emotion term lacks the depth to convey the nuanced causes underlying emotions, posing a challenge for diffusion models to grasp the diagnostic information triggering emotions. In contrast to existing text-to-image models, EmoEditor emulates human reasoning by aligning its thought process with text instructions in our EmoPair dataset, which leads the transition from the source emotion to the target emotion. To achieve this, we introduce an alignment loss in a neuro-symbolic space, between emotion embeddings  $e$  from  $\tau_{\theta}$  and text embeddings  $c$  of instructions in each image pair of our EmoPair dataset with pre-trained CLIP. Specifically, the alignment loss is formulated as the inverse of cosine similarity between  $e$  and  $c$ :  $L_{emb} = 1 - \cos(e, c)$ . Overall, we conduct joint training of  $\tau_{\theta}$  and  $\epsilon_{\theta}$  with the following loss:  $L_{total} = L_{noise} + \lambda L_{emb}$ , where  $\lambda = 0.5$  represents the weight for balancing different losses.
+
+Importantly, we differentiate our alignment loss from the losses typically employed in classical language models, which focus on predicting specific word tokens. Our approach loosens the constraint of precisely predicting the exact text instructions. This flexibility empowers our model to explore a wider array of editing solutions, that can augment the source emotions of human viewers to target emotions. Consequently, our model learns to reason in a neuro-symbolic manner akin to humans and preserves its creativity and capacity for exploring novel editing solutions.
+
+Iterative Emotion Inference (IEI). Editing emotions is a complex process often requiring iterative image edits. To address this, we introduce a recurrent emotion critic process during inference. This critic iteratively infers the evoked
+
+emotions from generated images while assessing their structural coherence and semantic consistency. Specifically, we employ the emotion predictor  $\mathcal{P}$  as the critic.
+
+In the first iteration of image generation, the source image  $I_{s}$  serves as both the input and condition image, producing latent variables  $z$  and  $z_{t}$ . These, along with the emotion vector  $e$ , are fed to the EmoEditor to obtain the generated image  $\hat{I}$ . The critic evaluates the generated image quality based on two criteria: (1) the structural similarity (SSIM) between  $\hat{I}$  and  $I_{s}$  falls within 0.5-0.8, and (2) the predicted emotion by  $\mathcal{P}$  on  $\hat{I}$  matches the target emotion, with a confidence level exceeding 0.6. If both criteria are met, EmoEditor ceases image generation; otherwise, it continues to generate new images. In subsequent iterations, we use  $\hat{I}$  as the input image, generating its noisy latent variable  $\hat{z}_{t}$ , with  $I_{s}$  remaining as the condition. We use  $\mathcal{P}$  to predict the emotional state of  $\hat{I}$  as the new source emotional state and calculate the new emotion editing direction to update the emotion embedding  $e$ . Based on these new inputs, EmoEditor generates a new edited image for the critic. This process iterates until both criteria are satisfied, or the number of critic iterations exceeds a predefined limit of 30. In the latter case, we select the generated result with the highest confidence level predicted by  $\mathcal{P}$  given the target emotion among all past iterations. See Sec S2.5 for more implementation details.
+
+# 4. Experiment
+
+Ideally, with source images and their classified emotions, our goal is to generate images evoking the other 7 target emotions. Considering emotional valence, we propose two tasks: within-valence emotion editing and cross-valence emotion editing. This division serves two purposes. Firstly, cross-valence emotion editing is more common in real-world scenarios, like enhancing positive emotions in stressful environments or creating fear in lively settings during Halloween. Secondly, images of the same valence may contain subtle differences challenging for humans to discern. For instance, an amusement park scene might evoke a mixture of excitement and amusement. We randomly select test images from EmoSet [31], ensuring they differ from those in our EmoPair dataset and that each source emotion category is evenly distributed. We then evaluate the competitive baselines and our EmoEditor on the cross-valence emotion editing task, with a total of 2,016 test images. We also present the results of applying our EmoEditor to more challenging emotion editing scenarios. To ensure a fair comparison, the input image size is standardized to  $224 \times 224$  across all methods.
+
+Baselines. We compare our EmoEditor with nine methods: (1) Color-Transfer (CT) [20]; (2) Neural-Style-Transfer (NST) [6]. Both methods require an additional reference image from the target emotion category as input. We randomly select these reference images from the EmoSet
+
+dataset. We also include representative text-driven image editing models (3) Csty [13]; (4) CtrlNet [36] (5) SDEdit [16]; (6) Ip2p [3] and (7) AIF [28]. We provide these models with the target emotion category as a one-word text prompt, such as "excitement". We also compare with (8) EmoEdit [32], which performs emotion-driven image editing by retrieving semantically similar examples from an emotion dictionary. In addition, we concatenate existing large language and vision models and introduce the baseline (9) "Large Model Series" (LMS). This includes GPT-4o [9] for image captioning, followed by GPT-o4 [18] for generating reasoning-based text instructions, and Ip2p for image editing based on the instructions (See Sec S2.2).
+
+Human Psychophysics Experiment. We evaluate the generated results of all methods on Amazon Mechanical Turk (MTurk) [26]. We recruit 136 participants, with each participant undergoing 180 trials, yielding a total of 24,480 trials. All the experiments are conducted with the subjects' informed consent and according to protocols approved by the Institutional Review Board of our institution. All participants are properly compensated. In each trial, participants engage in a "two-alternative forced choice" task. They are presented with two image stimuli and must select the one that most strongly evokes the target emotion in them. The two image stimuli consist of a result generated by our EmoEditor and a randomly sampled result from the baselines introduced above. The trial presentation order and the binary choices for each trial are randomized. To assess the quality of data collection, we introduce control trials and use them as filtering criteria. We discard the data from those people who fail the control trials at least  $17\%$  of the time. See Sec S2.3 for more details.
+
+Evaluation Metrics. While CLIP [21] assesses text-image consistency, it does not fully capture emotional complexity [29]. Thus, we introduce Emotional Structure Matching Index (ESMI):  $E S M I = \alpha \times S _ { s t r } + ( 1 - \alpha ) \times S _ { e m o }$  where  $\alpha$  is a weighting coefficient and set to 0.5. ESMI evaluates emotion-evoked image generation from two aspects: evoking specific emotions and maintaining structural and semantic consistency between the source and generated images. Evoking Targeted Emotions. To assess how well the generated image expresses the target emotion, we use  $\mathcal{P}$  to extract emotional distributions  $e_{s}$  and  $e_{g}$  from the source and generated images respectively. Next, we calculate the Kullback-Leibler Divergence between these distributions and the target emotion's one-hot encoding  $e_{oh}$  we get  $D_{src} = KLD(e_s,e_{oh})$  and  $D_{gen} = KLD(e_g,e_{oh})$ . Ideally, the generated image should emotionally be closer to the target, that is,  $D_{gen} < D_{src}$ . To avoid the influence of outliers or extreme values, we define the raw emotional improvement as  $\Delta D = \max (0,D_{src} - D_{gen})$ . To further normalize this improvement relative to the original emotional distance, we compute the emotional evocation
+
+![](images/ce2eb3a86734ca24a5ed2eac42a55759c893b7bca4ad049126b8ef0dd4b0f2df.jpg)  
+Figure 5. Results from Human Psychophysics Experiments. The proportions of images that human participants prefer our EmoEditor over all other methods on the x-axis are presented. Chance is  $50\%$  (red dotted line). Error bars are standard errors.
+
+score:  $S_{emo} = \Delta D / (D_{src} + \epsilon)$  where  $\epsilon$  is a small constant to prevent division by zero. This score ranges from 0 to 1, with higher values indicating a greater emotional shift toward the target emotion relative to the source. Preserving Original Structures. We define two regions: the Emotion-Evoking Region ( $R_{emo}$ ), which is responsible for triggering the source emotion (e.g. fire for anger) and is expected to be altered, and the Emotion-Neutral Region ( $R_{neu}$ ), which should remain unchanged. We measure structural consistency by computing the pixel-level L1 differences between the source and generated images in these regions, denoted as  $L_{emo}$  and  $L_{neu}$ . The structural preservation score is calculated as:  $S_{str} = L_{emo} / (L_{emo} + L_{neu} + \epsilon)$  where  $\epsilon$  is a small constant to avoid division by zero. This score reflects the proportion of changes in  $R_{emo}$  relative to all changed pixels, with higher values indicating better preservation of neutral areas. We adopt two strategies to define  $R_{emo}$ : (1) CAM-based:  $R_{emo}$  is obtained by applying Grad-CAM [24] to  $\mathcal{P}$  and binarizing the resulting heatmap. (2) HA-based:  $R_{emo}$  is derived by averaging manually annotated emotional regions of human annotators. See Sec S2.4 for more details.
+
+# 5. Results
+
+# 5.1. Evaluation in Cross-Valence Scenarios
+
+Quantitative Evaluation with SOTA. Figure 5 shows human psychophysics experimental results (see Sec S3.1 for more results). If all methods generate images that elicit target emotions equally effectively, human participants would make random choices, with a  $50\%$  chance in the "two-alternative forced choice" tasks. Observing Figure 5, we see that human participants consistently prefer the generated results of our EmoEditor over all competitive baselines, indicating its proficiency in evoking target emotions. Complimentary to psychophysics experiments, Table 1 presents quantitative results in CAM-based and HA-based ESMI, with our EmoEditor achieving the highest scores in both metrics. Notably, for each method, the scores remain consistent across the two evaluation metrics, indicating that the emotion predictor  $\mathcal{P}$
+
+<table><tr><td>ESMI(%) ↑</td><td>CT</td><td>NST</td><td>Csty</td><td>CtrlNet</td><td>SDEdit</td><td>Ip2p</td><td>AIF</td><td>EmoEdit</td><td>LMS</td><td>Ours</td></tr><tr><td>CAM-based</td><td>28.33</td><td>47.48</td><td>34.33</td><td>32.94</td><td>35.91</td><td>27.37</td><td>29.71</td><td>32.94</td><td>35.40</td><td>51.56</td></tr><tr><td>HA-based</td><td>26.68</td><td>46.00</td><td>32.77</td><td>31.53</td><td>34.27</td><td>25.72</td><td>28.31</td><td>37.53</td><td>33.76</td><td>49.99</td></tr></table>
+
+Table 1. Quantitative Evaluation of Generated Images for All Competitive Methods. The CAM-based metric computes ESMI using  $R_{\mathrm{emo}}$ , derived by binarizing the Grad-CAM output from the Emo Predictor. The HA-based metric computes ESMI using  $R_{\mathrm{emo}}$ , defined as the average of human-annotated regions. Best is in bold. Larger (↑) is better.  
+
+<table><tr><td rowspan="2">Variant</td><td colspan="3">Input of τθ</td><td rowspan="2">IEI</td><td rowspan="2">Lemb</td><td rowspan="2">ESMI(%)↑</td></tr><tr><td>text</td><td>eoh</td><td>edir</td></tr><tr><td>1</td><td>✓</td><td></td><td></td><td></td><td></td><td>5.85</td></tr><tr><td>2</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>50.24</td></tr><tr><td>3</td><td></td><td>✓</td><td></td><td></td><td>✓</td><td>7.36</td></tr><tr><td>4</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>8.51</td></tr><tr><td>5</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>47.36</td></tr><tr><td>6</td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td><td>49.80</td></tr><tr><td>7 (ours)</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>51.56</td></tr></table>
+
+Table 2. Quantitative Evaluation in CAM-based ESMI for Model Variants of our EmoEditor. A tick indicates the inclusion of a component in the corresponding model variant, while a blank cell indicates its absence. The last row represents our full EmoEditor model. Best results are in bold. Larger  $(\uparrow)$  is better.
+
+identifies emotion-evoking regions in a manner aligned with human annotations. This consistency further supports the reliability and validity of the CAM-based ESMI metric.
+
+Our EmoEditor significantly outperforms CT, demonstrating that emotion-evoked image generation requires more than global color adjustments. While NST and Csty can induce negative emotions through distortions and irregular textures, they often reduce image interpretability (see Fig S10). CtrlNet and SDEdit produce abstract textures that may evoke emotion but often fail to preserve the source image's semantics. Human evaluations also prefer EmoEditor over Ip2p, underscoring the limitations of current diffusion models trained for general image generation when applied to emotion-driven tasks. Although both AIF and EmoEdit are designed for emotional editing, our EmoEditor achieves higher human preference and ESMI scores. AIF applies global filters, resulting in less targeted changes. EmoEdit maps image-emotion pairs into an embedding, but the limited and biased emotion dictionary leads to repetitive edits and poor performance on strongly emotional inputs. Finally, our EmoEditor outperforms LMS in both human preference and ESMI, suggesting that simply chaining large models may amplify biases and errors. Emotion-evoked generation demands a nuanced understanding of both the source image and target emotion, making end-to-end learning crucial.
+
+Model Analysis. We conduct the model analysis and present the results in Table 2. In Row 1, the model variant takes only text as input and follows the structure of Ip2p. Although it is fine-tuned on our EmoPair dataset with the pre-trained CLIP model as  $\tau_{\theta}$ , the performance is the lowest among all variants, indicating that text
+
+![](images/4eab6517d062dbbe2191dae89d89de038ebfe726f067c96fdd88bf969a85058b.jpg)  
+Source Image
+
+![](images/64ba54abdaa3dea165f21b7ff8e8a1471c13feea2f291acc53910a68a2ea11c6.jpg)  
+(a) From anger to excitement
+
+![](images/002d1165eba1f35b9e722943340cead9a0f2fe01a08e03be2377dc44727117cc.jpg)  
+NST 11.49
+
+![](images/a9858bf75b1484cb483e3255921d6ae6a23aef2c38f749564b53c8120dbb1318.jpg)  
+AIF 17.39
+
+![](images/20fcd4101d3903a894195a7063dc51847f19772070ad3f14383a497cdb2223f1.jpg)  
+CT 39.16  
+EmoEdit 18.79
+
+![](images/aad545f1ea01e394ab3d98afaa9235939e24297a59a542a92997bf68ccab1b1d.jpg)  
+LMS 13.80
+
+![](images/98fd5ff487bac8354328b7984fd6c00349ce36bc06fc279859a5fb02b614ece1.jpg)  
+Ours 61.67
+
+![](images/155347737e0c81606d24bbb64151ede91c54bdba996958c01bef316ae8ff2465.jpg)  
+(b) From amusement to sadness
+
+![](images/37b5eaa969f12fa3ebccef57ffb84ebd47041d92e73413fb14e565dc62a55731.jpg)
+
+![](images/9a448c275ac9a90a985cea19fdc442b716a6755ed6c9a30fac77cdb87e4ebba9.jpg)  
+Figure 6. Visualization of Generated Images from Different Methods. Target emotion is highlighted in red and source image is framed in green. The blue number below each image is its CAM-based ESMI score, with higher values being better. More examples in Sec S3.2.
+
+![](images/eeea2faee1dd02a34851128fd2b9e84d39f65eaecfe4b2c0484d7a8a6f1a8617.jpg)
+
+![](images/fabb851e7ae562aa5719040cb0f6fc75a7bc49d31be8bc55fdb39497d8b79f49.jpg)
+
+guidance is insufficient. Adding IEI (Row 2) results in notable improvements over the version without IEI (Row 1), confirming its contribution to enhancing emotional consistency. Next, replacing the text input with a one-hot encoding of the target emotion  $e_{oh}$  (Row 3) leads to a better result, highlighting the advantage of structured emotion encoding over text. Further improvement is observed when replacing  $e_{oh}$  with an emotion editing direction  $e_{dir}$  (Row 4), suggesting that incorporating information about the source emotion enables more precise and effective editing. Adding the alignment loss  $L_{emb}$  (Row 6) improves performance over the variant without it (Row 5), confirming its complementary role in promoting emotionally consistent generation. Our final model (Row 7) integrates  $e_{dir}$ , IEI, and  $L_{emb}$ , enabling emotion-aware editing based on the source-target gap. This configuration achieves the highest score, demonstrating its superior emotional precision and generative diversity.
+
+Visualization of Emotional Image Generation across Valence. Figure 1 and 6 present visualizations of generated images in cross-valence scenarios. CT primarily replicates tonal characteristics from a reference image, often failing to significantly enhance emotions. NST relies on randomly
+
+![](images/737cc292247f91ad9a88d4629879f94b36bd80223f54db03f4eb72e84e3843d4.jpg)  
+(a) Same-Valence Editing
+
+![](images/1029588a6f97cafa863c3cc2dd02faccddd419e359be98524c3d78c71e5a814a.jpg)  
+Figure 7. Our EmoEditor can generalize to more challenging emotion editing scenarios. (a) Same-Valence Editing highlights our EmoEditor's ability to produce images evoking emotions of the same positive valence as the source images. (b) Neutral-Valence Editing demonstrates how our EmoEditor can transform neutral real-world images to evoke positive or negative emotions. (c) Multi-Emotion Editing demonstrates its capability to generate images that evoke a wider range of emotions, providing more nuanced and diverse visual outputs. Source images are framed in green. Target emotions are in red.
+
+![](images/6bf4c96cf1be5da2de35a9dbf73017f30c2383342746f984b92db958f1052bc3.jpg)  
+(b) Neutral-Valence Editing
+
+![](images/bb881074e29968a8e1bfe684196d36642ed4c2fdf1d98c93ad5e780825aef1b1.jpg)
+
+![](images/45726848c751ce13ef10bbc23e06b5fb48a81d4127f72d05e8897980fe6cc456.jpg)
+
+![](images/64e0cdd71f16e83b69d20e62454cc772392ae2d6f9ba22319688f4e89990e7e2.jpg)  
+(c) Multi-Emotion Editing
+
+![](images/06efc67a56383ca5e7259f460b1f3ee3a3cd97281293e7755bf766633f39d8dd.jpg)
+
+selected references, producing artistic textures aligned with target emotions but losing essential semantic content. AIF, dependent on emotional text descriptions, applies a global filter effect but struggles with localized editing. EmoEdit relies on fixed learned queries, leading to limited, context-incongruent edits (e.g., always adding balloons for amusement; Figure 1) and failing to edit local emotional cues such as fire flames or fireworks (Figure 6). Even with a richer vocabulary, such as LMS, which integrates three large models, it still struggles to generate desirable outputs. In contrast, our EmoEditor takes only a source image and target emotion as input, yet generates creative and emotionally aligned results while preserving structural and semantic coherence. By learning an emotion editing direction, it understands the source-target gap and applies precise edits. In Figure 6, where fire and fireworks dominate the original emotion, EmoEditor adapts its edits to the target: extinguishing flames and adding mountains to evoke excitement (a), or removing fireworks and darkening tones to convey sadness (b). In Figure 1, it replaces fire flames with lamps to elicit amusement. Notably, even when handling similar anger-inducing elements like fire, our EmoEditor produces diverse, context-aware edits tailored to the source image without any manual design, demonstrating a nuanced understanding of emotional cues. We also show CAM-based ESMI scores (in blue) for each result. Our EmoEditor achieves the highest scores, demonstrating its ability to accurately identify and effectively modify emotion-evoking regions in source images, striking a balance between evoking the target emotion and preserving the original structure.
+
+# 5.2. Generalization to Real-world Scenarios
+
+Editing images with the same emotional valence is more challenging due to subtle emotional cues. Figure 7a demonstrates EmoEditor's ability to handle such tasks, as it removes a parachutist to reveal a breathtaking landscape, shifting excitement to awe. In real-world scenarios, some naturalistic images may evoke mixed emotions or no strong emotional responses at all. We challenge our EmoEditor to generate emotion-evoked images using neutral-valence
+
+source images randomly selected from the MSCOCO dataset [14]. These images depict scenes of everyday life with neutral emotions. EmoEditor applies positive and negative emotion transformations to these source images. It enhances the lushness of a forest to evoke contentment and transforms it into a barren landscape for disgust (Figure 7b). Notably, EmoEditor generates these results purely from its creativity, without any human-annotated editing instructions or scene interpretations.
+
+Furthermore, EmoEditor can manage multi-emotion editing with the use of emotion encoding as input. We extend the single-target emotion scenario by randomly adding one additional emotion of the same valence as the target and then use EmoEditor for image generation. In Figure 7c, EmoEditor generates a majestic mountain in the flame area of the source image to evoke awe. After adding contentment, it enriches the area with dense forests.
+
+# 6. Discussion
+
+We introduce the challenging problem of emotion-evoked image generation and propose EmoEditor, an image diffusion model that understands emotional cues, creates implicit editing instructions aligned with human decisions, and manipulates image regions to evoke emotions, while maintaining coherent scene structures and semantics. Moreover, we contribute EmoPair dataset for model training. To benchmark all methods, we introduce a new evaluation metric that effectively measures the balance between evoking target emotions and maintaining the structural and semantic integrity of the source image.
+
+While EmoEditor excels in quantitative performance and visual results, it struggles with fine details on faces (See Sec S3.4). While we adopt the 8-category Mikels model, real-world emotions are far more nuanced and complex. In particular, differentiating between positive emotions tends to be more challenging than distinguishing negative ones. Additionally, EmoEditor carries risks of misuse, such as evoking undesirable emotions, which could harm mental well-being or mislead public sentiment. All of these aspects warrant further investigation in future research.
+
+# Acknowledgements
+
+This research is supported by the National Research Foundation, Singapore under its NRFF award NRF-NRFF15-2023-0001 and Mengmi Zhang's Startup Grant from Nanyang Technological University.
+
+# References
+
+[1] Rameen Abdal, Peihao Zhu, John Femiani, Niloy Mitra, and Peter Wonka. Clip2stylegan: Unsupervised extraction of stylegan edit directions. In ACM SIGGRAPH 2022 conference proceedings, pages 1-9, 2022. 3  
+[2] Roy F Baumeister, Ellen Bratslavsky, Catrin Finkenauer, and Kathleen D Vohs. Bad is stronger than good. Review of general psychology, 5(4):323-370, 2001. 2  
+[3] Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18392-18402, 2023. 2, 3, 6  
+[4] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020. 3  
+[5] Rinon Gal, Or Patashnik, Haggai Maron, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. Stylegan-nada: Clip-guided domain adaptation of image generators. ACM Transactions on Graphics (TOG), 41(4):1-13, 2022. 1  
+[6] Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. A neural algorithm of artistic style, 2015. 2, 3, 5  
+[7] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014. 3  
+[8] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control, 2022. 3  
+[9] Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024. 6  
+[10] Tiffany A Ito, Jeff T Larsen, N Kyle Smith, and John T Cacioppo. Negative information weighs more heavily on the brain: the negativity bias in evaluative categorizations. Journal of personality and social psychology, 75(4):887, 1998. 2  
+[11] Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. Imagic: Text-based real image editing with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6007-6017, 2023. 2  
+[12] Diederik P Kingma and Max Welling. Auto-encoding variational bayes, 2022. 5  
+[13] Gihyun Kwon and Jong Chul Ye. Clipstyler: Image style transfer with a single text condition. In Proceedings of
+
+the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18062-18071, 2022. 3, 6  
+[14] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014. 8  
+[15] Jana Machajdik and Allan Hanbury. Affective image classification using features inspired by psychology and art theory. In Proceedings of the 18th ACM international conference on Multimedia, pages 83-92, 2010. 3  
+[16] Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073, 2021. 3, 6  
+[17] Joseph A Mikels, Barbara L Fredrickson, Gregory R Larkin, Casey M Lindberg, Sam J Maglio, and Patricia A Reuter-Lorenz. Emotional category data on images from the international affective picture system. Behavior research methods, 37:626-630, 2005. 2, 3  
+[18] OpenAI. Introducing openai o3 and o4-mini: Our smartest and most capable models to date with full tool access. https://openai.com/index/introducing-o3-and-o4-mini/, 2025.6  
+[19] Kuan-Chuan Peng, Tsuhan Chen, Amir Sadovnik, and Andrew C Gallagher. A mixed bag of emotions: Model, predict, and transfer emotion distributions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 860-868, 2015. 2, 3  
+[20] François Pitie, Anil C Kokaram, and Rozenn Dahyot. Automated colour grading using colour distribution transfer. Computer Vision and Image Understanding, 107(1-2): 123-137, 2007. 2, 3, 5  
+[21] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 3, 6  
+[22] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022. 4, 5  
+[23] Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22500-22510, 2023. 2, 3  
+[24] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618-626, 2017. 6
+
+[25] Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel. Plug-and-play diffusion features for text-driven image-to-image translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1921-1930, 2023. 3  
+[26] Amazon Mechanical Turk. Amazon mechanical turk. Retrieved August, 17:2012, 2012. 6  
+[27] Su Wang, Chitwan Sahara, Ceslee Montgomery, Jordi Pont-Tuset, Shai Noy, Stefano Pellegrini, Yasumasa Onoe, Sarah Laszlo, David J Fleet, Radu Soricut, et al. Imagen editor and editbench: Advancing and evaluating text-guided image inpainting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18359-18369, 2023. 1  
+[28] Shuchen Weng, Peixuan Zhang, Zheng Chang, Xinlong Wang, Si Li, and Boxin Shi. Affective image filter: Reflecting emotions from text to images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10810-10819, 2023. 2, 3, 6  
+[29] Hanna-Sophia Widhoelzl and Ece Takmaz. Decoding emotions in abstract art: Cognitive plausibility of clip in recognizing color-emotion associations, 2024. 6  
+[30] Jufeng Yang, Dongyu She, Ming Sun, Ming-Ming Cheng, Paul L Rosin, and Liang Wang. Visual sentiment prediction based on automatic discovery of affective regions. IEEE Transactions on Multimedia, 20(9):2513-2525, 2018. 2, 3  
+[31] Jingyuan Yang, Qirui Huang, Tingting Ding, Dani Lischinski, Danny Cohen-Or, and Hui Huang. Emoset: A large-scale visual emotion dataset with rich attributes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 20383–20394, 2023. 2, 3, 5  
+[32] Jingyuan Yang, Jiawei Feng, Weibin Luo, Dani Lischinski, Daniel Cohen-Or, and Hui Huang. Emoedit: Evoking emotions through image manipulation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 24690–24699, 2025. 3, 6  
+[33] Quanzeng You, Jiebo Luo, Hailin Jin, and Jianchao Yang. Robust image sentiment analysis using progressively trained and domain transferred deep networks. In Proceedings of the AAAI conference on Artificial Intelligence, pages 381-388, 2015. 3  
+[34] Quanzeng You, Jiebo Luo, Hailin Jin, and Jianchao Yang. Building a large scale dataset for image emotion recognition: The fine print and the benchmark. In Proceedings of the AAAI conference on artificial intelligence, pages 308-314, 2016. 3  
+[35] Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S. Huang. Generative image inpainting with contextual attention. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5505-5514, 2018. 1  
+[36] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836-3847, 2023. 3, 6

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+# Make Your Training Flexible: Towards Deployment-Efficient Video Models
+
+Chenting Wang $^{1,2}$  Kunchang Li $^{2}$  Tianxiang Jiang $^{2,4}$  Xiangyu Zeng $^{2,5}$  Yi Wang $^{2,3}$  Limin Wang $^{1,2,5\spadesuit}$
+
+$^{1}$ Shanghai Jiao Tong University  $^{2}$ Shanghai AI Laboratory  $^{3}$ Shanghai Innovation Institute  
+<sup>4</sup>University of Science and Technology of China 5State Key Laboratory for Novel Software Technology, Nanjing University
+
+# Abstract
+
+Current video training methods rely on fixed spatiotemporal sampling grids to extract a predetermined number of tokens, limiting adaptability to diverse computational budgets and resulting in suboptimal accuracy-computation trade-offs. This rigidity constrains high-performance models trained in resource-rich environments from being efficiently deployed on resource-constrained devices. We hence introduce a novel paradigm for lossless adaptation across scenarios, enabling models to maintain optimal performance under high-resource conditions while seamlessly transferring to low-resource environments. Central to this is Token Optimization (TO), an adaptive inference framework that dynamically samples and selects input token set to optimize input information under varied computational constraints. To support this, we propose Flux, an augmentation tool that enables flexible sampling grids and token selection. It integrates seamlessly into popular video training frameworks, significantly enhancing model robustness and adaptability with negligible additional cost. Applied to large-scale video pretraining, our method produces FluxViT, which achieves state-of-the-art performance across multiple tasks under standard costs. Remarkably, with only 1/4 of the tokens, FluxViT matches prior state-of-the-art models under TO across tasks, achieving nearly  $90\%$  computational savings. Code and models are available at https://github.com/OpenGVLab/FluxViT.
+
+# 1. Introduction
+
+Video representation learning [20, 45, 71] is a fundamental task in computer vision, as it is critical in developing spatiotemporal perception and procedural reasoning in multimodal large language models [38, 41, 54, 77] and embodied AI systems [24, 78]. However, the deployment of video models is computationally intensive due to the high volume
+
+![](images/1c0853aa42a0303919f4fbc44443d4798a8228c20d8d20032bd87a5e320ef25a.jpg)  
+Figure 1. Flux (right) employs flexible sampling and token selection to achieve Token Optimization. Common methods(left) use rigid sampling(and use token reduction for applications directly).
+
+of video tokens compared to image tokens. Popular video training methods work on fixed-count patched tokens sampled from a predefined spatiotemporal grid (e.g.,  $8 \times 224^2$ ), leading to considerable redundancy in training and deployment, especially for long, high-resolution videos. Moreover, this inflexible and fixed training achieves sub-optimal results for tasks that require dynamic resolution settings, including restricted computation or emphasizing frame counts rather than frame resolution. Researchers have developed different approaches for better trade-offs.
+
+First, to fit a limited computation budget in inference, many works [15, 31, 59] use token reduction on the same densely sampled tokens as in training, showing sub-optimal performance. For one thing, there is a trade-off between the complexity (cost) and performance of the token reduction strategy. Current strategies can not perform well with a significant reduction rate. For another, the inflexibly trained models can not generalize with the sparse, masked tokens.
+
+Second, existing methods for flexible network training, such as Resformer [65] and FFN [88], have demonstrated the efficacy of flexible networks operating at different spatial or temporal resolutions but not both simultaneously. Al-
+
+though these approaches achieve robust performance across diverse settings, we contend that merely reducing frame counts or resolution under computational constraints fails to optimize token capacity utilization and remains susceptible to redundancy. Notably, FFN exhibits substantial performance degradation when operating with only two frames in their research. We argue that the input token set with a size equal to 2 frames can be optimized. Also, they are not validated in large-scale pre-training to achieve competitive results for real-world applications, and the performance gain with larger resolution input comes with quadratic computation cost increase, which is also insufficient.
+
+We propose a new perspective of the desired optimal computation-and-accuracy and spatial-and-temporal trade-offs across various settings, termed 'Token Optimization' (TO in short), as shown in Figure 1. It involves selecting an optimized set of input tokens based on budget from more properly sampled videos for information maximization. Since there is no perfect token selector for all settings, we promote a better solution of using flexible sampling, with more densely and finely sampled videos for higher computation and more sparsely sampled videos for lower budget. Spatial and temporal trade-offs are also achieved.
+
+To better achieve TO, we propose a new data augmentation tool for training flexible video models, termed Flux for Flexibility in universal contexts, by utilizing the combination of flexible sampling and token selector to make training flexible while not introducing costs. We integrate it with the Unmasked Teacher [42] pre-training, supervised-training, multi-modal contrastive training, and chat-centric training to validate its efficiency. Our new recipe brings more robustness caused by Flux augmentation both in standard settings and our TO setting. Two plug-in modules are used for better feature extraction regarding the reduced token set with flexible reduction rates and input spatiotemporal resolutions. We build bottom-up ablation studies in turning the model flexible. The resulting model FluxViT achieves supreme improvements regarding the currently state-of-the-art InternVideo2-Dist series models in fair settings across budgets and across tasks, as in Figure 2.
+
+Our FluxViT-S outperforms the previous state-of-the-art small-scale model, InternVideo2-S [77], by  $2.2\%$  on K400 under standard computation constraints. Moreover, it achieves comparable performance to InternVideo2-S while utilizing only about  $10\%$  of inference cost. FluxViTB achieves competitive results with state-of-the-art much larger scale models on various tasks, including scene-based action recognition task [34]  $(90.0\%)$  top-1 accuracy on K400), motion-intensive [23]  $(75.8\%)$  top-1 accuracy on SSv2), long-term task [64]  $(94.1\%)$  top-1 accuracy on COIN), zero-shot text-to-video retrieval [1, 12, 28, 61, 81]  $(49.9\%)$  R@1 on MSRVTT,  $53.5\%$  R@1 on DiDeMo and  $56.7\%$  R@1 on ActivityNet). Notably, it maintains state
+
+![](images/b7c292fdbdf379deeb3f34615f76f85e135099d17283a572ae582d37a25c38f0.jpg)
+
+![](images/a4e9a73fb1dc10a5f53b56f8d0aa05453293e4673b3d11228638b812cacb5acc.jpg)  
+Figure 2. Overview of our Flux method. The same-scaled FluxViT and InternVideo2 [77] series models are both pre-trained with the InternVideo2-1b model as the teacher using the same dataset. The "FluxViT+" refers to the results using Token Optimization at test time with the same GFLOPS.
+
+![](images/0ce28d2e99d9e6bc1ff2d764b966fe23c04afb46aecc44d4e43b497b8d905120.jpg)
+
+of-the-art performance among the similar scaled models while using only 1/4 to 1/2 of their tokens, resulting in  $70\%$  to  $95\%$  savings in computation. On chat centric tasks including MVbench [41] for general perception and Dream-1k [70] for fine-grained caption, our model surpasses widely used SigLIP, CLIP and UMT in 'linear prob' setting across computation budgets, where only the projector between the ViT and LLM is trained for comparability regarding the ViT's capability as the common stage-1 training in chat models. This highlights Flux's strong effect across computation constraints and tasks and underscores the robustness afforded by such flexibility.
+
+# 2. Related Work
+
+Flexible Model Training. Many recent studies focus on developing a method to train a single model that performs well across various settings, facilitating seamless adaptation to diverse downstream deployment scenarios. These methods include training a model as a set of shared weights to be pruned for specific tasks [7, 84], as well as optimizing models for varied spatial resolutions [65], frame counts [88], spatiotemporal resolutions [48, 51, 73, 80], patch sizes [6], and representation dimensions [8, 36]. The Resformer [65] and FFN [88] methods propose co-training with three different resolution settings in a single batch, uti
+
+![](images/102e5ce7acdd753e8603a9a396abb300de00927594960668459d79922bd3acb0.jpg)  
+Figure 3. Our proposed Flux method with UMT framework. We show that our proposed Flux training is easy to integrate with mainstream video training frameworks.
+
+lizing self-distillation mechanisms to enhance performance under limited input resolutions for spatial and temporal dimensions, although separately. We argue that they can't achieve optimal performance within constrained computational budgets. Simply reducing frame counts and resolutions is suboptimal. Further, performance gains brought by high-resolution inputs lead to significant cost increases, a challenge also noted in Oryx [51]. Token Optimization addresses this by optimizing the set of input tokens anytime.
+
+Masked Vision Modeling. Mask has always been seen as a critical method in training and utilizing scaled vision models, especially in the video domain. Previous methods can be mainly divided into two categories. First, drawing insights from the masked language modeling [16, 17, 52], researchers use masks in vision transformer [18] pre-training as a data augmentation strategy, as seen in masked autoencoder frameworks [21, 27, 66, 72] and BERT-like mask-then-predict frameworks [4, 58, 74, 90]. Works like UMT [42], MVP [79], and Milan [29] further utilize such augmentation strategy to predict the CLIP [60] model's feature instead of native pixels as a Teacher-Student pretraining method. Second, methods like MAR [59] and MCM [31] apply masking techniques during the fine-tuning stage and inference to save computation with minimal performance loss. We here propose that mask can be used as novel data-augmentation tool with our flexible sampling to boost performance with our Token Optimization idea.
+
+# 3. Method
+
+In this section, we introduce our Flux for effectively pretraining video models that represent videos at varying resolution scales using tokens with flexible quantities robustly.
+
+# 3.1. Flux-Pretraining
+
+We begin with a widely adopted pre-training paradigm in which the representations of a teacher model are employed as soft labels for a student model. Popular approaches following this typically rely on fixed sampling grids and utilize all extracted visual tokens for both the teacher and student
+
+![](images/7695ba9da52af9f9433e6b6696158d750d5a1e5ac767d8069290bba1391b694b.jpg)  
+Figure 4. Our proposed essential modules for Flux. From the model side, Flux modules include Group-dynamic token selector, dual patch norm, and Global-Local positional embedding.
+
+models. However, this methodology suffers from substantial redundancy, particularly in video data at finer resolutions. To address this, Unmasked Teacher (UMT) [42] proposes using only a subset of the visual tokens for the student while retaining the full input for the teacher. This masked alignment strategy enhances both efficiency and robustness for the student model. We propose a further examination of the input tokens for the teacher and leverage flexible sampling grids combined with a group-dynamic token selection mechanism. This enables us to select the same number of tokens as the original setting but from a larger pool extracted at flexibly higher spatiotemporal resolutions that are more informative, thus harvesting more diversified and meaningful representations from the teacher without cost, as shown in Figure 3. The other settings, like alignment losses, are the same as the original UMT, which trains the competitive InternVideo2-Series models [77].
+
+Flexi-Sampling. For each video in a batch, we randomly select a frame count ranging from  $F_{min}$  to  $F_{max}$  with a temporal stride  $t_s$  and select a spatial resolution between  $R_{min}$  and  $R_{max}$  with a spatial stride  $r_s$  and enforce a threshold  $T_{thres}$  in keeping a reasonable size of visual token pool.
+
+Double Mask Module. To make the training flexible while not introducing new costs, we leverage token selection in the teacher input side. We propose a simple but effective method that prioritizes tokens exhibiting significant changes between frames. We use additional sparse groups for this selection to ensure coverage of the full video and resist several rapid changes in videos, termed Group-Dynamic Selector. Formally, with a pre-defined token count  $K$ , which is set to keep unchanged teacher forward cost, and a given tokenized frame sequence  $\{F_1, F_2, \dots, F_T\}$  by teacher's patch embedding layer, we uniformly sample it into  $N$  sparse groups where  $B_i = \{F_t \mid t \in [iB, (i + 1)B - 1)\}, i \in [0, N)$ . In each group, we compute the dynamic value as  $\mathrm{D}(F_{t + 1,i}) = ||F_{t + 1,i} - F_{t,i}||_p$ , where  $F_{t,i}$  means the value of  $i_{th}$  spatial token in the  $t_{th}$  frame and  $p$  means the  $p$ -dim distance. Then we take the top  $\left(\frac{K}{N}\right)$  tokens with the highest dynamic values in each group to
+
+form  $K$  visible tokens in total. This selector, along with flexible sampling, is utilized for the teacher model. We also employ another student mask based on the teacher's attention score of CLS token adopted in Unmasked Teacher. The usage of the two masks is referred to as the Double Mask Module. Such a module greatly boosts the student model's performance and brings no additional cost, showing a natural integration of Flux in the UMT framework.
+
+# 3.2. Flux-Tuning
+
+For the stage of supervised tuning, we keep the flex-sampling method and group-dynamic selector for the trained student model. Table 1 shows such a method's gain even on a competitive model pre-trained with standard setting, showing Flux as a general augmentation tool.
+
+Token Optimization As proposed in the introduction, we hope to maximize token information under any given budget, termed as Token Optimization at downstream tasks. Thus, during evaluation, we use a subset of the train-set to find optimized sampling under a given token count for the task. We provide a heuristic searching method in the Appendix, which takes acceptable overhead.
+
+# 3.3. FluxViT
+
+Conventional ViT architecture struggles with the variable token numbers, flexible spatiotemporal resolution, and sparsity patterns inherent in Flux training. Thus, we propose two useful plug-in augmentation modules: Global-Local Positional Embedding (GLPE) and Dual Patch Normalization (DPN), as shown in Figure 4.
+
+Global-Local Positional Embedding. As we utilize flexible sampling with various spatiotemporal resolutions and token reduction rates, it is important to encode the position information of the selected input tokens about where they are from and how they are related. Thus, Global-Local Positional Embedding (GLPE) is utilized. We enhance the global learnable positional embedding, initialized using the sine-cosine method for the maximum possible input size, by applying a Depth-Wise Convolution [30]. Then, to encode local positional information at a finer granularity in the position-invariant attention mechanism, we apply a Linear Projection function (LPE) to the value vector  $\mathbf{V}$ . The modified attention is formulated as:
+
+$$
+\mathbf {Z} = \left(\operatorname {S o f t m a x} \left(\frac {\mathbf {Q K} ^ {T}}{\sqrt {D}}\right) + L P E\right) \mathbf {V}. \tag {1}
+$$
+
+Such LPE can be generated regardless of the input token number by adding a bias term for the permutation-invariant attention. This is vital in Flux to encode the discrete position information. The Value-dependent nature of LPE is more robust in Flux than others, like RoPE, in that the un-masked tokens are from different places from the source.
+
+The encoding of the relative relations between them is naturally Value-dependent. RoPE is more useful in scenarios without token selection for length extension.
+
+Dual Patch Normalization. The Patch Embedding layer plays a crucial role in dynamics estimation and it must be robust to various spatiotemporal resolution distributions. We observed that an additional Layer Normalization after the standard Patch Embedding improves the accuracy of dynamic estimation, leading to more effective token selection. Furthermore, the flexible sampling strategy leads to a varied distribution of input tokens. Consequently, we find the gradient norm of the Patch Embedding layer can become excessively large, as shown in the Appendix. To stabilize training, we introduce a second Layer-Normalization layer before the Patch Embedding operation.
+
+# 3.4. Train with Varied Input Lengths
+
+As we aim to train a flexible video model that can seamlessly adjust to varied token numbers according to budgets, we propose to use a multi-number co-training approach. For Flux-UMT frameworks, we suggest utilizing three distinct input numbers within a single batch for the student model. This strategy maximizes the benefits of the extensive teacher-forward computation while enhancing robustness under varying conditions. Specifically, we forward the student three times using three different token numbers and compute the teacher-student alignment loss for each. In standard training frameworks without a pre-trained teacher, we incorporate additional self-distillation, using the final aggregated features with the largest input as the teacher for the intermediate and the intermediate as the teacher for the smallest. We include training efficiency in the ablation.
+
+# 4. Experiments
+
+We leverage InternVideo2-1B [77] as the teacher in Flux-UMT due to its strong performance in general video understanding tasks. We use the same data-decomposition and similar training recipe as the currently most competitive InternVideo2-series distilled models as baselines. Note that they are also built using the Unmasked Teacher framework under the supervision of InternVideo2-1B. For notation simplification, we use Flux-Single for Flux training with a single token count and Flux-Multi for Flux training with multi token counts, while Flux-PT is for Flux with UMT pretraining and Flux-FT for supervised tuning.
+
+# 4.1. Ablation Study
+
+In the ablation study, we ablate the effect of our Flux through a systematic approach, progressively transforming the original UMT framework into our Flux-UMT and getting our competitive FluxViT. We utilize K710 for pretraining, which integrates the K400 [34], K600 [9], and
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">Input Size</td><td colspan="4">Test Token Number</td><td rowspan="2">Avg</td></tr><tr><td>3072</td><td>2048</td><td>1024</td><td>512</td></tr><tr><td rowspan="9">\( 8\times 224^2 \)Direct Tuned</td><td>\( 2\times 224^2 \)</td><td>∅</td><td>∅</td><td>∅</td><td>69.5</td><td>69.5</td></tr><tr><td>\( 4\times 224^2 \)</td><td>∅</td><td>∅</td><td>80.5</td><td>73.6</td><td>77.1</td></tr><tr><td>\( 8\times 224^2 \)</td><td>∅</td><td>83.9</td><td>81.9</td><td>72.7</td><td>79.5</td></tr><tr><td>\( 12\times 224^2 \)</td><td>84.6</td><td>84.2</td><td>80.9</td><td>69.8</td><td>79.9</td></tr><tr><td>\( 16\times 224^2 \)</td><td>84.5</td><td>83.5</td><td>79.1</td><td>67.0</td><td>78.5</td></tr><tr><td>\( 20\times 224^2 \)</td><td>84.1</td><td>82.7</td><td>77.7</td><td>64.3</td><td>77.2</td></tr><tr><td>\( 24\times 224^2 \)</td><td>83.6</td><td>82.1</td><td>76.4</td><td>62.3</td><td>76.1</td></tr><tr><td>Avg</td><td>84.2</td><td>83.3</td><td>79.4</td><td>68.5</td><td>-</td></tr><tr><td>Max</td><td>84.6</td><td>84.3</td><td>81.9</td><td>73.6</td><td>-</td></tr><tr><td rowspan="9">2048 fixed countFlux-Single Tuned</td><td>\( 2\times 224^2 \)</td><td>∅</td><td>∅</td><td>∅</td><td>68.1</td><td>68.1</td></tr><tr><td>\( 4\times 224^2 \)</td><td>∅</td><td>∅</td><td>79.9</td><td>74.7</td><td>77.3</td></tr><tr><td>\( 8\times 224^2 \)</td><td>∅</td><td>84.3</td><td>82.7</td><td>76.7</td><td>81.2</td></tr><tr><td>\( 12\times 224^2 \)</td><td>85.1</td><td>85.1</td><td>82.8</td><td>75.8</td><td>82.2</td></tr><tr><td>\( 16\times 224^2 \)</td><td>85.4</td><td>85.2</td><td>82.6</td><td>75.3</td><td>82.1</td></tr><tr><td>\( 20\times 224^2 \)</td><td>85.6</td><td>85.1</td><td>82.2</td><td>74.6</td><td>81.9</td></tr><tr><td>\( 24\times 224^2 \)</td><td>85.4</td><td>84.8</td><td>82.0</td><td>74.1</td><td>81.6</td></tr><tr><td>Avg</td><td>85.4</td><td>84.9</td><td>82.0</td><td>74.2</td><td>-</td></tr><tr><td>Max</td><td>85.6↑1.0</td><td>85.2↑0.9</td><td>82.8↑0.9</td><td>76.7↑3.1</td><td>-</td></tr><tr><td rowspan="9">\( 12\times 224^2 \)Direct Tuned</td><td>\( 2\times 224^2 \)</td><td>∅</td><td>∅</td><td>∅</td><td>69.3</td><td>69.3</td></tr><tr><td>\( 4\times 224^2 \)</td><td>∅</td><td>∅</td><td>80.5</td><td>72.8</td><td>76.7</td></tr><tr><td>\( 8\times 224^2 \)</td><td>∅</td><td>83.9</td><td>81.9</td><td>72.7</td><td>79.5</td></tr><tr><td>\( 12\times 224^2 \)</td><td>85.0</td><td>84.3</td><td>80.9</td><td>67.8</td><td>79.4</td></tr><tr><td>\( 16\times 224^2 \)</td><td>84.9</td><td>83.9</td><td>78.9</td><td>65.2</td><td>78.2</td></tr><tr><td>\( 20\times 224^2 \)</td><td>84.7</td><td>83.4</td><td>77.6</td><td>62.4</td><td>77.0</td></tr><tr><td>\( 24\times 224^2 \)</td><td>84.3</td><td>82.6</td><td>76.4</td><td>60.4</td><td>75.9</td></tr><tr><td>Avg</td><td>84.7</td><td>83.6</td><td>79.4</td><td>67.2</td><td>-</td></tr><tr><td>Max</td><td>85.0</td><td>84.3</td><td>81.9</td><td>72.8</td><td>-</td></tr><tr><td rowspan="9">(3072, 2048, 1024)Flux-Multi Tuned</td><td>\( 2\times 224^2 \)</td><td>∅</td><td>∅</td><td>∅</td><td>72.2</td><td>72.2</td></tr><tr><td>\( 4\times 224^2 \)</td><td>∅</td><td>∅</td><td>81.0</td><td>79.3</td><td>80.2</td></tr><tr><td>\( 8\times 224^2 \)</td><td>∅</td><td>84.4</td><td>82.8</td><td>80.3</td><td>82.5</td></tr><tr><td>\( 12\times 224^2 \)</td><td>85.4</td><td>85.2</td><td>83.3</td><td>79.9</td><td>83.5</td></tr><tr><td>\( 16\times 224^2 \)</td><td>85.7</td><td>85.1</td><td>83.5</td><td>79.2</td><td>83.4</td></tr><tr><td>\( 20\times 224^2 \)</td><td>85.7</td><td>85.3</td><td>83.0</td><td>78.9</td><td>83.2</td></tr><tr><td>\( 24\times 224^2 \)</td><td>85.6</td><td>85.0</td><td>82.7</td><td>78.2</td><td>82.9</td></tr><tr><td>Avg</td><td>85.6</td><td>85.0</td><td>82.7</td><td>78.3</td><td>-</td></tr><tr><td>Max</td><td>85.7↑0.7</td><td>85.3↑1.0</td><td>83.5↑1.6</td><td>80.3↑7.5</td><td>-</td></tr></table>
+
+Table 1. Directly use Flux-Tuning with the previsou SOTA InternVideo2-S on K400. Results are reported on K400 using 1clip  $\times$  1crop. It shows that Flux can be used as an advanced augmentation tool directly in supervised tuning scenario.
+
+K700 [10] datasets with duplicates removed. Performance is evaluated using scene-based K400 and motion-intensive SSv2 [23]. We use 1 clip  $\times$  1 crop for test configurations. For flexible sampling, we set the parameters as follows:  $F_{min} = 4$ ,  $F_{max} = 24$ ,  $t_s = 2$ ,  $R_{min} = 168$ ,  $R_{max} = 252$ ,  $r_s = 28$  and  $T_{thres} = (2048, 4096)$  by default.
+
+Flux-Single Pre-training and Tuning. Figure 5 illustrates the performance of three models differing in their pretraining and tuning approaches. The first model, termed the FixRes Distilled FixRes Tuned model, employs a fixed resolution of  $8 \times 224^2$  during both the pre-training stage and fine-tuning stage, the same as the original setting to get InternVideo2-S. We evaluate this tuned model using a fixed input token count of 2048 by our token selector on
+
+![](images/488e7e274f11ec5e5ce53767c52d7ad8096e169d076a1e4cdddf7b6cebbecbf7.jpg)  
+Figure 5. Comparison between different training methods on K400 using a fixed number of 2048 tokens. Note the three lines and all the points share similar training and inference costs. The shaded part shows results for the AnyRes Distilled AnyRes Tuned model with spatial resolution in range (196, 252), while others use a fixed spatial resolution at 224.
+
+<table><tr><td>Mask Type</td><td>K400</td><td>w/ TO</td><td>SSv2</td><td>w/ TO</td></tr><tr><td>∅ (single res)</td><td>78.4</td><td>78.4</td><td>65.4</td><td>65.4</td></tr><tr><td>Random</td><td>78.6</td><td>79.0 (↑0.6)</td><td>65.3</td><td>65.9 (↑0.5)</td></tr><tr><td>Tube</td><td>78.8</td><td>80.0 (↑1.6)</td><td>65.7</td><td>66.7 (↑1.3)</td></tr><tr><td>Dynamic(L1)</td><td>78.7</td><td>79.8 (↑1.4)</td><td>65.7</td><td>66.6 (↑1.2)</td></tr><tr><td>Dynamic(L2)</td><td>78.8</td><td>80.0 (↑1.6)</td><td>65.8</td><td>66.7 (↑1.3)</td></tr><tr><td>Group-Dynamic(L2)-2</td><td>78.8</td><td>80.2 (↑1.8)</td><td>66.0</td><td>67.0 (↑1.6)</td></tr><tr><td>Group-Dynamic(L2)-4</td><td>79.2</td><td>80.3 (↑1.9)</td><td>66.3</td><td>67.3 (↑1.9)</td></tr></table>
+
+Table 2. Ablation on using token selection strategies We validate the effect of different token selection methods on Flux-Single-Pre-training and Tuning on ViT. A group size of 4 works well.
+
+the K400 dataset, which corresponds to the number of input tokens in the standard  $8 \times 224^{2}$  configuration, but with varied frame counts to assess the model's capability to leverage additional temporal information in fixed-number tokens for enhanced performance. Thus, models trained and tuned with fixed sampling grid can hardly harvest costless performance gain with Token Optimization.
+
+In contrast, the AnyRes Distilled FixRes Tuned model utilizes Flux-UMT-Single upon the original setting. Through Flux token selection, we maintain consistent pretraining computational costs by regulating a fixed quota of 2048 input tokens. The tuning method also remains unchanged. The diversified features from the teacher by the flexible sampling accumulate more valued representations for the student, gaining a  $+0.7\%$  improvement at the original  $8 \times 224^2$  test setting. Notably, the new model exhibits greater robustness in processing a fixed number of tokens from larger spatiotemporal resolution, achieving another  $+0.3\%$  gain with Token Optimization costlessly.
+
+We further test tuning the any-res trained model using Flux-FT with a fixed input token number of 2048 on K400, as termed AnyRes Distilled AnyRes Tuned in the figure. As
+
+<table><tr><td>Method &amp; Arch</td><td>K400</td><td>w/ TO</td><td>SSv2</td><td>w/ TO</td></tr><tr><td>Vanilla + ViT</td><td>78.4</td><td>78.4</td><td>65.4</td><td>65.4</td></tr><tr><td>Vanilla + FluxViT</td><td>79.3</td><td>79.6 (↑1.2)</td><td>66.0</td><td>66.4 (↑1.0)</td></tr><tr><td>Flux-Single + ViT</td><td>79.2</td><td>80.3 (↑1.9)</td><td>66.3</td><td>67.3 (↑1.9)</td></tr><tr><td colspan="5">With new positional embeddings</td></tr><tr><td>w/ RoPE</td><td>79.5</td><td>80.7 (↑0.4)</td><td>66.5</td><td>67.5 (↑0.2)</td></tr><tr><td>w/ GPE</td><td>79.4</td><td>80.5 (↑0.2)</td><td>66.4</td><td>67.4 (↑0.1)</td></tr><tr><td>w/ LPE</td><td>79.7</td><td>81.0 (↑0.7)</td><td>66.8</td><td>68.3 (↑1.0)</td></tr><tr><td>w/ GLPE</td><td>79.9</td><td>81.3 (↑1.0)</td><td>67.0</td><td>68.6 (↑1.3)</td></tr><tr><td colspan="5">With DPN</td></tr><tr><td>w/ DPN</td><td>79.8</td><td>81.2 (↑0.9)</td><td>66.9</td><td>68.4 (↑1.1)</td></tr></table>
+
+Combining the two modules  
+
+<table><tr><td>Flux-Single + FluxViT</td><td>80.5</td><td>81.7 (↑3.3)</td><td>67.6</td><td>69.3 (↑3.9)</td></tr><tr><td>Flux-Multi + FluxViT</td><td>81.4</td><td>82.4 (↑4.0)</td><td>68.4</td><td>70.0 (↑4.6)</td></tr></table>
+
+Table 3. Results with  $8 \times 224^2$  (and TO w/ 2048 tokens, which is the same token count as in  $8 \times 224^2$ ) on K400 and SSv2. ( $\uparrow$ ) for absolute improvement and ( $\uparrow$ ) for relative gain. Vanilla means PT and FT without Flux augmentation.  
+
+<table><tr><td rowspan="2">Settings</td><td colspan="2">8×2242</td><td colspan="2">2048 + TO</td></tr><tr><td>K400</td><td>SSv2</td><td>K400</td><td>SSv2</td></tr><tr><td>Baseline</td><td>80.5</td><td>67.6</td><td>81.7</td><td>69.3</td></tr><tr><td>Change Thres to (2048, 6144)</td><td>80.1</td><td>67.2</td><td>81.2</td><td>68.7</td></tr><tr><td>w/o varied spatial resolution</td><td>80.5</td><td>67.7</td><td>81.4</td><td>69.1</td></tr><tr><td>enlarge Fmax to 32</td><td>80.4</td><td>67.4</td><td>81.6</td><td>69.1</td></tr><tr><td>enlarge Resmax to 336</td><td>80.2</td><td>67.3</td><td>81.4</td><td>69.0</td></tr></table>
+
+Table 4. Ablations on hyper-parameter on sampling designs for Flux-Single training with FluxViT. Most settings regarding flexible sampling cause only minor influences except  $T_{thres}$ .  
+
+<table><tr><td rowspan="2">Pre-training Length</td><td rowspan="2">Fine-tuning Length</td><td colspan="3">Test Length</td></tr><tr><td>2048</td><td>1024</td><td>512</td></tr><tr><td>∅(w/o Flux)</td><td>∅</td><td>79.3</td><td>74.7</td><td>62.1</td></tr><tr><td>Single</td><td>Single</td><td>80.5</td><td>77.4</td><td>65.8</td></tr><tr><td>Single</td><td>Multi(w/ align)</td><td>80.3</td><td>79.0</td><td>75.0</td></tr><tr><td>Multi</td><td>Single</td><td>81.0</td><td>78.8</td><td>70.2</td></tr><tr><td>Multi</td><td>Multi</td><td>80.9</td><td>79.1</td><td>73.3</td></tr><tr><td>Multi</td><td>Multi(w/ align)</td><td>81.4</td><td>80.3</td><td>76.6</td></tr></table>
+
+shown, this tuning method can harvest a  $+1.9\%$  top-1 accuracy gain on K400 compared with the normal distilled and normal tuned method while nearly bringing no computation increment throughout pre-training and fine-tuning.
+
+Table 1 further shows that only combining Flux-Tuning and Token Optimization on a competitive model pre-trained with standard setting can achieve non-trivial gain, highlighting that the sampling and selection module in Flux can effectively serve as novel augmentation tools.
+
+Token Selection Module. We test four token selection methods for Flux-Single, including random, tube, dynamic
+
+Table 5. Ablation on using varied input lengths in training FluxViT on K400. Results are tested with fixed  $8 \times 224^2$  input but varied test token lengths based on our selector.  
+
+<table><tr><td>Model</td><td>Extra Data</td><td>#P(M)</td><td>GFLOPs</td><td>Top-1</td></tr><tr><td>TimeSformer-L [5]</td><td>-</td><td>121</td><td>2380×3</td><td>80.7</td></tr><tr><td>VideoSwin-L [50]</td><td>IN-21K</td><td>197</td><td>604×12</td><td>83.1</td></tr><tr><td>VideoMAE-L [66]</td><td>-</td><td>305</td><td>3958×21</td><td>86.1</td></tr><tr><td>CoVeR-L [87]</td><td>JFT-3B+SMI</td><td>431</td><td>5860×3</td><td>87.1</td></tr><tr><td>UniFormerV2-L [39]</td><td>CLIP-400M+K710</td><td>354</td><td>12550×6</td><td>90.0</td></tr><tr><td>UMT-L [42]</td><td>K710</td><td>431</td><td>5860×3</td><td>90.6</td></tr><tr><td>VideoMAE2-H [72]</td><td>UnlabeledHybrid</td><td>633</td><td>1192×15</td><td>88.6</td></tr><tr><td>ViViT-H [2]</td><td>JFT-300M</td><td>654</td><td>3981×12</td><td>84.9</td></tr><tr><td>MTV-H [82]</td><td>IN-21K+WTS-60M</td><td>1000+</td><td>6130×12</td><td>89.9</td></tr><tr><td>CoCa-G [85]</td><td>JFT-3B+ALIGN-1.8B</td><td>1000+</td><td>N/A×12</td><td>88.9</td></tr><tr><td>MViTv1-B [19]</td><td>-</td><td>37</td><td>70×5</td><td>80.2</td></tr><tr><td>MViTv2-B [44]</td><td>-</td><td>37</td><td>255×5</td><td>81.2</td></tr><tr><td>ST-MAE-B [21]</td><td>K600</td><td>87</td><td>180×21</td><td>81.3</td></tr><tr><td>VideoMAE-B [66]</td><td>-</td><td>87</td><td>180×15</td><td>81.5</td></tr><tr><td>VideoSwin-B [50]</td><td>IN-21k</td><td>88</td><td>282×12</td><td>82.7</td></tr><tr><td>UniFormer-B [40]</td><td>IN-1k</td><td>50</td><td>259×12</td><td>83.0</td></tr><tr><td>UMT-B [42]</td><td>K710</td><td>87</td><td>180×12</td><td>87.4</td></tr><tr><td>InternVideo2-B [77]</td><td>K710+MASH</td><td>96</td><td>440×12</td><td>88.4</td></tr><tr><td rowspan="2">FluxViT-Be200</td><td rowspan="2">-</td><td rowspan="2">97</td><td>440×12</td><td>88.7 89.4</td></tr><tr><td>49×12</td><td>84.0 86.7</td></tr><tr><td rowspan="4">FluxViT-Be100</td><td rowspan="4">K710+MASH</td><td rowspan="4">97</td><td>440×12</td><td>89.6 90.0</td></tr><tr><td>255×12</td><td>89.3 89.7</td></tr><tr><td>108×12</td><td>87.3 88.9</td></tr><tr><td>49×12</td><td>84.7 87.4</td></tr><tr><td>UniFormer-S [40]</td><td>IN-1k</td><td>21</td><td>42×4</td><td>80.8</td></tr><tr><td>MViTv2-S [44]</td><td>-</td><td>35</td><td>64×5</td><td>81.0</td></tr><tr><td>VideoMAE-S [66]</td><td>-</td><td>22</td><td>57×15</td><td>79.0</td></tr><tr><td>VideoMAE2-S [72]</td><td>-</td><td>22</td><td>57×15</td><td>83.7</td></tr><tr><td>InternVideo2-S [77]</td><td>K710+MASH</td><td>23</td><td>154×12</td><td>85.8</td></tr><tr><td rowspan="2">FluxViT-Se200</td><td rowspan="2">-</td><td rowspan="2">24</td><td>154×12</td><td>86.4 87.3</td></tr><tr><td>13×12</td><td>79.7 84.0</td></tr><tr><td rowspan="4">FluxViT-Se100</td><td rowspan="4">K710+MASH</td><td rowspan="4">24</td><td>154×12</td><td>87.7 88.0</td></tr><tr><td>83×12</td><td>87.3 87.7</td></tr><tr><td>32×12</td><td>84.7 86.6</td></tr><tr><td>13×12</td><td>80.1 84.7</td></tr></table>
+
+Table 6. Comparison with the state-of-the-art methods with on scene-related Kinetics-400. #P is short for the number of parameters. The blue values of FluxViT show results using larger spatiotemporal resolutions but keeping fixed input token count to 3072, 2048, 1024, and 512 respectively, corresponding to the four GFLOPs listed. SMI is short for the train set of SSv2, MiT and ImageNet and MASH for MiT, ANet, SSv2 and HACS.
+
+(as also directly used in [31]) and our group-dynamic method. We further ablate the token dynamics measurement and the sparse group size. Table 2 shows that our group-dynamic strategy with token dynamics measured by L2-distance of tokens within adjacent frames in sparsely divided four groups is the most robust among the token-selection methods. More advanced token selectors can be utilized for better results but with increased complexity, cost, and tedious hyperparameters.
+
+FluxViT Modules. Table 3 analyzes the impact of our proposed plug-in modules. The inclusion of Global-Local positional embedding can strengthen the model's robustness when processing sparse tokens derived from larger spatiotemporal resolutions. We compare our GLPE with ROPE [63] within our Flux scheme and show better results
+
+<table><tr><td>Model</td><td>Extra Data</td><td>GFLOPs</td><td>Top-1</td><td>Top-5</td></tr><tr><td>TimeSformer-L [5]</td><td>IN-21k</td><td>2380×3</td><td>62.3</td><td>-</td></tr><tr><td>MViTv1-B [19]</td><td>K400</td><td>455×3</td><td>67.7</td><td>70.9</td></tr><tr><td>MViTv2-B [44]</td><td>K400</td><td>255×3</td><td>70.5</td><td>92.7</td></tr><tr><td>VideoMAE-B [66]</td><td>K400</td><td>180×6</td><td>69.7</td><td>92.3</td></tr><tr><td>VideoMAE-L [66]</td><td>K400</td><td>596×6</td><td>74.0</td><td>94.6</td></tr><tr><td>UniFormerV2-B [39]</td><td>CLIP-400M</td><td>375×3</td><td>70.7</td><td>93.2</td></tr><tr><td>UniFormerV2-L [39]</td><td>CLIP-400M</td><td>1718×3</td><td>73.0</td><td>94.5</td></tr><tr><td>UMT-B [42]</td><td>K710</td><td>180×6</td><td>70.8</td><td>92.4</td></tr><tr><td>InternVideo2-B [77]</td><td>K710+MASH</td><td>253×6</td><td>73.5</td><td>94.4</td></tr><tr><td rowspan="4">FluxViT-B</td><td></td><td>440×6</td><td>75.3 75.6</td><td>95.1 95.1</td></tr><tr><td></td><td>255×6</td><td>75.1 75.5</td><td>94.9 95.1</td></tr><tr><td></td><td>108×6</td><td>72.0 75.1</td><td>93.3 94.8</td></tr><tr><td></td><td>49×6</td><td>56.8 73.9</td><td>84.8 94.4</td></tr><tr><td>UniFormer-S [40]</td><td>IN-1K</td><td>42×3</td><td>67.7</td><td>91.4</td></tr><tr><td>VideoMAE-S [66]</td><td>K600</td><td>57×6</td><td>66.8</td><td>90.3</td></tr><tr><td>InternVideo2-S [77]</td><td>K710+MASH</td><td>83×6</td><td>71.5</td><td>93.4</td></tr><tr><td rowspan="4">FluxViT-S</td><td></td><td>154×6</td><td>73.4 73.8</td><td>94.1 94.1</td></tr><tr><td></td><td>83×6</td><td>72.9 73.4</td><td>94.0 94.1</td></tr><tr><td></td><td>32×6</td><td>70.0 72.5</td><td>93.4 93.8</td></tr><tr><td></td><td>13×6</td><td>55.3 70.9</td><td>83.7 93.1</td></tr></table>
+
+with LPE illustrated in the Method section. Furthermore, adding a dual patch norm module optimizes the performance gains in TO. Combining the two modules achieves  $+3.3\%$  and  $+3.9\%$  on K400 and SSv2 respectively with TO.
+
+Hyper-parameters of flexi-sampling Table 4 gives ablation studies on the results of different hyper-parameters regarding sampling. Most settings regarding flexible sampling cause only minor influences except  $T_{thres}$ . A proper scale of the flexible space is the most powerful.
+
+Flux-Multi training. We compare our proposed method of co-training with three input numbers(2048, 1024, 512) in both pretraining and tuning in Table 5 and also validate the tuning effects on well-pretrained InternVideo2-S in table 1(3072, 2048, 1024). As shown, pre-training with multiple token numbers can not only boost performance under standard settings but also increase model consistency in input token sparsity. Tuning with different token numbers with a smoothed-L1-loss based self-distillation mechanism can further enhance such alignment consistently.
+
+# 4.2. Single Modality Results
+
+We scale up the training data using the K-MASH[77] dataset of 1.1 million samples, aligning with the data employed in the Internvideo2-Distilled series models, including K710, SSv2[23], ANet[28], HACS[89], and MiT[56]. We train the FluxViT model using a total batch size of 2048 for 100 epochs. FluxViT's performance is validated using scene-based K400, motion-intensive SSv2, and long-term COIN with Token Optimization.
+
+K400. Table 6 reports our results compared with the previous solutions on K400. We set 3072, 2048, and 1024
+
+Table 7. Comparison with the state-of-the-art methods with on motion-intensive SSv2. Our model achieves far better results.  
+
+<table><tr><td>Model</td><td>e2e</td><td>BackBone</td><td>Top-1</td></tr><tr><td>Distant Supervision [47]</td><td>X</td><td>TimeSformer</td><td>90.0</td></tr><tr><td>ViS4mer [32]</td><td>X</td><td>Swin-B</td><td>88.4</td></tr><tr><td>Turbof32 [25]</td><td>✓</td><td>VideoMAE-B</td><td>87.5</td></tr><tr><td>VideoMamba64 [43]</td><td>✓</td><td>VideoMamba-S</td><td>88.7</td></tr><tr><td>VideoMamba64 [43]</td><td>✓</td><td>VideoMamba-M</td><td>90.4</td></tr><tr><td>InternVideo2f12 [77]</td><td>✓</td><td>InternVideo2-S</td><td>90.0</td></tr><tr><td>MA-LMM [26]</td><td>✓</td><td>MLLM</td><td>93.2</td></tr><tr><td>HERMES [33]</td><td>✓</td><td>MLLM</td><td>93.5</td></tr><tr><td>FluxViT3072</td><td>✓</td><td>FluxViT-S</td><td>91.8 92.1</td></tr><tr><td>FluxViT2048</td><td>✓</td><td>FluxViT-S</td><td>91.5 91.9</td></tr><tr><td>FluxViT1024</td><td>✓</td><td>FluxViT-S</td><td>89.8 91.0</td></tr><tr><td>FluxViT3072</td><td>✓</td><td>FluxViT-B</td><td>93.9 94.1</td></tr><tr><td>FluxViT2048</td><td>✓</td><td>FluxViT-B</td><td>93.7 93.9</td></tr><tr><td>FluxViT1024</td><td>✓</td><td>FluxViT-B</td><td>92.5 93.2</td></tr></table>
+
+Table 8. Comparison with the state-of-the-art on long-form video classification COIN dataset. We report the results based on our preset token number, with the left line using unmasked 12, 8, 4 frames, and 224 spatial resolution while the blue values show results that can be achieved using more informative tokens.
+
+as our token numbers in Flux-Multi-Tuning. For our FluxViT-S, we report  $88.0\% (+2.2\%)$  on K400 compared with InternVideo2-S, which is the previous SOTA solution, while still achieving  $84.7\%$  with nearly the same computation as the lightweight network UniFormer [40] but with competitive accuracy. For fair comparison without K-MASH, we pretrain FluxViT using only K400 in Flux-UMT, which also shows much better results.
+
+SSv2. Table 7 shows our model's performance in dealing with motion-intensive video understanding tasks. Our FluxViT-S and FluxViT-B models set new state-of-the-art performance on SSv2 with either standard or limited computation cost. For FluxViT-B, we achieve  $75.6\%$  with standard computation while still achieving  $75.1\%$  with only  $25\%$  cost of the standard setting. Previous work [88] instead gets a huge performance drop using 2 frames, which corresponds to the least computation listed.
+
+COIN. Table 8 shows our model's performance in dealing with long-form video understanding tasks. Our FluxViT-S and FluxViT-B models set new state-of-the-art performance on COIN with either standard or limited computation cost.
+
+# 4.3. Multi Modality Retrieval Results
+
+We use the pre-trained FluxViT with the CLIP [60] framework and our Flux method to train a video clip model using 27M corpus, far lower than most baseline models, including 25M coarse level caption data: Webvid10M [3], CC3M [62], COCO [46], VG [35], SBU [57], CC12M [11] and 2M high-quality data: S-MiT [55], InternVid-2M-Recap [76]. We leverage MobileClip-B [67] as the text encoder and only use the vanilla VTC loss, which is short for Video-Text Contrastive loss in training. We only unfreeze the ViT projector for the first stage using the 25M coarse
+
+<table><tr><td>Model</td><td>MSR</td><td>DDM</td><td>ANet</td><td>LSMDC</td><td>MSVD</td></tr><tr><td>Internvideo2-S2048 [77]</td><td>35.6</td><td>33.7</td><td>34.5</td><td>14.7</td><td>41.8</td></tr><tr><td>Frozen-B [3]</td><td>18.7</td><td>20.2</td><td>-</td><td>-</td><td>-</td></tr><tr><td>VIOLET-B [22]</td><td>25.9</td><td>23.5</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Singularity-B [37]</td><td>34.0</td><td>37.1</td><td>30.6</td><td>-</td><td>-</td></tr><tr><td>OmniVL-B [69]</td><td>34.6</td><td>33.3</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CLIP4Clip-B [53]</td><td>30.6</td><td>-</td><td>-</td><td>13.6</td><td>36.2</td></tr><tr><td>UMT-B [42]</td><td>35.2</td><td>41.2</td><td>35.5</td><td>19.1</td><td>42.3</td></tr><tr><td>Internvideo2-B2048 [77]</td><td>40.3</td><td>40.3</td><td>41.5</td><td>18.7</td><td>49.1</td></tr><tr><td>VINDLU-L [14]</td><td>32.0</td><td>36.9</td><td>30.9</td><td>-</td><td>-</td></tr><tr><td>InternVideo-L [75]</td><td>40.7</td><td>31.5</td><td>30.7</td><td>17.6</td><td>43.4</td></tr><tr><td>UMT-L [42]</td><td>40.7</td><td>48.6</td><td>41.9</td><td>24.9</td><td>49.0</td></tr><tr><td>ViClip-L [76]</td><td>42.4</td><td>18.4</td><td>15.1</td><td>20.1</td><td>49.1</td></tr><tr><td>InternVideo2-L [77]</td><td>42.1</td><td>42.8</td><td>43.6</td><td>21.4</td><td>-</td></tr><tr><td>LanguageBind-L [91]</td><td>42.8</td><td>39.7</td><td>38.4</td><td>-</td><td>54.1</td></tr><tr><td>LanguageBind-H [91]</td><td>44.8</td><td>39.9</td><td>41.0</td><td>-</td><td>53.9</td></tr><tr><td>VideoCoCa-G [83]</td><td>34.3</td><td>-</td><td>34.5</td><td>-</td><td>-</td></tr><tr><td>VideoPrism-G [83]</td><td>39.7</td><td>-</td><td>52.7</td><td>-</td><td>-</td></tr><tr><td>VAST-G [13]</td><td>49.3</td><td>55.5</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="2">FluxViT-S2048</td><td>44.4</td><td>48.3</td><td>52.4</td><td>20.8</td><td>49.4</td></tr><tr><td>45.0</td><td>49.3</td><td>52.4</td><td>22.4</td><td>49.7</td></tr><tr><td rowspan="2">FluxViT-S1024</td><td>42.2</td><td>45.4</td><td>47.2</td><td>18.7</td><td>48.1</td></tr><tr><td>44.5</td><td>49.0</td><td>50.3</td><td>20.5</td><td>48.5</td></tr><tr><td rowspan="2">FluxViT-S512</td><td>36.8</td><td>38.5</td><td>38.2</td><td>17.7</td><td>45.5</td></tr><tr><td>40.5</td><td>45.8</td><td>44.7</td><td>19.0</td><td>46.9</td></tr><tr><td rowspan="2">FluxViT-B2048</td><td>49.8</td><td>52.2</td><td>56.6</td><td>23.7</td><td>53.8</td></tr><tr><td>49.9</td><td>53.5</td><td>56.7</td><td>25.4</td><td>54.2</td></tr><tr><td rowspan="2">FluxViT-B1024</td><td>48.0</td><td>48.8</td><td>51.8</td><td>22.6</td><td>52.8</td></tr><tr><td>49.1</td><td>53.0</td><td>54.8</td><td>24.1</td><td>53.4</td></tr><tr><td rowspan="2">FluxViT-B512</td><td>42.6</td><td>42.9</td><td>42.8</td><td>20.1</td><td>50.7</td></tr><tr><td>47.2</td><td>49.8</td><td>50.3</td><td>22.8</td><td>52.1</td></tr></table>
+
+caption data for 3 epochs. Then we unfreeze all the modules for the second stage using the 2M high-quality data for one epoch to fully boost the ViT's capacity. By default, we train the clip model with a batch size of 4096. Also, we train the clip model with three input token numbers, 2048, 1024, and 512, and utilize smoothed-L1-loss on the final aggregated vision features to perform self-distillation and compute contrastive loss for each vision feature of the three numbers. Table 9 indicates that the clip model trained with our FluxViT method outperforms the Intervideo2-Series model with a large margin and further surpasses most of the top-performing models in Large or even Giant scale. Results on zero-shot action recognition results are included in Appendix for full validation of FluxViT-CLIP.
+
+Table 9. Zero-shot text-to-video retrieval on MSRVTT ("MSR"), DiDeMo ("DDM"), AcitivityNet ("ANet"), LSMDC, and MSVD. We only report the R@1 accuracy. The upper line regarding FluxViT shows results with non-masked  $8 \times 224^2$ ,  $4 \times 224^2$  and  $2 \times 224^2$  input setting as indicated by the token count while each lower bold line shows results further using more informative tokens. We employ Dual Softmax Loss for the results.  
+
+<table><tr><td>Encoder</td><td>#Tokens</td><td>w/ TO</td><td>MVbench</td><td>Dream1k-F1</td></tr><tr><td>Clip-L [60]</td><td>8×256</td><td>X</td><td>45.6</td><td>28.4</td></tr><tr><td>SigLIP336-L [86]</td><td>8×576</td><td>X</td><td>46.7</td><td>29.2</td></tr><tr><td>InternVideo2-L [77]</td><td>8×224</td><td>X</td><td>47.0</td><td>28.7</td></tr><tr><td>SigLIP336-L [86]</td><td>4×576</td><td>X</td><td>44.5</td><td>25.4</td></tr><tr><td>UMT-L [42]</td><td>4×256</td><td>X</td><td>45.0</td><td>24.6</td></tr><tr><td rowspan="3">FluxViT-L</td><td>8×256</td><td></td><td>48.3</td><td>29.0</td></tr><tr><td>4×256</td><td>X</td><td>46.9</td><td>27.9</td></tr><tr><td>2×256</td><td></td><td>46.0</td><td>25.6</td></tr><tr><td rowspan="3">FluxViT-L</td><td>2048</td><td></td><td>49.0 (↑0.7)</td><td>29.5 (↑0.5)</td></tr><tr><td>1024</td><td>✓</td><td>47.7 (↑0.8)</td><td>28.5 (↑0.6)</td></tr><tr><td>512</td><td></td><td>47.6 (↑1.6)</td><td>27.5 (↑1.9)</td></tr></table>
+
+Table 10. Results on Chat-Centric benchmarks MVbench (General perception) and Dream1k(Fine-grained caption). Models are trained in a multimodal 'linear prob' setting where both the Encoder and the LLM are frozen. #Tokens for the number of visual tokens by the vision encoder.
+
+# 4.4. Chat-Centric Evaluation Results
+
+We scale the pre-trained model size to FluxViT-Large and evaluate its performance under the VideoChat framework. We employ a single-layer MLP projector between ViT and an LLM(Qwen2-7B[73]) and train only the projector to ensure an unbiased evaluation of the ViT's performance, termed 'linear probe' setting in multimodal scenarios. Such a probing strategy is widely used in the Stage-1 training of multi-modal chat models. We utilize a large-scaled trainset including Llava-558K[49], S-MiT, 700k filtered subset of WebVid-10M, VidLN[68], and SSv2-open-ended. We then compare the models' performance on two benchmarks: MVbench, which assesses general spatiotemporal perception capabilities, and Dream1k, which evaluates the model's fine-grained ability to generate detailed captions. The other models tested include CLIP-L, SigLIP-L, and UMT-L, which are widely used in Video MLLMs. Given that ViT and LLM remain frozen, we maintain the original input settings as their pretraining phases to ensure comparability. We will scale the corpus size and fully tune the chat model for full performance in future work.
+
+# 5. Conclusion and Future work
+
+We present a token optimization process to maximize information in limited tokens within any budget. It integrates seamlessly with mainstream frameworks, offering a simple and scalable method to enhance real-world applications and facilitate future video foundation models. Future work could explore more advanced token selection methods for improvement, as the group setting can ensure full video coverage but not fully resist consistent camera motions. We test advanced Vid-TLDR [15] in Appendix, but with increased cost, tedious hyper-parameter tuning, and unstable improvement. For all current tasks tested, including fine-grained captioning, our method is still simple and effective.
+
+# Acknowledgement
+
+This work is supported by the National Key R&D Program of China (No. 2022ZD0160900), Jiangsu Frontier Technology Research and Development Program (No. BF2024076), and the Collaborative Innovation Center of Novel Software Technology and Industrialization.
+
+# References
+
+[1] Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. Localizing moments in video with natural language. In ICCV, 2017. 2  
+[2] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid. Vivit: A video vision transformer. In ICCV, 2021. 6  
+[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, 2021. 7, 8  
+[4] Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: Bert pre-training of image transformers. In ICLR, 2021. 3  
+[5] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, 2021. 6, 7  
+[6] Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim M. Alabdulmohsin, and Filip Pavetic. Flexivit: One model for all patch sizes. In CVPR, 2022. 2  
+[7] Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han. Once-for-all: Train one network and specialize it for efficient deployment. In ICLR, 2019. 2  
+[8] Mu Cai, Jianwei Yang, Jianfeng Gao, and Yong Jae Lee. Matryoshka multimodal models. ArXiv, abs/2405.17430, 2024. 2  
+[9] João Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, and Andrew Zisserman. A short note about kinetics-600. ArXiv, abs/1808.01340, 2018. 4  
+[10] João Carreira, Eric Noland, Chloe Hillier, and Andrew Zisserman. A short note on the kinetics-700 human action dataset. ArXiv, abs/1907.06987, 2019. 5  
+[11] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In CVPR, 2021. 7  
+[12] David L. Chen and William B. Dolan. Collecting highly parallel data for paraphrase evaluation. In ACL, 2011. 2  
+[13] Sihan Chen, Handong Li, Qunbo Wang, Zijia Zhao, Mingzhen Sun, Xinxin Zhu, and Jing Liu. Vast: A vision-audio-subtitle-text omni-modality foundation model and dataset. In NIPS, 2023. 8  
+[14] Feng Cheng, Xizi Wang, Jie Lei, David J. Crandall, Mohit Bansal, and Gedas Bertasius. Vindlu: A recipe for effective video-and-language pretraining. ArXiv, abs/2212.05051, 2022.8  
+[15] Joonmyung Choi, Sanghyeok Lee, Jaewon Chu, Minhyuk Choi, and Hyunwoo J. Kim. vid-tldr: Training free token
+
+merging for light-weight video transformer. In CVPR, 2024. 1,8  
+[16] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv, abs/1810.04805, 2018.3  
+[17] Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. Unified language model pre-training for natural language understanding and generation. NeurIPS, 32, 2019. 3  
+[18] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 3  
+[19] Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li, Zhicheng Yan, Jitendra Malik, and Christoph Feichtenhofer. Multiscale vision transformers. In ICCV, 2021. 6, 7  
+[20] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast networks for video recognition. In ICCV, 2019. 1  
+[21] Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, and Kaiming He. Masked autoencoders as spatiotemporal learners. NeurIPS, 2022. 3, 6  
+[22] Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, and Zicheng Liu. Violet: End-to-end video-language transformers with masked visual-token modeling. ArXiv, abs/2111.12681, 2021. 8  
+[23] Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al. The" something something" video database for learning and evaluating visual common sense. In ICCV, 2017. 2, 5, 7  
+[24] Jing Gu, Eliana Stefani, Qi Wu, Jesse Thomason, and Xin Wang. Vision-and-language navigation: A survey of tasks, methods, and future directions. In ACL, 2022. 1  
+[25] Tengda Han, Weidi Xie, and Andrew Zisserman. Turbo training with token dropout. In BMVC, 2022. 7  
+[26] Bo He, Hengduo Li, Young Kyun Jang, Menglin Jia, Xuefei Cao, Ashish Shah, Abhinav Shrivastava, and Ser-Nam Lim. Ma-lmm: Memory-augmented large multimodal model for long-term video understanding. In CVPR, 2024. 7  
+[27] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll'ar, and Ross B. Girshick. Masked autoencoders are scalable vision learners. In CVPR, 2022. 3  
+[28] Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. Activitynet: A large-scale video benchmark for human activity understanding. In CVPR, 2015. 2, 7  
+[29] Zejiang Hou, Fei Sun, Yen-Kuang Chen, Yuan Xie, and S. Y. Kung. Milan: Masked image pretraining on language assisted representation. ArXiv, abs/2208.06049, 2022. 3  
+[30] Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, M. Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional
+
+neural networks for mobile vision applications. ArXiv, abs/1704.04861, 2017. 4  
+[31] Sun-Kyoo Hwang, Jaehong Yoon, Youngwan Lee, and Sung Ju Hwang. Everest: Efficient masked video autoencoder by removing redundant spatiotemporal tokens. In ICML, 2022. 1, 3, 6  
+[32] Md. Mohaiminul Islam and Gedas Bertasius. Long movie clip classification with state-space video models. In ECCV, 2022. 7  
+[33] Faure Gueter Josmy, Yeh Jia-Fong, Chen Min-Hung, Su Hung-Ting, Hsu Winston H, and Lai Shang-Hong. Bridging episodes and semantics: A novel framework for long-form video understanding. In ECCV workshops, 2024. 7  
+[34] Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Apostol Natsev, Mustafa Suleyman, and Andrew Zisserman. The kinetics human action video dataset. ArXiv, abs/1705.06950, 2017. 2, 4  
+[35] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV, 2017. 7  
+[36] Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham M. Kakade, Prateek Jain, and Ali Farhadi. Matryoshka representation learning. In NIPS, 2022. 2  
+[37] Jie Lei, Tamara L Berg, and Mohit Bansal. Revealing single frame bias for video-and-language learning. ArXiv, abs/2206.03428, 2022. 8  
+[38] Junnan Li, Dongxu Li, Silvio Savarese, and Steven C. H. Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In ICML, 2022. 1  
+[39] Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Limin Wang, and Y. Qiao. Uniformerv2: Spatiotemporal learning by arming image vits with video uniformer. ArXiv, abs/2211.09552, 2022. 6, 7  
+[40] Kunchang Li, Yali Wang, Gao Peng, Guanglu Song, Yu Liu, Hongsheng Li, and Yu Qiao. Uniformer: Unified transformer for efficient spatial-temporal representation learning. In ICLR, 2022. 6, 7  
+[41] Kunchang Li, Yinan He, Yi Wang, Yizhuo Li, Wen Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. Videochat: Chat-centric video understanding. ArXiv, abs/2305.06355, 2023. 1, 2  
+[42] Kunchang Li, Yali Wang, Yizhuo Li, Yi Wang, Yinan He, Limin Wang, and Yu Qiao. Unmasked teacher: Towards training-efficient video foundation models. In ICCV, 2023, 2, 3, 6, 7, 8  
+[43] Kunchang Li, Xinhao Li, Yi Wang, Yinan He, Yali Wang, Limin Wang, and Yu Qiao. Videomamba: State space model for efficient video understanding. ArXiv, abs/2403.06977, 2024. 7  
+[44] Yanghao Li, Chaoxia Wu, Haoqi Fan, Karttikeya Mangalam, Bo Xiong, Jitendra Malik, and Christoph Feichtenhofer. Im
+
+proved multiscale vision transformers for classification and detection. ArXiv, abs/2112.01526, 2021. 6, 7  
+[45] Ji Lin, Chuang Gan, and Song Han. Tsm: Temporal shift module for efficient video understanding. In ICCV, 2019. 1  
+[46] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 7  
+[47] Xudong Lin, Fabio Petroni, Gedas Bertasius, Marcus Rohrbach, Shih-Fu Chang, and Lorenzo Torresani. Learning to recognize procedural activities with distant supervision. CVPR, 2022. 7  
+[48] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. ArXiv, abs/2310.03744, 2023. 2  
+[49] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In NeurIPS, 2023. 8  
+[50] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. In CVPR, 2022. 6  
+[51] Zuyan Liu, Yuhao Dong, Ziwei Liu, Winston Hu, Jiwen Lu, and Yongming Rao. Oryx mllm: On-demand spatial-temporal understanding at arbitrary resolution. ArXiv, abs/2409.12961, 2024. 2, 3  
+[52] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019. 3  
+[53] Huaishao Luo, Lei Ji, Ming Zhong, Yang Chen, Wen Lei, Nan Duan, and Tianrui Li. Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning. Neurocomputing, 2022. 8  
+[54] Muhammad Maaz, Hanoona Abdul Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. ArXiv, abs/2306.05424, 2023. 1  
+[55] Mathew Monfort and SouYoung Jin. Spoken moments: Learning joint audio-visual representations from video descriptions. In CVPR, 2021. 7  
+[56] Mathew Monfort, Bolei Zhou, Sarah Adel Bargal, Alex Andonian, Tom Yan, Kandan Ramakrishnan, Lisa M. Brown, Quanfu Fan, Dan Gutfreund, Carl Vondrick, and Aude Oliva. Moments in time dataset: One million videos for event understanding. TPAMI, 2020. 7  
+[57] Vicente Ordonez, Girish Kulkarni, and Tamara Berg. Im2text: Describing images using 1 million captioned photographs. In NeurIPS, 2011. 7  
+[58] Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei. Beit v2: Masked image modeling with vector-quantized visual tokenizers. ArXiv, abs/2208.06366, 2022. 3  
+[59] Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Xiang Wang, Yuehuang Wang, Yiliang Lv, Changxin Gao, and Nong Sang. Mar: Masked autoencoders for efficient action recognition. In ACMMM, 2022. 1, 3  
+[60] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual
+
+models from natural language supervision. In ICML, 2021. 3, 7, 8  
+[61] Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Joseph Pal, H. Larochelle, Aaron C. Courville, and Bernt Schiele. Movie description. International Journal of Computer Vision, 2016. 2  
+[62] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In ACL, 2018. 7  
+[63] Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 2024. 6  
+[64] Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, and Jie Zhou. Coin: A large-scale dataset for comprehensive instructional video analysis. In CVPR, 2019. 2  
+[65] Rui Tian, Zuxuan Wu, Qi Dai, Han Hu, Yu Qiao, and Yu-Gang Jiang. Resformer: Scaling vits with multi-resolution training. In CVPR, 2023. 1, 2  
+[66] Zhan Tong, Yibing Song, Jue Wang, and Limin Wang. VideoMAE: Masked autoencoders are data-efficient learners for self-supervised video pre-training. In NeurIPS, 2022. 3, 6, 7  
+[67] Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, and Oncel Tuzel. Mobile-clip: Fast image-text models through multi-modal reinforced training. In CVPR, 2024. 7  
+[68] Paul Voigtlaender, Soravit Changpinyo, Jordi Pont-Tuset, Radu Soricut, and Vittorio Ferrari. Connecting vision and language with video localized narratives. CVPR, 2023. 8  
+[69] Junke Wang, Dongdong Chen, Zuxuan Wu, Chong Luo, Luowei Zhou, Yucheng Zhao, Yujia Xie, Ce Liu, YuGang Jiang, and Lu Yuan. Omnivl: One foundation model for image-language and video-language tasks. ArXiv, abs/2209.07526, 2022. 8  
+[70] Jiawei Wang, Liping Yuan, Yuchen Zhang, and Haomiao Sun. Tarsier: Recipes for training and evaluating large video description models. arXiv, abs/2407.00634, 2024. 2  
+[71] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal segment networks: Towards good practices for deep action recognition. In ECCV, 2016. 1  
+[72] Limin Wang, Bingkun Huang, Zhiyu Zhao, Zhan Tong, Yi-nan He, Yi Wang, Yali Wang, and Yu Qiao. Videomae v2: Scaling video masked autoencoders with dual masking. In CVPR, 2023. 3, 6  
+[73] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. ArXiv, abs/2409.12191, 2024. 2, 8  
+[74] Rui Wang, Dongdong Chen, Zuxuan Wu, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Yu-Gang Jiang, Luowei Zhou, and Lu Yuan. Bevt: Bert pretraining of video transformers. CVPR, 2022. 3
+
+[75] Yi Wang, Kunchang Li, Yizhuo Li, Yinan He, Bingkun Huang, Zhiyu Zhao, Hongjie Zhang, Jilan Xu, Yi Liu, Zun Wang, Sen Xing, Guo Chen, Junting Pan, Jiashuo Yu, Yali Wang, Limin Wang, and Yu Qiao. *Internvideo: General video foundation models via generative and discriminative learning.* ArXiv, abs/2212.03191, 2022. 8  
+[76] Yi Wang, Yinan He, Yizhuo Li, Kunchang Li, Jiashuo Yu, Xin Jian Ma, Xinyuan Chen, Yaohui Wang, Ping Luo, Ziwei Liu, Yali Wang, Limin Wang, and Y. Qiao. Internvid: A large-scale video-text dataset for multimodal understanding and generation. ArXiv, 2023. 7, 8  
+[77] Yi Wang, Kunchang Li, Xinhao Li, Jiashuo Yu, Yinan He, Chenting Wang, Guo Chen, Baoqi Pei, Ziang Yan, Rongkun Zheng, Jilan Xu, Zun Wang, Yansong Shi, Tianxiang Jiang, Songze Li, Hongjie Zhang, Yifei Huang, Yu Qiao, Yali Wang, and Limin Wang. Internvideo2: Scaling foundation models for multimodal video understanding. In ECCV, 2024. 1, 2, 3, 4, 6, 7, 8  
+[78] Zun Wang, Jialu Li, Yicong Hong, Yi Wang, Qi Wu, Mohit Bansal, Stephen Gould, Hao Tan, and Yu Qiao. Scaling data generation in vision-and-language navigation. In ICCV, 2023. 1  
+[79] Longhui Wei, Lingxi Xie, Wen gang Zhou, Houqiang Li, and Qi Tian. Mvp: Multimodality-guided visual pre-training. In ECCV, 2022. 3  
+[80] Chao-Yuan Wu, Ross Girshick, Kaiming He, Christoph Feichtenhofer, and Philipp Krahenbuhl. A multigrid method for efficiently training video models. In CVPR, 2020. 2  
+[81] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In CVPR, 2016. 2  
+[82] Shen Yan, Xuehan Xiong, Anurag Arnab, Zhichao Lu, Mi Zhang, Chen Sun, and Cordelia Schmid. Multiview transformers for video recognition. In CVPR, 2022. 6  
+[83] Shen Yan, Tao Zhu, Zirui Wang, Yuan Cao, Mi Zhang, Soham Ghosh, Yonghui Wu, and Jiahui Yu. Video-text modeling with zero-shot transfer from contrastive captioners. ArXiv, abs/2212.04979, 2022. 8  
+[84] Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Ruoming Pang, and Quoc Le. Bignas: Scaling up neural architecture search with big single-stage models. In ECCV, 2020. 2  
+[85] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. Transactions on Machine Learning Research, 2022. 6  
+[86] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. CVPR, 2023. 8  
+[87] Bowen Zhang, Jiahui Yu, Christopher Fifty, Wei Han, Andrew M. Dai, Ruoming Pang, and Fei Sha. Co-training transformer with videos and images improves action recognition. ArXiv, abs/2112.07175, 2021. 6  
+[88] Yitian Zhang, Yue Bai, Chang Liu, Huan Wang, Sheng Li, and Yun Fu. Frame flexible network. In CVPR, 2023. 1, 2, 7  
+[89] Hang Zhao, Antonio Torralba, Lorenzo Torresani, and Zhicheng Yan. Hacs: Human action clips and segments
+
+dataset for recognition and temporal localization. In ICCV, 2019. 7  
+[90] Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Loddon Yuille, and Tao Kong. ibot: Image bert pre-training with online tokenizer. ArXiv, abs/2111.07832, 2021. 3  
+[91] Bin Zhu, Bin Lin, Munan Ning, Yang Yan, Jiaxi Cui, Wang HongFa, Yatian Pang, Wenhao Jiang, Junwu Zhang, Zong-wei Li, Cai Wan Zhang, Zhifeng Li, Wei Liu, and Li Yuan. Languagebind: Extending video-language pretraining to n-modality by language-based semantic alignment. In ICLR, 2024. 8

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+# MamTiff-CAD: Multi-Scale Latent Diffusion with Mamba+ for Complex Parametric Sequence
+
+Liyuan Deng $^{1}$  Yunpeng Bai $^{2}$  Yongkang Dai $^{1}$  Xiaoshui Huang $^{3}$  Hongping Gan $^{1}$  Dongshuo Huang $^{1}$  Hao Jiacheng $^{4}$  Yilei Shi $^{1*}$ $^{1}$ Northwestern Polytechnical University  $^{2}$ National University of Singapore  
+ $^{3}$ Shanghai Jiao Tong University  $^{4}$ Nanchang University
+
+{dly, daiyongkang, ganhongping, huangdongshuo, yilei_shi}@mail.nwu.edu.cn  
+bai-yunpeng99@u.nus.edu huangxiaoshui@sjtu.edu.cn haojiacheng@email.ncu.edu.cn
+
+# Abstract
+
+Parametric Computer-Aided Design (CAD) is crucial in industrial applications, yet existing approaches often struggle to generate long sequence parametric commands due to complex CAD models' geometric and topological constraints. To address this challenge, we propose MamTiff-CAD, a novel CAD parametric command sequences generation framework that leverages a Transformer-based diffusion model for multi-scale latent representations. Specifically, we design a novel autoencoder that integrates Mamba+ and Transformer, to transfer parameterized CAD sequences into latent representations. The Mamba+ block incorporates a forget gate mechanism to effectively capture long-range dependencies. The non-autoregressive Transformer decoder reconstructs the latent representations. A diffusion model based on multi-scale Transformer is then trained on these latent embeddings to learn the distribution of long sequence commands. In addition, we also construct a dataset that consists of long parametric sequences, which is up to 256 commands for a single CAD model. Experiments demonstrate that MamTiff-CAD achieves state-of-the-art performance on both reconstruction and generation tasks, confirming its effectiveness for long sequence (60-256) CAD model generation.
+
+# 1. Introduction
+
+Computer-Aided Design (CAD) is a core tool in modern industrial product manufacturing, constructing complex 3D models through parametric command sequences (such as Sketch, Extrusion, and Boolean operations). It not only defines geometric shapes but also thoroughly records design logic and engineering intentions. With the growing demand
+
+![](images/4cf82f70895fc1e97da4bb4dce3bc3090ea0d629067a0d4ef92bb6929d2d3716.jpg)  
+Figure 1. Gallery of Generated CAD Designs. Our generative model infers parametric CAD command sequences, enabling the creation of diverse and structurally valid CAD models. The resulting 3D shapes exhibit clean geometry, well-defined features, and full editability, allowing users to modify designs seamlessly.
+
+for highly complex and long sequence CAD models in industrial design, existing generation methods are limited by computational efficiency and sequence modeling capabilities, making it difficult to effectively handle industrial-grade design processes that contain hundreds of commands.
+
+The key challenges lie in overcoming the bottleneck of long sequence modeling and enabling the automated generation of complex CAD models. Significant progress has been made in deep learning-based parametric CAD modeling, with Transformer architectures being employed to generate parametric CAD models [42, 45, 46]. While these approaches achieve strong performance in short-sequence command generation, the inherent quadratic complexity of the Transformer's self-attention mechanism limits its scalability to longer sequences [37]. This constraint makes it difficult to model intricate CAD structures, significantly restricting the applicability of existing methods for generating complex CAD models.
+
+To address these challenges, we propose MamTiff-
+
+CAD, a novel CAD command sequence generation framework that integrates Mamba+ and Transformer autoencoder with a multi-scale diffusion model. In the autoencoding stage, the Mamba+ encoder optimizes state transitions through a gating mechanism, improving its ability to model long-range dependencies. It works with a non-autoregressive Transformer decoder to reconstruct latent representations of CAD command sequences. Once trained, the encoder maps parametric CAD data into a compact, low-dimensional latent space. In the generation stage, a multi-scale Transformer-driven diffusion model is employed. Through hierarchical denoising, it synchronizes local geometric details with global topological constraints, generating logically coherent latent vectors from Gaussian noise. These vectors are then decoded into executable parametric CAD command sequences. We make following contributions in this paper:
+
+- proposing a novel autoencoder architecture that combines Mamba+ and Transformer to model long sequence parametric CAD commands.  
+- proposing a multi-scale diffusion-based CAD sequence generator to fuse local and global topology, improving both generation quality and  
+- evaluations showing that our approach achieving state-of-the-art (SOTA) performance on a long sequence CAD dataset containing 13,705 samples with sequence lengths ranging from 60 to 256.
+
+# 2. Related Work
+
+# 2.1. CAD Model Generation
+
+Parametric CAD generation has recently shifted from traditional geometric representations to deep learning approaches emphasizing sequence modeling. DeepCAD [42] pioneered the use of a Transformer architecture for autoregressive CAD command generation, but it was constrained to short sequences (length  $< 60$ ), limiting its ability to capture hierarchical structures in complex models. Follow-up efforts introduced hierarchical representations to improve generation accuracy. SkexGen [45] proposed a decoupled approach for sketching and extrusion operations, facilitating geometric-topological separation, while HNC-CAD [46] built independent codebooks for rings, sketches, and extrusions via a VQ-VAE [36] framework, enabling multi-granularity control. Nevertheless, these methods still grapple with the quadratic computational overhead of Transformer-based architectures, hindering their ability to generate complex, long-sequence CAD models.
+
+The rise of multimodal generation techniques has opened new pathways for CAD generation. CAD-MLLM [44] constructed the first multimodal dataset supporting text, image, and point cloud inputs. However, its generative model still faces topological rupture issues in complex geometric over
+
+lapping regions. FlexCAD [49], by fine-tuning LLMs[13], has achieved controllable CAD generation, but the generated models are relatively simple and do not meet the complex design demands of industrial-grade applications.
+
+# 2.2. Long-Sequence Modeling Techniques
+
+A key challenge in long-sequence modeling is balancing computational efficiency with long-term dependency capture. Traditional methods [5, 12] suffer from the problem of vanishing gradients, making it difficult to effectively capture long-term dependencies. While Transformers address this via self-attention, their quadratic complexity in sequence length drives up memory and training costs. To address this issue, sparse attention mechanisms [2, 14, 29, 38] and local window strategies [22, 32, 33] have been proposed. These methods can alleviate computational bottlenecks to some extent. However, they sacrifice global context awareness, which can lead to design logic breakdowns and information loss when processing CAD sequence generation.
+
+State Space Models [10] offer a novel approach for long-sequence modeling. Mamba [8] incorporates a selective scanning mechanism to achieve long-sequence modeling with linear complexity, demonstrating significant advancements in fields such as audio [3, 16, 40], vision [4, 17, 20, 21], and 3D [19, 39]. However, Mamba's application in the CAD domain remains underexplored. In this work, we adapt Mamba for CAD sequence modeling and refine its structure to better capture long-range dependencies.
+
+# 2.3. 3D Generation with Diffusion Models
+
+Diffusion models, as an advanced generative approach, achieve high-quality generation through the process of gradually adding Gaussian noise to the data and then performing denoising in reverse [11]. In the field of 3D generation, diffusion models have been widely applied to tasks such as point cloud to mesh [9, 26] image to 3D reconstruction [23, 28, 34, 43] and CAD model generation [7, 47].
+
+Recent studies have made significant progress in combining diffusion models with 3D generation. Generating 3D models through point clouds remains the mainstream method [25, 35, 50]. For example, 3DShape2VecSet[48] proposed a latent set diffusion framework, achieving 3D shape generation and point cloud completion through multiscale feature fusion. DiT-3D[30] is the first Diffusion Transformer architecture designed for 3D shape generation, leveraging the denoising process of DDPM on voxelized point clouds to efficiently generate high-fidelity 3D models. DiffCAD [7] employs diffusion models to learn the probabilistic model of CAD generation and alignment. GetMesh [27] uses a latent set diffusion model to generate meshes from point clouds, while Polydiff [1] uses a discrete denoising diffusion model to process mesh data, generating 3D triangular mesh structures. Diffusion-SDF [18] generates
+
+![](images/faf100c8a13f3b8ffba6570328d32abd812522bf2cfec80de483dd5d2d61496b.jpg)  
+Figure 2. The framework of MamTiff-CAD consists of two main steps. In Step 1, an autoencoder integrating Mamba and Transformer architectures is used to learn the latent representation of CAD command sequences. In Step 2, a diffusion-based module is employed to model the generative distribution of the learned latent representations. During inference, the model directly generates latent variables, which are subsequently decoded to reconstruct the CAD command sequences.
+
+3D shapes via diffusion models, offering flexibility in generating diverse shapes that match textual descriptions.
+
+Although these methods have shown potential in generating complex 3D structures, they still face challenges when dealing with parametric CAD sequences. Existing methods primarily focus on generating geometric shapes, neglecting the design logic and parametric constraints of CAD models, which makes the generated results difficult to directly apply to industrial design. The MamTiff-CAD framework proposed in this paper is the first to introduce diffusion models into the field of parametric CAD sequence generation. By learning in latent space vectors and combining Mamba's efficient long-sequence modeling capabilities, it significantly enhances the stability of long-sequence generation.
+
+# 3. Method
+
+# 3.1. Architecture Overview
+
+As shown in Figure 2, our MamTiff-CAD architecture consists of two steps. In the first step, we train a autoencoder to encode CAD command sequences into latent representation. The input CAD command sequences is represented as a set of parameterized commands  $M = \{m_i\}_{i=1}^{N_c}$ , where  $N_c$  is the fixed sequence length set to 256. This CAD first passes through an embedding layer and is then fed into Mamba+ blocks. Afterwards, it undergoes compression via a Compress Block for the convenience of following training, generating a latent vector  $Z$ . The Transformer-based decoder takes both a learnable embedding and  $Z$  as inputs and subsequently outputs the predicted command sequence  $\hat{M} = \{\hat{m}_i\}_{i=1}^{N_c}$ . In the second step, we use diffusion-based
+
+networks to learn the generation of encoded latent representation  $Z$ . During inference, MamTiff-CAD uses the trained diffusion network to generate the latent representation  $\hat{Z}$ , which is then decoded into a command sequence using the frozen decoder.
+
+# 3.2. AutoEncoder for Latent Representation
+
+Parametric CAD Representation. We follow Deep-CAD [42] to represent each CAD model as a sequence of parametric commands  $M = \{(C_1, p_1), \ldots, (C_n, p_n)\}$ , where  $C_i$  is a command type and  $p_i$  is its associated parameter set. For neural network input, we set the sequence length to 256 and end with the special token  $\langle \mathrm{EOS} \rangle$ . For sequences less than 256, we pad the length to 256. Each command's parameters  $p_i$  are encoded as a 16-dimensional vector:  $\mathbf{p}_i = [x, y, \alpha, f, r, \theta, \phi, \gamma, p_x, p_y, p_z, s, e_1, e_2, b, u] \in \mathbb{R}^{16}$ , where unused entries are padded with -1. Continuous parameters are normalized to a  $2 \times 2 \times 2$  cube and quantized into 256 discrete levels to preserve geometric consistency. All parameters are eventually converted to discrete tokens for learning.
+
+Forget Gate Encoder. To encode CAD commands into the latent space  $Z$ , we employ a Forget Gate Encoder. We first embed the parametric representation by considering the command type  $C_i$ , parameters  $p_i$ , and the command's position within the sequence  $pos_i$ . The embedding of each CAD command,  $\mathrm{Emb}(m_i)$ , is defined as follows:
+
+$$
+\operatorname {E m b} \left(m _ {i}\right) = W _ {\mathrm {c m d}} \delta_ {C _ {i}} + W _ {\mathrm {p a}} \cdot \text {f l a t t e n} \left(W _ {\mathrm {p b}} \delta_ {p _ {i}}\right) + W _ {\mathrm {p o s}} \delta_ {i} \tag {1}
+$$
+
+![](images/de3d4c72fa8f416287c0c280e7bb95e02efa21ce46f28200d1cd27e442cb64e4.jpg)  
+Figure 3. An overview of our autoencoder architecture. The input CAD command sequence is first parameterized and fused with command, parameter, and positional embeddings. It is then processed through four Mamba+ blocks and mapped to the latent representation  $Z$  via a compression bolck. Finally, the Transformer decoder reconstructs the CAD sequence, predicting command types and their corresponding parameters.
+
+Here,  $W_{\mathrm{cmd}} \in \mathbb{R}^{d_E \times 6}$  is a learnable matrix and  $\delta_{C_i} \in \mathbb{R}^6$  is a one-hot vector indicating the command type. The system defines six command types. Each CAD command comprises 16 parameters, each quantized into an 8-bit integer. We convert each integer into a one-hot vector of dimension 257, where 256 dimensions correspond to the quantized values and the remaining one dimension indicates that the parameter is unused. Stacking these 16 one-hot vectors forms a matrix  $\delta_{p_i} \in \mathbb{R}^{257 \times 16}$ . Then, using the learnable matrix  $W_{\mathrm{pb}} \in \mathbb{R}^{d_E \times 257}$ , we embed each parameter individually, flatten the resulting embeddings, and apply a linear transformation via  $W_{\mathrm{pa}} \in \mathbb{R}^{d_E \times 16d_E}$ . Finally,  $W_{\mathrm{pos}} \in \mathbb{R}^{d_E \times N_c}$  is another learnable matrix, and  $\delta_i \in \mathbb{R}^{N_c}$  is a one-hot vector that marks the command's position within the sequence, with  $N_c = 256$  being the maximum sequence length.
+
+We then use four Mamba+ blocks, i.e. optimized SSM-based modules integrated with a forget gate mechanism, as our main encoder block. Specifically, the Mamba+ block employs a dual-branch structure for information processing. The feature transformation branch (b1) extracts sequence features using 1D convolution and SSM blocks, while the gating mechanism branch (b2) generates control signals for information flow using the SiLU activation function. To further enhance the retention of historical information, we introduce a forget gate  $\mathbf{G}_f$  as:
+
+$$
+\mathbf {G} _ {f} = 1 - \mathbf {G} _ {b 2} \tag {2}
+$$
+
+$\mathrm{G}_{b2}$  is calculated using the Sigmoid function, controlling the flow of current input information. The forget gate  $\mathrm{G}_f$ , modulates the output  $x^{\prime}$  from  $b1$ , ensuring that some historical information is not completely discarded. The updated historical information is given by  $x^{\prime \prime} = \mathrm{G}_f\cdot x^{\prime}$ . The final output of Mamba+ blocks integrates the SSM computation result with the feature adjusted by the forget gate  $h_\mathrm{out} = x'' + h_{\mathrm{SSM}}$ . This design enables the Mamba+ block to effectively extract new features while preserving long-term dependencies, enhancing its ability to process complex CAD command sequences efficiently.
+
+To efficiently encode long sequence CAD commands, we construct the Mamba+ block by stacking four layers of Mamba+ blocks, capable of modeling CAD command sequences ranging from 60 to 256 in length. The encoder first converts the input command sequence  $M = \{m_{1}, m_{2}, \ldots, m_{256}\}$  into continuous representations through an embedding layer, then progressively captures local and global dependencies through hierarchical processing in the Mamba+ blocks.
+
+Decoder. We employ a Transformer-based decoder, which consists of four blocks, to reconstruct parametric CAD command sequences. The decoder's input comprises two parts: 1) a latent vector  $Z$ , and 2) a set of learnable embedding vectors produced by a learnable embedding layer, which provides a representation for each position in the sequence. In our model, the maximum sequence length is set to  $N_{c} = 256$ . The decoder processes these inputs layer by layer to capture global dependencies among commands in long sequences and ultimately generates the predicted CAD command sequence  $\hat{M} = \{\hat{m}_1, \hat{m}_2, \dots, \hat{m}_{256}\}$ , where each command  $\hat{m}_i$  consists of a command type  $\hat{C}_i$  and parameters  $\hat{\mathbf{p}}_i$ . Our objective is to maximize the generation probability:
+
+$$
+p (\hat {M} \mid z, \Theta) = \prod_ {i = 1} ^ {N _ {c}} p \left(\hat {C} _ {i}, \hat {\mathbf {p}} _ {i} \mid z, \Theta\right) \tag {3}
+$$
+
+Here,  $\Theta$  denotes the decoder's network parameters. Finally, the decoder's output hidden states are mapped through a linear transformation layer into the specific command type and parameter space, thereby completing the generation of the entire CAD command sequence.
+
+Training of the Autoencoder. We train our autoencoder by reconstructing parameterized CAD command sequences to learn the latent representation  $Z$  of CAD models. The training objective is to minimize the discrepancy between the predicted outputs and the ground truth, ensuring that the model accurately captures the structural information of the parameterized CAD sequences. We employ a cross-entropy loss function to supervise the learning of both CAD command types and parameters.
+
+Our output sequence is fixed to  $N_{c} = 256$  commands, each with  $N_{p} = 16$  parameters. For the  $i$ -th command, the
+
+ground truth command type is denoted by  $t_i$  (derived from the true command  $C_i$ ), and the corresponding ground truth parameters are represented as  $\{a_{i,j}\}_{j=1}^{N_p}$  (where each  $a_{i,j}$  is an element of the previously defined parameter vector). The model predicts a probability distribution over command types  $p_i$  such that  $p_i(t_i)$  represents the probability assigned to the true command type, and for each parameter  $a_{i,j}$ , it predicts a probability distribution  $q_{i,j}$  over quantized parameter values. The total loss is defined as:
+
+$$
+L = \sum_ {i = 1} ^ {N _ {c}} \ell \left(p _ {i} \left(t _ {i}\right)\right) + \beta \sum_ {i = 1} ^ {N _ {c}} \sum_ {j = 1} ^ {N _ {p}} \ell \left(q _ {i, j} \left(a _ {i, j}\right)\right) \tag {4}
+$$
+
+where  $\beta$  is set to 2 to balance the parameter loss against the command type loss. Contributions from padding commands, such as the  $\langle \mathrm{EOS} \rangle$  token, and unused parameters labeled as  $-1$  are ignored during loss computation. For further details on the experimental configurations, please refer to the Appendix.
+
+# 3.3. Diffusion Generator
+
+The above frozen autoencoder encodes the command sequences into a compact latent variable  $Z \in \mathbb{R}^{N \times 256 \times 64}$ . In the forward process, we employ a linear variance schedule  $\{\beta_t\}$  to add noise into the latent space, while the denoising process is guided by a Multi-Scale Transformer-based Diffusion Generator (MST-D), as shown in Figure 4.
+
+Multi-Scale Transformer Denoiser. The denoising network is composed of stacked multi-scale Transformer layers, each following a three-stage pipeline: Hierarchical Attention: Each layer contains three parallel attention branches with window sizes of 64, 128 and 256 to capture local geometric constraints, medium-range topological dependencies, and global semantic coherence respectively, processing the latent sequence at three different scales. Adaptive Fusion: The outputs of these attention branches are concatenated and fused via a learnable gating mechanism:
+
+$$
+\mathbf {H} = \operatorname {M L P} \left(\sigma \left(\mathbf {W} _ {g} \left[ \mathbf {H} _ {l} \| \mathbf {H} _ {m} \| \mathbf {H} _ {g} \right]\right) \odot \left[ \mathbf {H} _ {l} \| \mathbf {H} _ {m} \| \mathbf {H} _ {g} \right]\right) \tag {5}
+$$
+
+where  $\sigma(\cdot)$  is the sigmoid function, and  $\parallel$  denotes concatenation. Time-Step Embedding: This module inputs temporal information into each Transformer block by transforming the diffusion time step  $t$  into an embedding vector. The embedding is then processed through an MLP to generate two sets of scale and shift parameters  $\{\xi_i, \psi_i, \omega_i\}$ . Here,  $\xi_i$  and  $\psi_i$  modulate the layer-normalized activations, while  $\omega_i$  adjusts the strength of the residual connections. Together, these parameters enable the Transformer to adapt its behavior in a time-aware manner.
+
+Sequence-Aware Positional Encoding. To preserve the sequential logic of CAD commands, we employ a scalable si
+
+![](images/75f5e5c4338b6976733c73cf42388d79c4f147affc4a779f125b0ab176c2663a.jpg)  
+Figure 4. Our denoising diffusion model. The input noise undergoes linear projection with positional and timestep embeddings, followed by feature extraction through encoder blocks  $(\times 6)$ . The core denoising structure, MST with Adaptive Fusion, integrates multi-scale attention and adaptive fusion to dynamically adjust feature distributions. Finally, the MLP Head reconstructs the denoised output features.
+
+nusoidal positional encoding  $PE$ , defined as
+
+$$
+P E (Z) = Z + \eta \cdot \operatorname {P E} (\operatorname {p o s}, D) \tag {6}
+$$
+
+where  $\eta \in \mathbb{R}$  is a trainable scalar that adaptively adjusts the positional weighting. Instead of writing the full vector in one long equation, we define the positional encoding function piecewise as follows:
+
+$$
+\mathrm {P E} _ {2 i} (\text {p o s}, D) = \sin \left(\frac {\text {p o s}}{1 0 0 0 0 ^ {2 i / D}}\right) \tag {7}
+$$
+
+$$
+\mathrm {P E} _ {2 i + 1} (\operatorname {p o s}, D) = \cos \left(\frac {\operatorname {p o s}}{1 0 0 0 0 ^ {2 i / D}}\right)
+$$
+
+for  $i = 0,1,\dots ,\frac{D}{2} -1$ . Here, pos denotes the sequence position, and  $D$  is the embedding dimension. This encoding scheme provides both absolute and relative position information, enabling the model to capture the sequential dependencies of CAD commands more effectively.
+
+Training and Generation. The denoising network learns to predict the noise injected at each timestep, modeling the distribution of the target latent space. Given a clean latent vector  $Z_{0}$  from the frozen encoder, we first sample a timestep  $t \sim U(1,T)$  and add noise according to the linear variance schedule  $\{\beta_t\}$ :
+
+$$
+Z _ {t} = \alpha_ {t} Z _ {0} + \sqrt {1 - \alpha_ {t}} \epsilon \tag {8}
+$$
+
+where  $\epsilon \sim \mathcal{N}(0,I)$ ,  $\alpha_{t} = \prod_{s=1}^{t}(1 - \beta_{s})$  controls the rate of noise accumulation. The network  $\epsilon_{\theta}$  is then trained to predict  $\epsilon$  by minimizing the mean squared error:
+
+$$
+L _ {\text {d i f f}} = \mathbb {E} _ {t, Z _ {0}, \epsilon} \left[ \| \epsilon - \epsilon_ {\theta} (Z _ {t}, t) \| _ {2} ^ {2} \right] \tag {9}
+$$
+
+Table 1. Statistical comparison between the DeepCAD dataset and our ABC-256 dataset, including the total number, the average length of parametric CAD sequences, and the length distribution.  
+
+<table><tr><td>Dataset</td><td>Total</td><td>Average Length</td><td>1-10</td><td>11-60</td><td>61-128</td><td>129-256</td></tr><tr><td>DeepCAD</td><td>178,238</td><td>15</td><td>44.58</td><td>55.42</td><td>-</td><td>-</td></tr><tr><td>ABC-256</td><td>13,705</td><td>99</td><td>-</td><td>-</td><td>82.89</td><td>17.11</td></tr></table>
+
+Sampling Process: Starting from Gaussian noise  $Z_{T}$ , the latent vector is iteratively denoised using:
+
+$$
+Z _ {t - 1} = \frac {1}{\alpha_ {t}} \left(Z _ {t} - \beta_ {t} \frac {1}{\sqrt {1 - \alpha_ {t}}} \epsilon_ {\theta} \left(Z _ {t}, t\right)\right) + \beta_ {t} z \tag {10}
+$$
+
+where  $z \sim \mathcal{N}(0, I)$ . Here,  $\alpha_{t}$  and  $\beta_{t}$  denote the attenuation factor and variance schedule parameter at timestep  $t$ , respectively. Through this process, our diffusion generator progressively removes noise in the latent space to yield CAD command sequences with global consistency and rich structural information.
+
+# 4. ABC-256 Dataset
+
+Although several publicly available CAD datasets exist, such as ABC [15], DeepCAD [42], Fusion 360 Gallery [41], and CAD as Language [6], datasets that fully capture the CAD design process remain scarce. The ABC dataset contains 1 million B-rep models from Onshape [31], but lacks design operation sequences, making full process reconstruction difficult. The Fusion 360 Gallery dataset provides CAD command sequences based on sketch profile extrusions, but these sequences are short, limiting their suitability for long sequence CAD generation. DeepCAD includes 178,238 CAD models with parameterized sequences, but its maximum sequence length of 60 constrains its ability to model complex, long sequence CAD designs.
+
+To address this, we constructed and publicly released a complex CAD command sequence dataset to advance research in this field. Using ABC dataset model links, we leveraged the Onshape API and FeatureScript to extract CAD commands from CSG representations and convert them into parameterized sequences. We then filtered models to retain only those with complete operations, excluding simpler cases with only sketching and extrusion, ensuring sequence lengths between 60 and 256. The final dataset includes 13,705 CAD models, split into 10,964 for training, 1,370 for validation, and 1,371 for testing. Compared to DeepCAD, our dataset has a  $6.6 \times$  longer average sequence length (Table 1), offering greater complexity in both length and distribution, making it more suitable for long sequence CAD model generation.
+
+# 5. Experiment
+
+# 5.1. Implementation details
+
+We implemented the MamTiff-CAD model based on the PyTorch framework and completed the training of the model on an NVIDIA RTX 4090 GPU. During training, we adopted the AdamW optimizer [24] with an initial learning rate of 0.001, applying linear warmup for the first 2000 steps. Additionally, we introduced gradient clipping to prevent gradient explosion issues. The training of MamTiff-CAD is divided into two stages. The first stage involves training for 300 epochs with a batch size of 32. In the second stage, training for 200,000 epochs with a batch size of 64 to ensure the model has enough time to fully learn and reach convergence.
+
+# 5.2. Evaluation Metrics
+
+We comprehensively evaluate the performance of the autoencoder using five metrics, including Command Accuracy, Parameter Accuracy, Median Chamfer Distance, Invalid Ratio, and Step Ratio.
+
+Command Accuracy  $(\mathrm{ACC_c})$  : the match between the predicted CAD command type and the ground truth.  
+- Parameter Accuracy  $(\mathrm{ACC_p})$ : the match of command parameters under the condition that the command type is correctly predicted.  
+- Median Chamfer Distance (MCD): the geometric error between the reconstructed 3D shape and the reference shape, computed as the median distance between corresponding points.  
+- Invalid Ratio (IR): the proportion of CAD models that cannot be converted into point clouds.  
+- Step Ratio (SR): the proportion of generated CAD sequences successfully converted to the STEP format.
+
+For the performance evaluation of the generator, we adopt six metrics, including Coverage (COV), Jensen-Shannon Divergence (JSD), Minimum Matching Distance (MMD), Novelty, Uniqueness, and Step Ratio. The specific metric descriptions are as follows:
+
+- Coverage (COV): whether the generated data covers the distribution of real data.  
+- Jensen-Shannon Divergence (JSD): the difference between the generated data distribution and the real data distribution.  
+- Minimum Matching Distance (MMD): the minimum average distance between generated and real samples.  
+- Unique: the proportion of non-duplicate samples in the generated dataset.  
+- Novel: the proportion of samples in the generated dataset that do not appear in the training set.
+
+![](images/e84d3b4b318b40340d01c889094e33aa7b491693eb80674b13d822dedf0693ac.jpg)  
+Figure 5. This figure compares the CAD models generated by DeepCAD, SkexGen, HNC-CAD, and MamTiff-CAD, highlighting differences in shape complexity and geometric details among the methods. It can be observed that MamTiff-CAD produces models with higher structural integrity and greater complexity.
+
+# 5.3. Autoencoding Parametric CAD
+
+To comprehensively evaluate the effectiveness of our proposed MamTiff-CAD framework, we conducted a comparative analysis across multiple models, including DeepCAD, MT-CAD, MLSTM-CAD, and our own model. Among them, MT-CAD integrates Mamba with Transformer, while MLSTM-CAD combines Mamba with LSTM. Experimental results confirm that our framework offers significant advantages in long sequence parametric CAD modeling, further reinforcing the rationale behind our design decisions.
+
+Results. The experimental results are presented in Table 2. Overall, our model achieves SOTA performance across all evaluation metrics. Specifically, our proposed MamTiff-CAD attains a command accuracy of  $99.99\%$  and a parameter accuracy of  $99.93\%$ , demonstrating superior capability in long command sequence prediction. In terms of Median Chamfer Distance (MCD), MamTiff-CAD outperforms other baseline models, indicating that the reconstructed 3D shapes are geometrically closer to the reference data. Invalid Ratio and Step Ratio, our model achieves an IR of  $8.50\%$  and a Step Ratio of  $93.93\%$ , indicating a higher proportion of valid command sequences, thereby demonstrating the reliability of MamTiff-CAD. Furthermore, Figure 6 provides a visual comparison of CAD sequence re
+
+Table 2. Autoencoding performance on ABC-256 dataset. Comparison of MamTiff-CAD and other models in CAD sequence reconstruction. MCD is multiplied by  $10^{3}$ , and  $\mathrm{ACC}_c$ ,  $\mathrm{ACC}_p$ , and IR are multiplied by  $100\%$ .  
+
+<table><tr><td>Method</td><td>ACCc↑</td><td>ACCp↑</td><td>MCD↓</td><td>IR↓</td><td>SR↑</td></tr><tr><td>DeepCAD</td><td>92.24</td><td>75.93</td><td>41.02</td><td>33.11%</td><td>70.46%</td></tr><tr><td>MT-CAD</td><td>89.72</td><td>66.87</td><td>121.35</td><td>39.89%</td><td>63.97%</td></tr><tr><td>MLSTM-CAD</td><td>86.09</td><td>65.55</td><td>112.89</td><td>42.53%</td><td>59.85%</td></tr><tr><td>OURS</td><td>99.99</td><td>99.93</td><td>0.75</td><td>8.50%</td><td>93.93%</td></tr></table>
+
+Table 3. Quantitative evaluation of generalization ability on the Fusion360 reconstruction dataset.  
+
+<table><tr><td>Method</td><td>ACCc↑</td><td>ACCp↑</td><td>MCD↓</td><td>IR↓</td><td>SR↑</td></tr><tr><td>DeepCAD</td><td>93.35</td><td>77.99</td><td>104.76</td><td>19.41%</td><td>82.57%</td></tr><tr><td>MT-CAD</td><td>91.85</td><td>60.18</td><td>300.21</td><td>11.81%</td><td>90.17%</td></tr><tr><td>MLSTM-CAD</td><td>88.42</td><td>62.13</td><td>261.80</td><td>23.39%</td><td>80.97%</td></tr><tr><td>OURS</td><td>99.99</td><td>97.99</td><td>1.44</td><td>5.70%</td><td>95.16%</td></tr></table>
+
+construction results across different models. MamTiff-CAD produces CAD models with better structural integrity and geometric accuracy, making them closer to the ground truth. This further validates the superior performance of MamTiff-CAD in long sequence parametric CAD prediction.
+
+Generalization Performance. To evaluate the generalization capability of MamTiff-CAD, we trained the model on the ABC-256 dataset and directly tested it on the Fusion360 dataset, which was not used during training. The Fusion360 dataset consists of approximately 8,000 parametric CAD sequences derived from Autodesk Fusion 360 designs. Although the CAD models in the Fusion360 dataset exhibit simpler structures, which narrows the performance gap between MamTiff-CAD and the baseline models in certain metrics, MamTiff-CAD still consistently outperforms all other models across all evaluation metrics, achieving the best overall performance (see Table 3).
+
+# 5.4. Unconditional Generation
+
+We comprehensively evaluated latent vector-based CAD generation across several models, including DeepCAD, SkexGen, HNC-CAD, and our MamTiff-CAD. Each method randomly generated 10,000 samples, which were converted to point clouds for metric-based evaluation. The experimental results in Table 4 indicate that our method achieves state-of-the-art (SOTA) performance in all metrics, particularly excelling in JSD (3.19) and Step Ratio
+
+![](images/398ad3a7a21a1d26e0404eb7e920ffe6ecbf54a9ecc5462db3698a82691ec98c.jpg)  
+Figure 6. Visual illustrations of CAD sequence reconstruction. GT represents Ground Truth, and Ours refers to MamTiff-CAD. The symbol  $\emptyset$  indicates an invalid CAD sequence, resulting in the failure of 3D shape construction.
+
+Table 4. The unconditional generation result compared with Deep-CAD, SkexGen, HNC-CAD and ours.  
+
+<table><tr><td>Method</td><td>MMD ↓</td><td>JSD ↓</td><td>COV ↑</td><td>Unique ↑</td><td>Novel ↑</td><td>SR ↑</td></tr><tr><td>DeepCAD</td><td>2.66</td><td>6.49</td><td>56.66%</td><td>75.8</td><td>88.0</td><td>23.96%</td></tr><tr><td>SkexGen</td><td>2.31</td><td>4.53</td><td>57.76%</td><td>80.5</td><td>96.9</td><td>75.26%</td></tr><tr><td>HNC-CAD</td><td>1.63</td><td>4.25</td><td>62.03%</td><td>89.2</td><td>91.8</td><td>80.86%</td></tr><tr><td>OURS</td><td>1.32</td><td>3.19</td><td>65.31%</td><td>99.6</td><td>99.4</td><td>85.38%</td></tr></table>
+
+(85.38%), demonstrating the higher quality and effectiveness of the generated sequences. Furthermore, as shown in Figure 5, MamTiff-CAD generates more complex CAD models compared to other methods, further validating its advantage in long sequence CAD generation tasks.
+
+# 5.5. Ablation Studies
+
+We conducted two ablation studies to systematically assess the impact of the two key techniques, Mamba+ and MultiScale Transformer (MST), on our model's performance.
+
+Mamba+ Block. To investigate the role of the Mamba+ block in reconstructing long sequence parametric CAD commands, we conducted two ablation experiments: (1) replacing the Mamba+ block with Transformer block, and (2) replacing the Mamba+ block with the standard Mamba block. The experimental results (see Table 5) indicate that, compared to the standard Mamba block, the Mamba+ block achieves improvements across most evaluation metrics. This confirms that the incorporation of the forget gate mechanism enhances the model's ability to capture long-range dependencies in CAD command sequences, with particularly notable improvements in common CAD sequence tasks over the Transformer-based approach.
+
+MST or Non-MST. To assess the impact of MST on our generative model, we conducted an ablation study by randomly generating 10,000 models and comparing the generation capabilities with and without the MST module. The experimental results in Table 6 demonstrate that incorporating MST significantly improves model performance, partic
+
+Table 5. Comparison of reconstruction performance between Mamba and Mamba+ block for parametric CAD sequences.  
+
+<table><tr><td>Method</td><td>ACCc↑</td><td>ACCp↑</td><td>MCD ↓</td><td>IR ↓</td><td>SR ↑</td></tr><tr><td>Transformer</td><td>77.29</td><td>63.62</td><td>64.30</td><td>67.46%</td><td>34.28%</td></tr><tr><td>Mamba</td><td>99.98</td><td>99.90</td><td>0.76</td><td>10.35%</td><td>92.01%</td></tr><tr><td>Mamba+</td><td>99.99</td><td>99.93</td><td>0.75</td><td>8.50%</td><td>93.93%</td></tr></table>
+
+Table 6. The unconditional generation result compared between w/o MST and MST.  
+
+<table><tr><td>Method</td><td>MMD ↓</td><td>JSD ↓</td><td>COV ↑</td><td>Unique ↑</td><td>Novel ↑</td><td>SR ↑</td></tr><tr><td>w/o MST</td><td>1.47</td><td>4.92</td><td>61.69%</td><td>99.5</td><td>99.6</td><td>77.05%</td></tr><tr><td>w/ MST</td><td>1.32</td><td>3.19</td><td>65.31%</td><td>99.6</td><td>99.4</td><td>85.38%</td></tr></table>
+
+ularly in terms of generation quality and validity, confirming the critical role of MST in the diffusion process.
+
+# 6. Discussion and Conclusion
+
+Although MamTiff-CAD has demonstrated exceptional performance in long-sequence parameterized CAD modeling tasks, several limitations remain that warrant further investigation. Firstly, the dataset used in this study does not cover all CAD command types in industrial design, which somewhat limits the model's ability to handle more complex topological structures. Secondly, this research primarily focuses on the generation and parsing of CAD command sequences and does not directly extract features from Boundary Representation (Brep). Therefore, future research is required to address how to effectively integrate CSG (Constructive Solid Geometry) and Brep representations to enhance the model's generalization ability and applicability. This remains a challenging but promising direction.
+
+In conclusion, this paper presents MamTiff-CAD, a novel framework that integrates an improved Mamba module with a diffusion model to efficiently model long parameterized CAD command sequences and generate complex 3D shapes. We also introduce a dataset of 13,705 CAD models with sequence lengths of 60 to 256, significantly surpassing existing datasets. Experimental results validate MamTiff-CAD's superiority in sequence reconstruction and generation. Future work will focus on refining the model and exploring its integration with advanced design processes and interactive methods to enhance intelligent CAD design.
+
+# Acknowledgments
+
+This research was funded by the Fundamental Research Funds for the Central Universities, the Natural Science Basic Research Program of Shaanxi Province under Grant 2024JC-YBQN-0702 and NUS Research Scholarship. Partially supported by Hunan Natural Science Foundation Project (No. 2025JJ50338) and Shang
+
+hai Education Committee AI Project(No. JWAIYB-2)
+
+# References
+
+[1] Antonio Alliegro, Yawar Siddiqui, Tatiana Tommasi, and Matthias Nießner. Polydiff: Generating 3d polygonal meshes with diffusion models. arXiv preprint arXiv:2312.11417, 2023. 2  
+[2] Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150, 2020. 2  
+[3] Rong Chao, Wen-Huang Cheng, Moreno La Quatra, Sabato Marco Siniscalchi, Chao-Han Huck Yang, Szu-Wei Fu, and Yu Tsao. An investigation of incorporating mamba for speech enhancement. In 2024 IEEE Spoken Language Technology Workshop (SLT), pages 302–308. IEEE, 2024. 2  
+[4] Yubo Cui, Zhiheng Li, Jiaqiang Wang, and Zheng Fang. Loma: Language-assisted semantic occupancy network via triplane mamba. arXiv preprint arXiv:2412.08388, 2024. 2  
+[5] Jeffrey L Elman. Finding structure in time. Cognitive science, 14(2):179-211, 1990. 2  
+[6] Yaroslav Ganin, Sergey Bartunov, Yujia Li, Ethan Keller, and Stefano Saliceti. Computer-aided design as language. Advances in Neural Information Processing Systems, 34:5885–5897, 2021. 6  
+[7] Daoyi Gao, David Rozenberszki, Stefan Leutenegger, and Angela Dai. Diffcad: Weakly-supervised probabilistic cad model retrieval and alignment from an rgb image. ACM Transactions on Graphics (TOG), 43(4):1-15, 2024. 2  
+[8] Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023. 2  
+[9] Anchit Gupta, Wenhan Xiong, Yixin Nie, Ian Jones, and Barlas Oğuz. 3dgen: Triplane latent diffusion for textured mesh generation. arXiv preprint arXiv:2303.05371, 2023. 2  
+[10] James D Hamilton. State-space models. Handbook of econometrics, 4:3039-3080, 1994. 2  
+[11] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 2  
+[12] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural Computation, 9(8):1735-1780, 1997. 2  
+[13] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3, 2022. 2  
+[14] Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer. arXiv preprint arXiv:2001.04451, 2020. 2  
+[15] Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, and Daniele Panozzo. Abc: A big cad model dataset for geometric deep learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9601-9611, 2019. 6, 1
+
+[16] Kai Li, Guo Chen, Runxuan Yang, and Xiaolin Hu. Sp-mamba: State-space model is all you need in speech separation. arXiv preprint arXiv:2404.02063, 2024. 2  
+[17] Kunchang Li, Xinhao Li, Yi Wang, Yinan He, Yali Wang, Limin Wang, and Yu Qiao. Videomamba: State space model for efficient video understanding. In European Conference on Computer Vision, pages 237-255. Springer, 2024. 2  
+[18] Muheng Li, Yueqi Duan, Jie Zhou, and Jiwen Lu. Diffusionsdf: Text-to-shape via voxelized diffusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12642-12651, 2023. 2  
+[19] Jiuming Liu, Ruiji Yu, Yian Wang, Yu Zheng, Tianchen Deng, Weicai Ye, and Hesheng Wang. Point mamba: A novel point cloud backbone based on state space model with octree-based ordering strategy. arXiv preprint arXiv:2403.06467, 2024. 2  
+[20] Xiao Liu, Chenxu Zhang, and Lei Zhang. Vision mamba: A comprehensive survey and taxonomy. arXiv preprint arXiv:2405.04404, 2024. 2  
+[21] Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, Jianbin Jiao, and Yunfan Liu. Vmamba: Visual state space model. Advances in neural information processing systems, 37:103031-103063, 2025. 2  
+[22] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 2  
+[23] Zhen Liu, Yao Feng, Michael J Black, Derek Nowrouzezahrai, Liam Paull, and Weiyang Liu. Meshdiffusion: Score-based generative 3d mesh modeling. arXiv preprint arXiv:2303.08133, 2023. 2  
+[24] Ilya Loshchilov, Frank Hutter, et al. Fixing weight decay regularization in adam. arXiv preprint arXiv:1711.05101, 5: 5, 2017. 6  
+[25] Shitong Luo and Wei Hu. Diffusion probabilistic models for 3d point cloud generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2837-2845, 2021. 2  
+[26] Zhaoyang Lyu, Jinyi Wang, Yuwei An, Ya Zhang, Dahua Lin, and Bo Dai. Controllable mesh generation through sparse latent point diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 271-280, 2023. 2  
+[27] Zhaoyang Lyu, Ben Fei, Jinyi Wang, Xudong Xu, Ya Zhang, Weidong Yang, and Bo Dai. Getmesh: A controllable model for high-quality mesh generation and manipulation. arXiv preprint arXiv:2403.11990, 2024. 2  
+[28] Baorui Ma, Yu-Shen Liu, and Zhizhong Han. Learning signed distance functions from noisy 3d point clouds via noise to noise mapping. 2023. 2  
+[29] Yiqun Mei, Yuchen Fan, and Yuqian Zhou. Image superresolution with non-local sparse attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3517-3526, 2021. 2  
+[30] Shentong Mo, Enze Xie, Ruihang Chu, Lanqing Hong, Matthias Niessner, and Zhenguo Li. Dit-3d: Exploring plain
+
+diffusion transformers for 3d shape generation. Advances in neural information processing systems, 36:67960-67971, 2023. 2  
+[31] Onshape. Onshape featurescript. https://cad.onshape.com/FsDoc/, 2019.6, 1  
+[32] Xiang Pan, Hongyan Zhou, Chenwei Yang, Luming Sun, Paul S Smith, Tuo Ji, Ning Jiang, Peng Jiang, Wenjuan Liu, Honglin Lu, et al. Mrk 1239: a type-2 counterpart of narrow-line seyfert-1? The Astrophysical Journal, 912(2):118, 2021. 2  
+[33] Pengzhen Ren, Changlin Li, Guangrun Wang, Yun Xiao, Qing Du, Xiaodan Liang, and Xiaojun Chang. Beyond fixation: Dynamic window visual transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11987-11997, 2022. 2  
+[34] Michal J Tyszkiiewicz, Pascal Fua, and Eduard Trulls. Gecco: Geometrically-conditioned point diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2128-2138, 2023. 2  
+[35] Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis, et al. Lion: Latent point diffusion models for 3d shape generation. Advances in Neural Information Processing Systems, 35:10021-10039, 2022. 2  
+[36] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. 2  
+[37] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 1  
+[38] Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma. Linformer: Self-attention with linear complexity. arXiv preprint arXiv:2006.04768, 2020. 2  
+[39] Zicheng Wang, Zhenghao Chen, Yiming Wu, Zhen Zhao, Luping Zhou, and Dong Xu. Pointramba: A hybrid transformer-mamba framework for point cloud analysis. arXiv preprint arXiv:2405.15463, 2024. 2  
+[40] Zhong-Qiu Wang, Samuele Cornell, Shukjae Choi, Younglo Lee, Byeong-Yeol Kim, and Shinji Watanabe. Tf-gridnet: Making time-frequency domain models great again for monaural speaker separation. In ICASSP 2023-2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 1-5. IEEE, 2023. 2  
+[41] Karl DD Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph G Lambourne, Armando Solar-Lezama, and Wojciech Matusik. Fusion 360 gallery: A dataset and environment for programmatic cad construction from human design sequences. ACM Transactions on Graphics (TOG), 40(4): 1-24, 2021. 6  
+[42] Rundi Wu, Chang Xiao, and Changxi Zheng. Deepcad: A deep generative network for computer-aided design models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6772–6782, 2021. 1, 2, 3, 6  
+[43] Jiale Xu, Weihao Cheng, Yiming Gao, Xintao Wang, Shenghua Gao, and Ying Shan. Instantmesh: Efficient 3d mesh generation from a single image with sparse-view large reconstruction models. arXiv preprint arXiv:2404.07191, 2024. 2
+
+[44] Jingwei Xu, Chenyu Wang, Zibo Zhao, Wen Liu, Yi Ma, and Shenghua Gao. Cad-mllm: Unifying multimodality-conditioned cad generation with mllm. arXiv preprint arXiv:2411.04954, 2024. 2  
+[45] Xiang Xu, Karl DD Willis, Joseph G Lambourne, Chin-Yi Cheng, Pradeep Kumar Jayaraman, and Yasutaka Furukawa. Skexgen: Autoregressive generation of cad construction sequences with disentangled codebooks. arXiv preprint arXiv:2207.04632, 2022. 1, 2  
+[46] Xiang Xu, Pradeep Kumar Jayaraman, Joseph G Lambourne, Karl DD Willis, and Yasutaka Furukawa. Hierarchical neural coding for controllable cad model generation. arXiv preprint arXiv:2307.00149, 2023. 1, 2  
+[47] Xiang Xu, Joseph Lambourne, Pradeep Jayaraman, Zhengqing Wang, Karl Willis, and Yasutaka Furukawa. Brepgen: A b-rep generative diffusion model with structured latent geometry. ACM Transactions on Graphics (TOG), 43 (4):1-14, 2024. 2  
+[48] Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Transactions On Graphics (TOG), 42(4):1-16, 2023. 2  
+[49] Zhanwei Zhang, Shizhao Sun, Wenxiao Wang, Deng Cai, and Jiang Bian. Flexcad: Unified and versatile controllable cad generation with fine-tuned large language models. arXiv preprint arXiv:2411.05823, 2024. 2  
+[50] Linqi Zhou, Yilun Du, and Jiajun Wu. 3d shape generation and completion through point-voxel diffusion. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5826–5835, 2021. 2

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+# MamV2XCalib: V2X-based Target-less Infrastructure Camera Calibration with State Space Model
+
+Yaoye Zhu Zhe Wang Yan Wang* Institute for AI Industry Research (AIR), Tsinghua University
+
+zhuyaoye22@gmail.com wangzhe@air.tsinghua.edu.cn wangyan@air.tsinghua.edu.cn
+
+# Abstract
+
+As cooperative systems that leverage roadside cameras to assist autonomous vehicle perception become increasingly widespread, large-scale precise calibration of infrastructure cameras has become a critical issue. Traditional manual calibration methods are often time-consuming, labor-intensive, and may require road closures. This paper proposes MamV2XCalib, the first V2X-based infrastructure camera calibration method with the assistance of vehicle-side LiDAR. MamV2XCalib only requires autonomous vehicles equipped with LiDAR to drive near the cameras to be calibrated in the infrastructure, without the need for specific reference objects or manual intervention. We also introduce a new targetless LiDAR-camera calibration method, which combines multi-scale features and a 4D correlation volume to estimate the correlation between vehicle-side point clouds and roadside images. We model the temporal information and estimate the rotation angles with Mamba, effectively addressing calibration failures in V2X scenarios caused by defects in the vehicle-side data (such as occlusions) and large differences in viewpoint. We evaluate MamV2XCalib on the V2X-Seq and TUMTraf-V2X real-world datasets, demonstrating the effectiveness and robustness of our V2X-based automatic calibration approach. Compared to previous LiDAR-camera methods designed for calibration on one car, our approach achieves better and more stable calibration performance in V2X scenarios with fewer parameters. The code is available at https://github.com/zhuyaoye/MamV2XCalib.
+
+# 1. Introduction
+
+In recent years, due to the inherent limitations of on-board perception systems, many researchers have started focusing on utilizing external sensor information to assist autonomous vehicles [5, 10]. Among these external sources, infrastructure cameras are widely used in V2X systems
+
+![](images/b05421792a77790f1c70539ea64d72b7db36d7e6ebe29ab416da0cdaecae6fae.jpg)  
+(a) Calibration with targets in ing trajectory or vehicle itself
+
+![](images/913811fe8bb880274fa7c2e77220d2609418b4dd60954033040cfce9ef0dff25.jpg)
+
+![](images/0ee347861dcef5dde3174568b8f9ebce7e48cabf92219ecaec9e9972267f47f3.jpg)  
+(b) Calibration with reference images prepared before calibration  
+(c) Ours: V2X-based target-less camera calibration method  
+Figure 1. Comparison of roadside camera calibration methods.
+
+for their wide deployment, broader field of view, and reduced occlusion [24]. However, outdoor deployment exposes roadside cameras to extreme weather and vibrations from large vehicles, causing shifts from their initial positions. If not recalibrated, such shifts can significantly degrade the accuracy of data fusion between road and vehicle sensors. In cases of large deviations, they may even result in a complete loss of monitoring over the intended area [38]. Therefore, efficient, scalable calibration methods are essential for stable intelligent transportation systems in outdoor environments.
+
+Traditional calibration methods [32, 39] primarily rely on manual targets, but unlike vehicle sensors that can be calibrated indoors, fixed roadside cameras cannot be relocated. As the demand for camera calibration increases, these manual methods become time-consuming and labor-intensive and may cause disruptions to regular traffic operations. Re-
+
+cent efforts focus on automated calibration [7, 30, 40]. Some methods use autonomous vehicle trajectories or vehicle detection targets. However, these approaches heavily depend on the accuracy of trajectory prediction and object detection, while overlooking rich background data. Other methods require the preparation of reference images or use real-world map images, employing virtual cameras aligned with these images as reference objects [6]. Preparing a series of reference images for each camera is both inconvenient and lacks real-time adaptability.
+
+Whether traditional or modern methods, determining the position of a sensor in the world coordinate system requires two key conditions: known reference objects and the sensor's perception of these objects. In road scenarios, autonomous vehicles, being high-quality intelligent agents, possess real-time, robust capabilities to collect environmental data and accurately localize themselves. Given this, can we invert the typical V2X process, using a vehicle's perception data to treat the entire environment as a reference, thereby calibrating multiple roadside cameras as the vehicle moves?
+
+We propose a novel V2X-based calibration algorithm that fuses vehicle-mounted LiDAR point clouds with roadside camera images. A neural network is then used to regress the camera's rotation deviation relative to its initial position. Unlike vehicle cameras, roadside cameras are typically hinge-mounted on infrastructure, limiting significant translational shifts. Minor deviations (centimeterscale) have negligible impact on perception in V2X scenario [26, 35], whereas small rotational shifts, given the wider field of view, greatly degrade image quality (see Sec. 4.5). Thus, our primary focus is on the rotational deviation relative to the initial setup.
+
+Inspired by the optical flow estimation [29, 33] and lidar-camera calibration task [17, 21], we project vehicle LiDAR point clouds onto roadside camera images to generate a depth map. Then, using a ResNet + FPN (Feature Pyramid Networks) [18], we extract multi-scale features to form a 4D correlation volume. We iteratively update a  $2 \times H \times W$  feature matrix representing pixel-level correspondences between the depth map and the RGB image, using this matrix to associate new features from the 4D correlation volume. Our Mamba architecture further integrates spatial and temporal data, combining time-series information with feature matrices from prior iterations to overcome single-frame perception limitations. We divide the training process into two stages which will be explained in Sec. 4.2.
+
+Compared to traditional roadside camera calibration methods, our approach fully utilizes environmental data, including but not limited to the vehicle itself. Meanwhile, it allows calibration at any road location as needed, without any pre-prepared reference equipment or images for these cameras, making it highly deployable. Compared to the ex
+
+isting LiDAR-camera calibration algorithms used on a single vehicle, our carefully designed network mitigates the issue of point clouds collected by vehicles in different frames occupying a relatively small and uncertain area in the roadside camera's view, delivering robust calibration results in V2X scenarios with fewer parameters.
+
+Our main contributions are summarized as follows:
+
+- To the best of our knowledge, we are the first to propose a target-less V2X-based roadside camera calibration solution.  
+- We designed a novel calibration network that combines multi-scale processing, iterative updates, and the mamba architecture to address the instability issues encountered when directly applying traditional LiDAR-camera calibration methods to V2X scenarios, while also improving calibration efficiency.  
+- We validated the effectiveness and robustness of our method on the V2X-Seq [37] and TUMTraf-V2X [43] datasets.
+
+# 2. Related Works
+
+Roadside camera calibration: A classic approach to roadside camera calibration is target-based calibration, typically using checkerboard patterns or other reference objects [39]. However, this method demands extensive manual effort and is impractical in busy traffic scenarios. To address this, some studies leverage vehicle-provided positioning or motion trajectories, aligning them with detection boxes [28, 30] or estimated trajectories [40] from roadside cameras. While eliminating the need for placing specific targets, these approaches depend heavily on detection accuracy, leading to error accumulation. Other methods exploit road geometry, such as vanishing points [12], or match perspective features like parallel curves [4], but suffer from limited accuracy in complex scenes. More recent work utilizes prepared reference images or 3D reconstructions of road environments [2, 6, 31], which are labor-intensive to collect and unsuitable for real-time deployment. Co-CalibNet [36] introduces additional roadside sensors, but this increases roadside deployment costs and calibration workload. To overcome these limitations, we propose a fundamentally different, target-less calibration framework that utilizes vehicle sensor data to assist roadside camera calibration without relying on physical targets, vision-based detection, or pre-built scene representations.
+
+LiDAR-camera calibration: LiDAR-camera calibration methods can be broadly categorized into target-based and target-less methods [17]. Traditional methods typically rely on specific calibration targets, such as checkerboards, spherical markers, or custom boards, transforming the calibration problem into an optimization problem. Recent research has shifted towards target-less methods, primarily applied to extrinsic parameter estimation for monoc
+
+ular camera and LiDAR systems on a single car. Li et al. [16] categorizes target-less calibration methods into four types: information-theoretic, feature-based, ego-motion-based, and learning-based approaches. Among these, deep learning methods have garnered widespread attention due to their promising results. RegNet [25] was the first to adopt a deep learning-based approach, using a network to extract features and then regress the parameters. CalibNet [11] introduced a geometry-supervised deep network to perform real-time calibration by maximizing geometric and photometric consistency. LCCNet [21] and CaLiCaNet [23] utilized cost volume to establish correspondences between features, which significantly improved upon previous deep learning-based calibration methods. Zhu et al. [41] use depth estimation to mitigate the modal differences. The latest researches [14, 34] attempt to enhance the form of cost volume using new architectures like Transformer. However, these deep learning-based methods perform poorly in V2X scenarios. That's because vehicle-mounted LiDAR often covers only a small portion, posing higher demands for cross-modal feature matching. Moreover, the covered area of depth maps remains nearly constant in the single vehicle's view, while the point clouds collected by the vehicle occupy different regions over time from a roadside perspective. Our method aims to address these problems by using temporal information and a better cost volume update mechanism.
+
+State Space Models: Recently, State-Space Models (SSMs) have excelled in modeling long sequences, particularly in capturing long-range dependencies. The Structured State-Space Sequence (S4) [9] has been proposed as an alternative to Convolutional Neural Networks (CNNs) and Transformers, offering the advantage of linear complexity for efficient handling of long sequences. As research on S4 continues to deepen, various improved models have emerged, such as the S5 layer [27] which introduces MIMO SSMs and efficient parallel scanning and the Gated State-Space layer (GSS) [22]. These models have shown promising performance in language modeling tasks. Excitingly, the recent introduction of the data-dependent SSM layer has led to the development of Mamba [8], which outperforms Transformers of various sizes on large-scale real-world data and achieves linear scaling in sequence length. Recent studies have attempted to bring Mamba model into the visual domain. Vision Mamba [42] borrows the concept from Vision Transformers [19] by marking image sequences with position embeddings and compressing the visual representation using bidirectional state space models. Furthermore, Video Mamba [15] offers a linear-complexity approach for modeling dynamic spatiotemporal contexts, making it ideal for high-resolution long videos. Research indicates that the Mamba architecture has advantages over LSTM and Transformer architectures in video processing tasks. Building on
+
+Mamba, our work aims to achieve more efficient temporal modeling with reduced computational cost. We draw on visual-domain Mamba applications to integrate LiDAR and roadside camera temporal features at the representation level, thereby mitigating calibration failures caused by poor single-frame data quality.
+
+# 3. Method
+
+In this section, we present MamV2XCalib, a novel roadside camera calibration method. The framework of our approach is shown in Fig. 2. In the first stage, we review the mathematical definition of the roadside camera calibration problem (Sec. 3.1) so as to better understand our newly proposed calibration strategy. Next, we provide a detailed explanation of our network design, loss function, and inference process.
+
+# 3.1. Problem Formulation and Input Processing
+
+The modeling of a standard perspective camera can be described by the correspondence between 3D spatial points and pixels.
+
+$$
+d _ {i} \left[ \begin{array}{l} u _ {i} \\ v _ {i} \\ 1 \end{array} \right] = \mathbf {K T} \left[ \begin{array}{l} x _ {i} \\ y _ {i} \\ z _ {i} \\ 1 \end{array} \right] = \mathbf {K} \left[ \begin{array}{l l} \mathbf {R} & \mathbf {t} \\ \mathbf {0} & 1 \end{array} \right] \left[ \begin{array}{l} x _ {i} \\ y _ {i} \\ z _ {i} \\ 1 \end{array} \right] \tag {1}
+$$
+
+where  $\left[x_i\quad y_i\quad z_i\quad 1\right]^T$  represents the homogeneous coordinates of a 3D point in an arbitrary known coordinate system  $A$  and  $\left[u_i\quad v_i\quad 1\right]^T$  denotes a homogeneous camera image coordinates of a picture.  $\mathbf{K}$  denotes the camera's intrinsic parameters, while  $\mathbf{R}$  and  $\mathbf{t}$  represent the rotation and translation matrices, which transform the coordinates from system  $A$  to the camera coordinate system.  $d_{i}$  represents the projection depth of the 3D point. Specifically, in our method,  $A$  is set as the vehicle LiDAR coordinate system. Correspondingly,  $\mathbf{R}$  and  $\mathbf{t}$  represent the transformation from the onboard lidar coordinate system to the camera coordinate system. According to the description in the introduction, we assume that these parameters' initial values are given according to the expected calibration values, denoted as  $K_{init}$ ,  $R_{init}$ ,  $t_{init}$ , where  $K_{init}$  and  $t_{init}$  are stable and easy to deal with, and changes mainly occur in  $R_{init}$ :
+
+$$
+T _ {i n i t} = \Delta T \cdot T _ {L C} \tag {2}
+$$
+
+$$
+\Delta T = \left[ \begin{array}{c c} \mathbf {R} _ {\text {e r r o r}} & \mathbf {0} \\ \mathbf {0} & 1 \end{array} \right] \tag {3}
+$$
+
+where  $T_{LC}$  represents the transformation from the current LiDAR coordinate system to the camera coordinate system.  $\mathbf{R}_{\mathrm{error}}$  is the rotational deviation.  $T_{init}$  is artificially set and our goal is to automatically obtain the value of  $\Delta T$  through sensor data, which can determine the current position of the
+
+![](images/9e6939c2882ff616e531e7389130c15e8e555599d8970c71081ba51480216bb2.jpg)  
+Figure 2. The overall framework of MamV2XCalib. Initially, the point clouds are projected into the camera view using inaccurate initial extrinsic parameters. The network input consists of multiple frames of erroneous depth maps and roadside camera images. Multi-scale features are extracted from the images and depth maps through three channels, forming 4D correlation volumes. A GRU block is utilized to recurrently update the pixel-level correspondences between the images and depth maps (calibration flow) by retrieving features from the volume based on existing estimates. A series of calibration flows generated from multiple frames and iterations is divided into patches and fed into the Mamba model, with temporal and spatial embeddings. An additional token summarizes the features to regress the rotation deviation value.
+
+roadside camera in the world coordinate system. Before being fed into the network, the sensor data needs to be aligned to a unified coordinate system. The vehicle's point cloud is projected onto the roadside camera plane using the initial parameters  $T_{init}$  and Eq. 1.
+
+# 3.2. Multi-scale feature extraction
+
+Our method employs three feature extraction branches to separately extract image information, depth map information, and image context. The two branches processing camera images both use a pre-trained ResNet-18 as the backbone. To obtain richer multi-scale features, and considering that highly abstract feature maps are not conducive to the subsequent search for pixel-level relationships, we fuse the upsampled feature map of latter layers with the former layers to obtain the final feature map. Finally, the output of image encoder  $F_{1}$  and  $F_{\text{context}}$  is  $\mathbb{R}\frac{H}{8}\times \frac{W}{8}\times D$  where we set  $D = 256$ . The depth map branch architecture  $F'$  is similar to the image branch, except that the input channel is modified to one dimension, and pre-trained parameters are not loaded. Due to the unique nature of depth maps, the three branches do not share parameters.
+
+# 3.3. Feature Matching and Iterative refinement
+
+We directly associate the correspondences between roadside images  $I_{1} \in \mathcal{I}$  and depth maps  $I_{2} \in \mathcal{D}$  with the deviation of the roadside camera's extrinsic parameters. This
+
+pixel-level correspondence can be regarded as a special type of flow field, which we refer to as the calibration flow in this paper. Inspired by the RAFT [29] model used for optical flow estimation, we construct a multi-scale 4D correlation volume as follows:
+
+$$
+I n n \left(F _ {1} \left(I _ {1}\right), F ^ {\prime} \left(I _ {2}\right)\right) \in \mathbb {R} ^ {h \times w \times h \times w} \tag {4}
+$$
+
+$$
+U _ {k} = \operatorname {P o o l} (I n n) \in \mathbb {R} ^ {h \times w \times \frac {h}{2 ^ {k}} \times \frac {w}{2 ^ {k}}} \tag {5}
+$$
+
+where  $Inn$  is calculated through inner product and Pool is designed to get multi-scale 4D correlation volume. Through these operations, the similarity between any two pixels is stored in this volume. Next, we repeatedly query features from the 4D dictionary to estimate pixel correspondences based on the former correspondences. We initialize the flow field (pixel correspondences) to zeros. In each iteration, given the feature flow map, we take each pixel's corresponding point as the center and get the similarity of all pixels within a distance of  $r$ . We call this operation LookUp.
+
+$$
+h ^ {\prime \prime} = \operatorname {G R U} (\text {L o o k U p} \{U _ {k}, f, r \}, h ^ {\prime}, F _ {\text {c o n t e x t}} (I _ {1})) \tag {6}
+$$
+
+$$
+d f = \operatorname {F l o w} \left(h ^ {\prime \prime}\right) \tag {7}
+$$
+
+We then input the features we just looked up, hidden states from last iteration, and context features together into the GRU module, obtaining new hidden states. The flow residuals  $df$  is predicted by layer Flow consisting of two convolutional layers. The residual  $df$  is added to the previous
+
+flow field to update it. In this way, we obtain a series of flow field estimates  $f_{i} \in \mathcal{F}$  equal to the number of iterations.
+
+To ensure that the model can get calibration flow fields, in the first stage of training, we directly regress the extrinsic parameters from each flow field obtained at every step using two fully connected layers.
+
+# 3.4. Global Aggregation with Mamba
+
+While our 4D correlation volume effectively captures global correlations between inaccurate depth maps and images, the unique V2X scenario poses additional challenges. The already sparse point clouds become even sparser due to viewpoint disparities between roadside and vehicle perspectives. Moreover, the effective regions of depth maps shift over time, making it inherently difficult to derive accurate calibration flow fields in uncertain, data-scarce regions using single-frame data alone. Directly regressing extrinsics from all correspondences would inevitably introduce bias.
+
+Fortunately, although the vehicle is in motion, the roadside camera's deviation from its initial position remains fixed over a calibration period. This stability allows us to perform temporal fusion, integrating data across frames to overcome the limitations described above. We select Mamba because our sequence of calibration flow fields has dimensions akin to video data, where Transformers excel in fusion tasks. However, our iterative process produces a large number of tokens, prompting us to adopt the latest Mamba [15, 42] for its superior efficiency in handling such data.
+
+Specifically, we extend the input from a single frame to a sequence. According to the method in Sec. 3.3, we can obtain a calibration flow field of size  $\mathbb{R}^{T\times i\times h\times w}$ . We treat the iterative process and temporal dimension as two separate axes. First, we concatenate the flow field maps obtained from each frame into an overall map of size  $\mathbb{R}^{T\times h\times (w\times i)}$ . Then we divide it into a series of patches  $x_{n}^{t}$  instead of flattening them simply and project them into a hidden space through convolutional layers. The patches of the frames are arranged as shown in Fig. 2. Similar to [1, 15], we add positional and temporal embeddings  $p_{s}$  and  $p_t$  to retain the spatiotemporal distinctions and insert an additional token  $z_{add}$  into the entire sequence to summarize the information.
+
+$$
+Z = \left[ z _ {a d d}, \mathbf {E} x _ {1} ^ {1}, \mathbf {E} x _ {2} ^ {1}, \dots , \mathbf {E} x _ {N} ^ {T} \right] + p _ {s} + p _ {t} \tag {8}
+$$
+
+$$
+z _ {a d d} = \operatorname {M a m} (Z) \tag {9}
+$$
+
+where the projection by  $\mathbf{E}$  is equivalent to 3D convolution and Mam is the bidirectional mamba block. Through the Mamba model, we construct state space and scan it as illustrated, enabling the model to selectively model different regions of each flow field map from a spatial perspective. Additionally, this allows the model to automatically associate features generated from data with varying quality at different timestamps. The added token summarizes the series of
+
+lengthy calibration flow maps generated through iterations and temporal processes.
+
+After obtaining the refined summarized features  $z_{add}$ , we use two fully connected layers to regress the quaternion, achieving the desired estimation of the rotational error.
+
+# 3.5. Loss Function
+
+For each obtained rotation prediction, our loss has the following form:
+
+$$
+L _ {i} = \lambda_ {r} L _ {r} + \lambda_ {p} L _ {p} \tag {10}
+$$
+
+where  $\lambda_r$  and  $\lambda_p$  denote respective loss weights.  $L_r$  is the rotation loss and we use angular distance to represent the difference between quaternions. Considering that our method utilizes coordinate transformations of point clouds  $P_i$ , we calculate the distance between the two point clouds after the actual transformation and the predicted transformation to obtain  $L_p$ :
+
+$$
+L _ {p} = \frac {1}{N} \sum_ {i = 1} ^ {N} \left\| T _ {L C} ^ {- 1} \cdot T _ {p r e d} ^ {- 1} \cdot T _ {i n i t} \cdot P _ {i} - P _ {i} \right\| _ {2} \tag {11}
+$$
+
+where  $T_{LC}$  is the ground truth LiDAR-Camera extrinsic matrix and  $T_{pred}$  is the output of the calibration network.
+
+In the first training stage, we iteratively obtain  $n$  estimates per frame, with the loss as a weighted sum of their individual losses using exponentially increasing factors.
+
+$$
+L = \sum_ {i = 1} ^ {n} \gamma^ {n - i} L _ {i} \tag {12}
+$$
+
+In the second stage, multi-frame data is input, with each frame producing a single estimate, and the loss is defined as in Eq. 10.
+
+# 3.6. Calibration Inference
+
+One of our inputs, the depth map, is influenced not only by the quality of the point cloud itself but also by the initially given extrinsic parameters. To address significant deviations and maintain consistency with LCCNet [21], we concatenate multiple networks that have the same architecture but are trained under different noise conditions. We then reproject the depth maps based on the predictions  $T_{i}$  from the previous network to obtain higher-quality input data. Therefore, during the final inference, we have:
+
+$$
+\tilde {T} _ {L C} = \left(T _ {0} \cdot T _ {1} \dots T _ {n}\right) ^ {- 1} T _ {\text {i n i t}} \tag {13}
+$$
+
+# 4. Experiment
+
+We conduct extensive experiments on two real-world datasets, V2X-Seq [37] and TUMTraf-V2X [43]. In this section, we will specifically introduce the training details and present the experimental results.
+
+<table><tr><td rowspan="2">Deviation</td><td rowspan="2">Method</td><td colspan="4">Mean (°)</td><td colspan="4">Std (°)</td></tr><tr><td>Total</td><td>Roll</td><td>Pitch</td><td>Yaw</td><td>Total</td><td>Roll</td><td>Pitch</td><td>Yaw</td></tr><tr><td>(−20°, +20°)</td><td>LCCNet [21]</td><td>1.0741</td><td>0.1788</td><td>0.6056</td><td>0.6455</td><td>2.5336</td><td>0.1537</td><td>2.4624</td><td>0.7920</td></tr><tr><td>(−20°, +20°)</td><td>Calib-anything [20]</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(−20°, +20°)</td><td>Ours</td><td>0.6313</td><td>0.1867</td><td>0.2409</td><td>0.4780</td><td>0.3211</td><td>0.1489</td><td>0.1860</td><td>0.3500</td></tr><tr><td>(−10°, +10°)</td><td>LCCNet [21]</td><td>0.7909</td><td>0.1792</td><td>0.3440</td><td>0.5548</td><td>1.6825</td><td>0.1111</td><td>1.6147</td><td>0.5985</td></tr><tr><td>(−10°, +10°)</td><td>Calib-anything [20]</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>(−10°, +10°)</td><td>Ours</td><td>0.5858</td><td>0.2260</td><td>0.2441</td><td>0.4001</td><td>0.2428</td><td>0.1525</td><td>0.1745</td><td>0.2776</td></tr></table>
+
+Table 1. Comparison results with other calibration methods on V2X-Seq dataset [37]. Calib-anything [20] fails. (See Fig. 4)
+
+![](images/6c8fb4a0b52e64dd5f8bf0c7c042c056f42f7d5f29f2b39d1485266be55259cd.jpg)  
+(a) Ground Truth
+
+![](images/c874a75bcb8b45df15c36d3d4d9d363317fd753c6e86f7c574208ef894751ea7.jpg)  
+(b) Before Calibration
+
+![](images/0ea1d89586b946c68c096a6ddfac7f2a39cb05cb50aaa1a46b53d2d154e49788.jpg)  
+(c) LCCNet  
+Figure 3. Qualitative reprojection results. First row: On V2X-Seq dataset, within the initial large deflection range of  $(-20^{\circ}, +20^{\circ})$ , LCCNet [21] shows significant deviation in some scenes, while our method remains accurate. Second row: TUMTraf-V2X (daytime). Third row: TUMTraf-V2X (nighttime). The initial deflections are within a small range of  $(-5^{\circ}, +5^{\circ})$  and our method outperforms better.
+
+![](images/a69110ba1f4d78e45ac9d7a68dec1f01d00174726fd5403f12782d65f71b764d.jpg)  
+(d) MamV2XCalib
+
+# 4.1. Dataset
+
+V2X-Seq: Yu et al. [37] introduce a large-scale real-world cooperative vehicle-infrastructure dataset. It contains a large amount of time-synchronized data from vehicle-side and roadside sensors, as well as transformations between multiple coordinate systems.
+
+TUMTraf-V2X: There is another V2X dataset named TUMTraf-V2X [43]. It focuses on challenging traffic scenarios and various day and nighttime scenes. We utilize vehicle-side LiDAR data and roadside camera data.
+
+Similar to single-vehicle LiDAR-camera calibration tasks, we add random noise within a specified range to the extrinsic parameters to generate abundant training data. Specifically, after applying a random perturbation  $\Delta T$  to  $T_{LC}$ , we obtain the initial extrinsic parameter  $T_{init} = \Delta T \cdot T_{LC}$ . By randomly changing the deviation value, we can acquire a large amount of training data.
+
+# 4.2. Training Details
+
+We train our network on a single A100 GPU, with the training process divided into two stages. Next, I will elaborate on the training process and use the hyperparameter settings on V2X-Seq [37] as an example.
+
+In the first stage, we perform calibration using only single-frame data. Specifically, we exclude the Mamba module and directly regress the extrinsic parameters from the calibration flow map. This approach allows the network to focus on accurately predicting the calibration flow map. We set the initial learning rate to 0.00003 and train for 70 epochs, after which the learning rate is reduced until convergence. All available paired data is utilized in this stage.
+
+In the second stage, we train the Mamba module based on the first stage, enabling the network to fuse multiple calibration flow maps generated over time. The initial learning rate is set to 0.0001 and decays during training. We begin
+
+<table><tr><td>Experiment</td><td>Temporal</td><td>Iteration</td><td>4D Correlation Volume</td><td>Mean (○)</td><td>Std (○)</td><td>Param (M)</td></tr><tr><td>Ours</td><td>Mamba[8]</td><td>✓</td><td>✓</td><td>0.6313</td><td>0.3211</td><td>47</td></tr><tr><td>w/o Mamba (+Trans)</td><td>Timesformer[3]</td><td>✓</td><td>✓</td><td>0.6683</td><td>0.3808</td><td>55</td></tr><tr><td>w/o Temporal</td><td>-</td><td>✓</td><td>✓</td><td>0.9742</td><td>2.0013</td><td>41</td></tr><tr><td>w/o Iteration</td><td>Mamba[8]</td><td>-</td><td>✓</td><td>0.7036</td><td>1.6162</td><td>47</td></tr><tr><td>w/o Temporal and Iteration</td><td>-</td><td>-</td><td>✓</td><td>1.1025</td><td>2.9178</td><td>47</td></tr><tr><td>3D feature matching [21]</td><td>-</td><td>-</td><td>-</td><td>1.0741</td><td>2.5336</td><td>70</td></tr></table>
+
+by freezing the network components preceding the Mamba module and then jointly update all network parameters. To enhance training data quality, we use data where the horizontal distance between vehicles and infrastructure is less than 50 meters.
+
+# 4.3. Evaluation
+
+During evaluation, we performed distance filtering before inputting the data into the network. The calibration system is activated only when the vehicle is within certain distance. We will explain the benefits of this operation in Sec. 4.5.
+
+On V2X-Seq Dataset [37]: Tab. 1 highlights the effectiveness of our method in roadside camera calibration tasks. For a mis-calibration range of  $(-20^{\circ}, +20^{\circ})$ , our approach achieves a mean calibration error of  $0.6313^{\circ}$ , with mean Euler angle errors of  $[0.1867^{\circ}, 0.2409^{\circ}, 0.4780^{\circ}]$ . We compare our method to a high-performing single-vehicle LiDAR-camera calibration technique [21], demonstrating that our approach offers superior accuracy in V2X scenarios over directly applying the single-vehicle method. Furthermore, we assess its robustness. Our method significantly reduces the likelihood of extreme calibration failures, lowering the standard deviation of the angle-error from  $2.5336^{\circ}$  to  $0.3211^{\circ}$ —a reduction by an order of magnitude. The deviations within this range are manageable by existing perception methods [35]. When the initial error is reduced to  $(-10^{\circ}, +10^{\circ})$ , our method yields even better results while maintaining greater robustness and a lower risk of severe errors. Additionally, we present a qualitative comparison with the latest train-free LiDAR-camera calibration method [20] in Fig. 4. In V2X scenarios with substantial camera deflection, the competing method fails, whereas our approach consistently succeeds. We also compared with a traditional LiDAR-camera calibration method [13] (see Appendix 4.3 for details).
+
+On TUMTraf-V2X Dataset [43]: Tab. 3 presents comparative results on the TUMTraf-V2X dataset, where our method remains competitive. For a mis-calibration range of  $(-5^{\circ}, +5^{\circ})$ , our approach achieves a mean calibration error of  $0.267^{\circ}$ . Remarkably, our calibration strategy proves effective even in nighttime scenarios (see Fig. 3).
+
+To ensure a fair comparison, we retrained LCCNet [21]
+
+on the same dataset and modified its output to predict only rotational errors, aligning it with our network's configuration. Fig. 3 shows that after passing through our calibration network, effective calibration can still be achieved despite significant initial extrinsic parameter deviations. Based on the calibrated position of the roadside camera, the vehicle point clouds can be well-matched with the images from the perspective of the roadside camera.
+
+![](images/c75b3a106571c9351b554798066458306d6c85791fc01bd92e43432f93fe1b35.jpg)  
+Figure 4. Ground Truth (Left). Calib-Anything [20] (Middle) fails to calibrate within  $(-20^{\circ}, +20^{\circ})$ , while ours (Right) succeeds.
+
+![](images/cd90a9f39e21523bd7d723f89c57c8d8ff2a363c8c42e8033600a4f5e1cf0151.jpg)
+
+![](images/307e722c8844b8c66b250cb1465377282930b5309e9eb9a390fb20ee1cbfae71.jpg)
+
+Table 2. Ablation study on the effect of different components of our model.  
+
+<table><tr><td>Deviation</td><td>Method</td><td>Mean (°)</td><td>Std (°)</td></tr><tr><td>(−5°, +5°)</td><td>LCCNet [21]</td><td>0.389</td><td>0.608</td></tr><tr><td>(−5°, +5°)</td><td>Ours</td><td>0.267</td><td>0.280</td></tr></table>
+
+Table 3. Comparison results on TUMTraf-V2X dataset [43].
+
+# 4.4. Ablations and Analysis
+
+We conducted a series of ablation experiments to demonstrate the importance of each component.
+
+Mamba: First, we removed the Mamba component used for spatiotemporal fusion while retaining feature flow estimation for direct regression. Results are shown in Tab. 2. It can be observed that the Mamba module significantly improves calibration stability, greatly reducing the standard deviation of the estimated values, which indicates the necessity of this module. Additionally, we attempted to replace it with a Transformer-based Timesformer [3] module. It can be seen that the Mamba-based method achieves relatively better results with a smaller number of parameters.
+
+Iteration: Our method refines the calibration flow progressively through iteration. During inference, we iterate 10 times to generate 10 calibration flow maps, which together provide spatial dimensional information. We verified
+
+that using only a single iteration to produce the calibration flow during inference significantly weakens the network's capability, indicating that generating calibration flow maps through multiple iterations is essential.
+
+Multi-scale 4D Correlation Volume: We replace the 4D correlation volume with the 3D feature matching layer in LCCNet [21], and find it costs much more parameters and perform worse than 4D correlation volume with iteration (marked by w/o Temporal).
+
+# 4.5. Other experiments about V2X Calibration
+
+Why Ignore Translation Noise: Fig. 5 shows the point cloud projection deviations caused by translational and rotational errors in a V2X scenario. It is evident that minor translational have a negligible impact on the perception system and can be disregarded. In contrast, rotational errors often lead to significant matching discrepancies in vehicle-road data. The experiment in the appendix demonstrate that such slight deviations do not significantly affect rotational calibration. We attribute this to the fact that, in V2X scenarios, the absolute distance between the vehicle and the road typically far exceeds the potential translational offset of roadside cameras due to environmental factors. Therefore, it is reasonable to simplify the calibration problem of roadside cameras by focusing on rotation.
+
+Necessity of the Mamba Module: To further substantiate the importance of the Mamba module, we expanded our ablation study by comparing pre-fusion and post-fusion approaches. As shown in Tab. 4, on the V2X-Seq dataset [37], our method markedly outperforms two alternatives: directly stacking multi-frame point clouds or applying post-processing to the frames using a single-frame technique.
+
+Distance Filtering: By utilizing vehicle mobility, we can easily choose data with shorter distances between vehicles and infrastructure for calibration. This distance can be calculated before feeding it into the neural network, allowing us to initially evaluate data quality based on distance to boost calibration accuracy. This is an advantage that earlier methods lack. In Fig. 6, we show the mean error and standard deviation of calibration using vehicle point clouds across various distance ranges. Experiments demonstrate that filtering data by distance improves data quality and strengthens the method's robustness.
+
+Migration to single car calibration: Although our method was originally designed for V2X, it can naturally be extended to single car where translation errors must be considered. The results are shown in the attachment.
+
+# 4.6. Limitation
+
+Our method relies on vehicle-side LiDAR perception data, which places certain demands on the quality of vehicle-side sensors. Additionally, although the V2X-based calibration strategy offers benefits such as target-free operation, high
+
+![](images/215ae92200735965a91400cb53e70059d21beb028568d52309e91f9acc900b34.jpg)  
+Figure 5. The impact of extrinsic noise. Ground Truth (Left). Translated within  $10\mathrm{cm}$  (Middle). Rotated within  $20^{\circ}$  (Right).
+
+![](images/2673a0e6f523f29e3912ed984fa9de56e19c09e038326e89cbed574ea2319f9a.jpg)
+
+![](images/41d28ffc49762b2da5baa91c5b2a7d7203306ad276513104974007bf8f401746.jpg)
+
+<table><tr><td>Fusion Methods</td><td>Mean (°)</td><td>Std (°)</td></tr><tr><td>stacking points</td><td>0.952</td><td>2.184</td></tr><tr><td>average</td><td>0.974</td><td>2.001</td></tr><tr><td>least square fitting</td><td>0.820</td><td>1.254</td></tr><tr><td>ours</td><td>0.631</td><td>0.321</td></tr></table>
+
+Table 4. Comparison with other fusion methods on the V2X-Seq dataset [37] (initial deviation  $(-20^{\circ}, +20^{\circ})$ ).
+
+![](images/ba56af16ec2d01fb54ff7431a0b283bf62d006c180d5990ec4126c7107bc734a.jpg)  
+Figure 6. Calibration result on V2X-Seq dataset [37] after filtering vehicle point cloud data with a horizontal distance between the vehicle and roadside camera of distance  $< d$ .
+
+![](images/dda8659ef8ece9e23f7464d480527f5fa0a3cf7a4ccc78e2a5ecb01368ead7a1.jpg)
+
+flexibility, and large-scale deployment, it also introduces vehicle-to-infrastructure information interaction, which imposes higher requirements on time synchronization. Besides, similar to other deep learning-based calibration methods, fine-tuning is necessary to achieve good results when transferring to entirely different datasets.
+
+# 5. Conclusion
+
+This paper proposes a target-less infrastructure camera calibration strategy based on vehicle-road collaboration and introduces a novel method designed specifically for V2X scenarios. Compare to existing approaches, our strategy makes full use of environmental information and is suited for busy road scenarios. We present an iterative method that establishes pixel-level correspondences between depth maps and images. By incorporating the Mamba model's strengths in temporal modeling, we overcome instability challenges in single-vehicle calibration applied to V2X contexts with fewer parameters. Our method proves effective and robust on two datasets. We hope that it can inspire future related study.
+
+# Acknowledgements
+
+This work is supported by National Science and Technology Major Project (2022ZD0115502), and Wuxi Research Institute of Applied Technologies, Tsinghua University under Grant 20242001120.
+
+# References
+
+[1] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6836-6846, 2021. 5  
+[2] Esra Ataer-Cansizoglu, Yuichi Taguchi, Srikumar Ramalingam, and Yohei Miki. Calibration of non-overlapping cameras using an external slam system. In 2014 2nd International Conference on 3D Vision, pages 509-516. IEEE, 2014. 2  
+[3] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, page 4, 2021. 7  
+[4] Eduardo R Corral-Soto and James H Elder. Automatic single-view calibration and rectification from parallel planar curves. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV 13, pages 813-827. Springer, 2014. 2  
+[5] Jiaxun Cui, Hang Qiu, Dian Chen, Peter Stone, and Yuke Zhu. Coopernaut: End-to-end driving with cooperative perception for networked vehicles. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17252-17262, 2022. 1  
+[6] Giacomo D'Amicantonio, Egor Bondarev, et al. Automated camera calibration via homography estimation with gnns. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 5876-5883, 2024. 2  
+[7] Markéta Dubská, Adam Herout, Roman Juránek, and Jakub Sochor. Fully automatic roadside camera calibration for traffic surveillance. IEEE Transactions on Intelligent Transportation Systems, 16(3):1162-1171, 2014. 2  
+[8] Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023. 3, 7  
+[9] Albert Gu, Karan Goel, and Christopher Ré. Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396, 2021. 3  
+[10] Lei Huang and Wenzhun Huang. Rd-yolo: An effective and efficient object detector for roadside perception system. Sensors, 22(21):8097, 2022. 1  
+[11] Ganesh Iyer, R Karnik Ram, J Krishna Murthy, and K Madhava Krishna. Calibnet: Geometrically supervised extrinsic calibration using 3d spatial transformer networks. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1110-1117. IEEE, 2018. 3  
+[12] Viktor Kocur and Milan Ftáčnik. Traffic camera calibration via vehicle vanishing point detection. In Artificial Neural Networks and Machine Learning-ICANN 2021:
+
+30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings, Part V 30, pages 628-639. Springer, 2021. 2  
+[13] Kenji Koide, Shuji Oishi, Masashi Yokozuka, and Atsuhiko Banno. General, single-shot, target-less, and automatic lidar-camera extrinsic calibration toolbox. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 11301-11307. IEEE, 2023. 7  
+[14] Yu-Chen Lee and Kuan-Wen Chen. Lccraft: Lidar and camera calibration using recurrent all-pairs field transforms without precise initial guess. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 16669-16675. IEEE, 2024. 3  
+[15] Kunchang Li, Xinhao Li, Yi Wang, Yinan He, Yali Wang, Limin Wang, and Yu Qiao. Videomamba: State space model for efficient video understanding. In European Conference on Computer Vision, pages 237-255. Springer, 2025. 3, 5  
+[16] Xingchen Li, Yuxuan Xiao, Beibei Wang, Haojie Ren, Yanyong Zhang, and Jianmin Ji. Automatic targetless lidar-camera calibration: a survey. Artificial Intelligence Review, 56(9):9949-9987, 2023. 3  
+[17] Kang Liao, Lang Nie, Shujuan Huang, Chunyu Lin, Jing Zhang, Yao Zhao, Moncef Gabbouj, and Dacheng Tao. Deep learning for camera calibration and beyond: A survey. arXiv preprint arXiv:2303.10559, 2023. 2  
+[18] Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117-2125, 2017. 2  
+[19] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021. 3  
+[20] Zhaotong Luo, Guohang Yan, and Yikang Li. Calib-anything: Zero-training lidar-camera extrinsic calibration method using segment anything. arXiv preprint arXiv:2306.02656, 2023. 6, 7  
+[21] Xudong Lv, Boya Wang, Ziwen Dou, Dong Ye, and Shuo Wang. Lccnet: Lidar and camera self-calibration using cost volume network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2894-2901, 2021. 2, 3, 5, 6, 7, 8  
+[22] Harsh Mehta, Ankit Gupta, Ashok Cutkosky, and Behnam Neyshabur. Long range language modeling via gated state spaces. arXiv preprint arXiv:2206.13947, 2022. 3  
+[23] Arya Rachman, Jürgen Seiler, and André Kaup. End-to-end lidar-camera self-calibration for autonomous vehicles. In 2023 IEEE Intelligent Vehicles Symposium (IV), pages 1-6. IEEE, 2023. 3  
+[24] Bernhard Rinner and Wayne Wolf. An introduction to distributed smart cameras. Proceedings of the IEEE, 96(10): 1565-1575, 2008. 1  
+[25] Nick Schneider, Florian Piewak, Christoph Stiller, and Uwe Franke. Regnet: Multimodal sensor registration using deep neural networks. In 2017 IEEE intelligent vehicles symposium (IV), pages 1803-1810. IEEE, 2017. 3
+
+[26] Christoph Schöller, Maximilian Schnettler, Annkathrin Krammer, Gereon Hinz, Maida Bakovic, Müge Güzet, and Alois Knoll. Targetless rotational auto-calibration of radar and camera for intelligent transportation systems. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pages 3934-3941. IEEE, 2019. 2  
+[27] Jimmy TH Smith, Andrew Warrington, and Scott W Linderman. Simplified state space layers for sequence modeling. arXiv preprint arXiv:2208.04933, 2022. 3  
+[28] Jakub Sochor, Roman Juránek, and Adam Herout. Traffic surveillance camera calibration by 3d model bounding box alignment for accurate vehicle speed measurement. Computer Vision and Image Understanding, 161:87-98, 2017. 2  
+[29] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II 16, pages 402-419. Springer, 2020. 2, 4  
+[30] Alexander Tsaregorodtsev, Adrian Holzbock, Jan Strohbeck, Michael Buchholz, and Vasileios Belagiannis. Automated static camera calibration with intelligent vehicles. In 2023 IEEE Intelligent Vehicles Symposium (IV), pages 1-7. IEEE, 2023. 2  
+[31] Khiem Vuong, Robert Tamburo, and Srinivasa G Narasimhan. Toward planet-wide traffic camera calibration. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 8553-8562, 2024. 2  
+[32] Kunfeng Wang, Hua Huang, Yuantao Li, and Fei-Yue Wang. Research on lane-marking line based camera calibration. In 2007 IEEE International Conference on Vehicular Electronics and Safety, pages 1-6. IEEE, 2007. 1  
+[33] Yihan Wang, Lahav Lipson, and Jia Deng. Sea-raft: Simple, efficient, accurate raft for optical flow. In European Conference on Computer Vision, pages 36-54. Springer, 2025. 2  
+[34] Yuxuan Xiao, Yao Li, Chengzhen Meng, Xingchen Li, Jian-min Ji, and Yanyong Zhang. Calibformer: A transformer-based automatic lidar-camera calibration network. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 16714-16720. IEEE, 2024. 3  
+[35] Lei Yang, Kaicheng Yu, Tao Tang, Jun Li, Kun Yuan, Li Wang, Xinyu Zhang, and Peng Chen. Bevheight: A robust framework for vision-based roadside 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21611-21620, 2023. 2, 7  
+[36] Chen Yaqing and Wang Huaming. Robust extrinsic calibration for lidar-camera systems via depth and height complementary supervision network. IEEE Access, 2025. 2  
+[37] Haibao Yu, Wenxian Yang, Hongzhi Ruan, Zhenwei Yang, Yingjuan Tang, Xu Gao, Xin Hao, Yifeng Shi, Yifeng Pan, Ning Sun, et al. V2x-seq: A large-scale sequential dataset for vehicle-infrastructure cooperative perception and forecasting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5486-5495, 2023. 2, 5, 6, 7, 8  
+[38] Xinyu Zhang, Yijin Xiong, Qianxin Qu, Renjie Wang, Xin Gao, Jing Liu, Shichun Guo, and Jun Li. Coopera-
+
+tive visual-lidar extrinsic calibration technology for intersection vehicle-infrastructure: A review. arXiv preprint arXiv:2405.10132, 2024. 1  
+[39] Zhengyou Zhang. A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence, 22(11):1330-1334, 2000. 1, 2  
+[40] Cong Zhao, Delong Ding, Yupeng Shi, Yuxiong Ji, and Yuchuan Du. Graph matching-based spatiotemporal calibration of roadside sensors in cooperative vehicle-infrastructure systems. IEEE Transactions on Intelligent Transportation Systems, 2024. 2  
+[41] Jiangtong Zhu, Jianru Xue, and Pu Zhang. Calibdepth: Unifying depth map representation for iterative lidar-camera online calibration. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 726-733. IEEE, 2023. 3  
+[42] Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417, 2024. 3, 5  
+[43] Walter Zimmer, Gerhard Arya Wardana, Suren Sritharan, Xingcheng Zhou, Rui Song, and Alois C Knoll. Tumtraf v2x cooperative perception dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22668-22677, 2024. 2, 5, 6, 7

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+![](images/655b374c1345b99398188edc6435cda222f790d52078174b9e42683a2139c675.jpg)
+
+# Mamba-3VL: Taming State Space Model for 3D Vision Language Learning
+
+# Yuan Wang $^{1,2}$  Yuxin Chen $^{2}$  Zhongang Qi $^{2}$  Lijun Liu $^{3}$  Jile Jiao $^{1,4}$  Xuetao Feng $^{1,4}$  Yujia Liang $^{5}$  Ying Shan $^{2}$  Zhipeng Zhang $^{6,7‡}$
+
+$^{1}$ Tsinghua University  $^{2}$ ARC Lab, Tencent PCG  $^{3}$ UCAS  $^{4}$ Deepeleph  $^{5}$ HUST
+
+$^{6}$ School of Artificial Intelligence, SJTU  $^{7}$ Anyverse Intelligence  $\ddagger$ Corresponding Author
+
+# Interactive 3D Referring Segmentation
+
+# Single-target Referring Segmentation
+
+![](images/6734a2408b4128529f23c60d77c1f53ef2c6bd269e6e41afb47ce12f9988da2a.jpg)
+
+A black sofa behind the fan and a black backpack is lying on the top of it.
+
+![](images/ed0d66e10bb4e49e7cf0555cefc2a42d17a849de05c8cf7ecb3758bb9188f8b4.jpg)
+
+A circular end table can be found adjacent to a teal couch and an brown arm chair.
+
+# Box-guided Referring Segmentation
+
+![](images/8b06577565eab7fffd10fd590459107b8e8c0426c961f3c43a5c2783c3b8140f.jpg)  
+[-0.07 0.45 0.54]
+
+![](images/c5dbe8f794e5c82321b35983dcf634c8135f94e45d12d68c7d014821955884c7.jpg)  
+[-1.48 -0.41 0.41]
+
+# Interactive Embodied AI Tasks
+
+# Task Planning
+
+![](images/f20ffc303a94a224288b9c73e92e42e42dde2e1b220c8265a7708d25632a42c5.jpg)
+
+# 3D Dialog
+
+![](images/a36817c85aa6a2db9a32e44ca3f8e94e36cac3dfb138ea85bff4db958ec4e23b.jpg)
+
+# Robot Manipulation
+
+![](images/76a07e9726b23381e71c032c843a3a74e95bc2d631ec0d0e8fe906bc5ce89cee.jpg)
+
+# Embodied Navigation
+
+![](images/09e807708c5ac74523936c53ce33eb3d488b3066bd631c5e46d6cb247046a213.jpg)
+
+Human: Please arrange a space for eating.  
+Robot: 1. arrange chairs and ensure them face table.  
+2. Place any objects or decorations on the table.  
+3. Clean the table and chairs, wiping away any dust.  
+4. Check the lighting and temperature in the area.
+
+Q: Hello, could you describe the furniture in the living room?  
+A: Sure! there has a wooden table, two big wooden chair, and a gray upholstered sofa.  
+Q: Thanks for your help! It's I want to know
+
+Human User: pack all the blue andblack sneakers in the brown box
+
+![](images/aacb17d1112341f1efe3d8b889da2757eb9a7f7a4a67dd42b26ffd4447ab6579.jpg)
+
+![](images/2826334a34deb1727795f9e83efb2b260cd9eb2756b6e65ebcbe243403ae4830.jpg)
+
+Find the seating
+
+1. Turn right  
+2. Stop  
+3. Move forward  
+4. Turn left
+
+# Interactive 3D Visual Grounding
+
+# Single-target Grounding
+
+![](images/9f3d03e908fe3ea03a2a01cf39c3cbca327a290c51db158467edd278a38e5dfa.jpg)  
+It is a rectangular bed, with a white mattress and a yellow pillow on top.  
+Figure 1. Mamba-3VL serves as an interactive and general-purpose interface for 3D vision-language tasks, including Referring Segmentation, Visual Grounding, Question Answering, Dense Captioning, while generalizing well on Embodied AI tasks.
+
+![](images/eaf58d2d1587518e1c2360ac2bf5eb1cf5a86b6a3a6a60cd24ac78883c2a2861.jpg)  
+White washing machines are positioned in front of dryers
+
+# Interactive 3D Question Answering & Dense Captioning
+
+# Question Answering
+
+![](images/eda6e270b0ad5e37153a6f560574595f0441e99787ebaba83411614b2675ecc0.jpg)  
+What's black television above? Response: on the nightstand
+
+# Situated Reasoning
+
+![](images/26512d80d7ae7d7aa8c41a7d851e8eca36fbb9d362c1c5d050939c11180a647c.jpg)  
+Standing in front of the coffee table with the window on my left. How many chairs are behind me?
+
+# Dense Captioning
+
+![](images/39a35e8298a990d1e6105083fab72a7af01f4180f8d64d142c572fb68f1b4aaf.jpg)  
+A desk sits behind the brown chairs to both it's left and right. it's to the right of a bookshelf.
+
+# Abstract
+
+3D vision-language (3D-VL) reasoning, connecting natural language with 3D physical world, represents a milestone in advancing spatial intelligence. While transformer-based methods dominate 3D-VL research, their quadratic complexity and simplistic positional embedding mechanisms severely limits effective modeling of long-range 3D-VL dependencies and spatial relationships in 3D-VL tasks. State Space Models (SSM) have emerged as promising linear-complexity alternatives for sequential data processing, while inherent selection mechanism offers notable capability for spatial modeling. Despite its potential, straightforward adoption of Mamba to 3D-VL tasks encounters two obstacles: (1) how to perceive the position of 3D objects and understand complex spatial relationships, and (2) how to achieve thorough synergies of
+
+multi-modal features. In this paper, we propose Mamba-3VL, a pioneering 3D-VL framework to model complex intra- and inter-modality correlations and enhance spatial relation reasoning, while guaranteeing top-tier performance, high efficiency, and generalization potential for 3D-VL tasks. Specifically, Mamba Mixer explicitly models 3D-VL interaction via channel twisting and relation-prioritized spatial scanning policy. It maximally retain spatial relation of object-centric features. To further provide precise spatial encoding for mamba, we develop Instance-aware Dynamic Position Adapter (IDPA) to dynamically adjust instance-specific positional embeddings and enhance local spatial relation of 3D objects. Extensive results validate Mamba-3VL trumps other competitors on seven 3D-VL benchmarks and showcases versatile potentials for challenging Embodied AI tasks.
+
+# 1. Introduction
+
+Spatial Intelligence [31] aims to develop general-purpose assistants to understand the 3D physical world and execute
+
+![](images/1812a76ad0f42ed2baf55803153c399653d49215c670fa7fb7e08f8f616704de.jpg)  
+Figure 2. Comparison of grounding accuracy and model efficiency.
+
+human commands in real scenarios. Towards this goal, numerous datasets and 3D foundation models for benchmarking 3D multi-modal understanding have been proposed, e.g., visual grounding [6, 59, 69], dense captioning [4, 11], and question answering [2, 42]. The primary challenge of these tasks lies in comprehending spatial relationships between 3D objects and effectively modeling 3D-VL interactions.
+
+Drawing inspiration from 2D vision-language (2D-VL) models [10, 14, 28, 33, 34], contemporary 3D-VL models rely heavily on transformer-based cross-modality interaction to fuse 3D scene and linguistic features. Representative works, e.g., 3D-VisTA [73], SceneVerse [25], UniT3D [12], explore pre-trained transformers and contrastive alignment to seek mutual benefits from related 3D-VL tasks. Despite remarkable progress, quadratic complexity of full attention imposes prohibitive overhead. When handling massive points or lengthy texts, it struggles to capture long-range 3D-VL dependencies due to entropy maximization of attention weights. Instead, recent studies [20, 39, 55] develop linear attention mechanism as a scalable alternative. But in fact, their simplistic positional embedding limits performance on 3D-VL tasks. Leveraging State Space Models (SSMs) as its core, a flux of mamba proposes a selection scanning mechanism, enabling it to handle long-range sequences and spatial modeling in near-linear complexity. However, the unmodified utilization of mamba does not yield satisfactory performance on 3D-VL tasks due to the following challenges: (1) 3D point clouds are sparse, unordered and encode rich spatial information. When converting 3D data into 1D format, such dimensional reduction causes mamba to struggle to capturing spatial relationships and preserving geometric structures of 3D scene due to inherent sequential dependencies induced by its recurrent-based processing. (2) Mamba's vanilla framework lacks native cross-modal interaction mechanisms necessary to seamlessly align semantics with 3D geometries.
+
+Towards the dilemma, we develop the first mamba-based framework Mamba-3VL for 3D-VL understanding. Departing from transformer-based works, Mamba-3VL with linear complexity explicitly models spatial relation of 3D objects and enhances intra- & inter-modality interactions. As illustrated in Fig. 2, Mamba-3VL achieves top-tier performance on 3D-VG benchmarks compared with other query-based decoding procedures, while maintaining its high efficiency.
+
+To capture spatial relationships of 3D object sequences while enhancing fine-grained interactions of 3D-VL interaction, we develop a Mamba Mixer module, which consists of a
+
+relation-prioritized spatial scanning and a channel twisting. The relation-prioritized spatial scanning is an effective 3D-to-1D reordering policy to better model spatial relations of 3D objects. It combines the Nearest Instance Scanning (NIS) and the Furthest Instance Scanning (FIS), where the former captures local spatial information and the latter strengthens long-range dependency understanding from a global perspective. A hybrid feature chain then combines 3D object queries and multi-modal features. The channel twisting enables inter-modal semantic fusion via channel-wise traversal of this feature chain, ensuring 3D objects contextualized awareness of task-specific features across spatial positions.
+
+Although the spatial scanning demonstrates ability for spatial relationships modeling, the reliance on global positional embeddings to capture fine-grained instance-level features remains constrained. In fact, instance-specific positional embeddings are indispensable in 3D-VL tasks as they offer accurate spatial context and geometric attributes (e.g., color and shape). Motivated by this, we propose an Instance-aware Dynamic Position Adapter (IDPA) with intercalated EdgeConv [56-58] and Language-modulated Instance Adapter (LISA) layers. The essence of IDPA lies in its capacity to deliver fine-grained, instance-specific positional embeddings, which further enhance Mamba Mixer's spatial relationship modeling. EdgeConv enhances spatial relationships by integrating neighborhood features and embedding geometric priors of 3D objects as positional embedding. Further, LISA dynamically modulates instance-specific positional embeddings via a geometric-semantic warping mechanism, to align with contexts provided by promptable features.
+
+We evaluate Mamba-3VL on seven established 3D-VL datasets: ScanNet200 [51] for instance segmentation, ScanRefer [6], ReferIt3D [1], Multi3DRef [67] for visual grounding, ScanQA [2] and SQA3D [42] for question answering, and Scan2Cap [11] for dense captioning. Mamba-3VL surpasses state-of-the-arts by a clear margin. Remarkably, only fine-tuning  $1\%$  LLM parameters, it exhibits impressive generalization to Embodied AI tasks, e.g., robotic manipulation.
+
+- We pioneer Mamba-3VL, marking the first exploration of mamba in 3D-VL tasks, achieving top performance, efficient linear complexity and generalization potential.  
+- To calibrate mamba with 3D-VL tasks, a Mamba Mixer models spatial relationship and enhance its 3D-VL interaction abilities. IDPA further dynamically adjust instance-aware positional embeddings to improve spatial reasoning.  
+- Experiments validate Mamba-3VL achieves SOTA results on all canonical benchmarks, marking mamba as a promising foundational architecture for future 3D-VL research.
+
+# 2. Related Works
+
+# 2.1. 3D Vision-language (3D-VL) Learning
+
+Recently, the AI community has witnessed rising interests in 3D-VL learning, driven by its potential to bridge physical
+
+![](images/15f077204a541a4cd9b638e99eae5e8039a964d546ff9331b25ea77cc03da314.jpg)  
+Figure 3. Preliminaries on pipeline of the proposed Mamba-3VL. (a) Our Mamba-3VL is a query-based decoding framework composed of stacked Mamba Mixer and IDPA layers. (b) Mamba Mixer with the interleaved (d) channel twisting mechanism and (e) relation-prioritized spatial scanning policy. (c) IDPA dynamically adjusts instance-aware positional embeddings via a geometric-semantic warping.
+
+world with natural language—a cornerstone for advancing spatial intelligence. During this carnival, a bunch of 3D-VL tasks, e.g., visual grounding [22, 24, 59–61, 69], dense captioning [4, 9, 11, 27], question answering [2, 42], situated reasoning [37] are proposed to evaluate capabilities to interpret human instructions in relation to object properties and spatial relationships. Embodied navigation [43, 50, 70, 71] and robotic manipulation [5, 29, 53, 62] tasks further probe the embodied perception and interacting capacity in physical environments. Despite this burst, initial efforts focus on developing task-specific models, restricting flexibility towards universal user-assistant interactions. Recent researches have pivoted towards building unified models capable of managing multiple 3D-VL tasks. [4, 7, 12] capitalize on multi-task synergies. Methods like [25, 28, 73, 74] develop pre-trained transformers and unified representations to facilitate cross-modal alignment. However, their reliance on full attention struggle to capture long-range 3D-VL dependencies. Departing from transformer-centric paradigm, our Mamba-3VL marks the first mamba-based foundation model with top-tier performance and high efficiency for 3D-VL reasoning.
+
+# 2.2. State Space Models and Visual Applications
+
+State Space Models (SSMs) [15, 17-19] excel at capturing complex correlations in the language sequence. Mamba, as a prominent SSM, has been rapidly transitioned to multiple vision tasks [36, 41, 68, 72]. Vim [72] presents the first pure SSM-based model that efficiently compress the vision representation for intensive prediction tasks. VideoMamba [35] introduces self-distillation into the non-hierarchical Vim to address the temporal redundancy in video understanding. Recent studies [32, 36, 65, 66] investigate the applicability of mamba on 3D tasks by employing distinct point cloud order
+
+ing policy. Despite impressive success achieved, mamba in 3D-VL tasks remains under-explored. We propose Mamba-3VL with designs like relation-prioritized scanning, which paves the road to spearhead new avenues in 3D-VL research.
+
+# 3. Methods
+
+# 3.1. Overall Framework
+
+In Fig. 3 (a), we formulate multiple 3D-VL tasks in a generalized query-based decoding procedure following [74-77]. We extract point cloud, multi-scale voxel, and multi-view image features with a point encoder, a voxel encoder, and an image encoder. We first group the dense 3D points into  $S$  segments. For the point cloud, we use the pre-trained PointNet++ [47] to obtain point features  $\mathbf{P} = \{\mathbf{p}_0,\mathbf{p}_1,\dots,\mathbf{p}_{\mathrm{S}}\}$  of the segments. For the multi-view images, the pre-trained OpenSeg [16] model is utilized to compute per-pixel embeddings, which are back-projected to 3D space and aggregated into segment-level image features  $\mathbf{I} = \{\mathbf{i}_0,\mathbf{i}_1,\dots,\mathbf{i}_{\mathrm{S}}\}$ . For the voxel, we discretize the 3D scene with Mask3D [52] and leverage a sparse convolutional U-Net to extract voxel representations  $\mathbf{V} = \{\mathbf{v}_0,\mathbf{v}_1,\dots,\mathbf{v}_{\mathrm{S}}\}$  of the pre-generated segments. Similar to PQ3D, these three segment-level features are aligned within a shared 3D coordinate space, yielding the final 3D scene representation, denoted as  $\mathbf{X} = \{\mathbf{V},\mathbf{I},\mathbf{P}\}$ . Further, a mamba-based query decoder is introduced with the multi-modal Mamba-Mixer module (Sec. 3.2) and Instance-aware Dynamic Position Adapter (Sec. 3.3). Forming the foundation of Mamba-3VL, it couples task-specific prompts (e.g., texts and bounding boxes) with the 3D scene features to enhance the cross-modal interaction while preserving spatial relationships among 3D objects. Each query is ultimately routed through three universal output heads to predict the instance mask, task-related score, and scene-grounded texts.
+
+![](images/28b51ad8d8db17b90b0683d42b40edbb17efc0ec2e3062d36a72a14adf4dc278.jpg)  
+A toilet bowl under tissue holder
+
+![](images/56a919199830a7e4975aedbd9eccb77ec355c1dc1b223e6a9d6d181608dcbb58.jpg)  
+Brown couch faces coffee table
+
+![](images/f170f5a37a808489e9d22a9f2b2f755679b6b5d82e07320583f925ca0564c693.jpg)
+
+![](images/57cdc1341dd16ed1819517878ea3ae231e26bde410f3c1d3d869817496ceb17a.jpg)  
+A black sofa behind the fan.
+
+![](images/dd76e893829af9ad6d59604d5e29165621bf4f8537a15ab395a594a139685a01.jpg)  
+a circular end table can be found next to a teal couch and an arm chair.
+
+![](images/69b198c11426c8dc9253c25b52ce037722428839bc6a4a7d8a509e1673a54cb3.jpg)
+
+![](images/6be4d79d4916f45fc21923fd6dc7325a36a81d50168f65310e4aaff7ef51df16.jpg)  
+a dark monitor sits on a wood desk against the wall, its screen flickering with activity.  
+an office chair, streamlined and professional in appearance, is sitting around a large table.
+
+![](images/2847b06e30e0d64a4b601a5f8d23711c999fed65fbf81c67b90ba59bc8f4de9f.jpg)
+
+![](images/3fd37afc964b8e6ede64eb4f018010648861b0bf5d1b995e95163cb50ccde186.jpg)  
+Figure 4. Visualized examples for promptable segmentation tasks.
+
+# 3.2. Multi-Modal Mamba Mixer Block
+
+To better adapt mamba to 3D-VL tasks, we introduce Mamba Mixer, which interprets spatial relationships of 3D objects and achieves holistic inter-modality and intra-modality interactions. In Fig. 3 (b), Mamba Mixer consists of relation-prioritized spatial scanning (e) and channel twisting (d).
+
+Following PQ3D [74], a zero-initialized query  $\mathbf{Q}_0$  centered on 3D object centroids, is inputted into the mamba-based decoder, which guides instance queries to incorporate scene representations  $\mathbf{X}$  and promptable features  $\mathbf{T}$ . In each decoder layer  $l$ , instance queries  $\mathbf{Q}_l$  sequentially retrieve scene-relevant features and prompt-encoded knowledge with two successive Mamba Mixer modules, severally. Consider the prompt-based Mamba Mixer as an exemplar:
+
+(1) Intra-modality interactions. Before feeding instance queries  $\mathbf{Q}_l \in \mathcal{R}^{N \times d}$  into the mamba block, a critical step involves reordering them into 1D sequences. While previous endeavors [36, 65, 66] employ canonical mamba for 3D representations, they fail to capture intrinsic spatial relationship among 3D objects in irregular 3D point clouds. To address this limitation, we design a relation-prioritized spatial scanning policy, which integrates the Nearest Instance Scanning (NIS) and Furthest Instance Scanning (FIS) to ensure the spatial dependencies of instance query sequence. In detail, we sort  $\mathbf{Q}_l$  based on spatial proximity into two sequences: farthest and nearest neighbor orders. For FIS, we randomly select an initial target instance, then iteratively choose the one with the furthest spatial proximity from the selected set and vice versa. Before entering the SSM layer, each branch undergoes a series of normalization, linear projection, and 1D convolution operations. In theory, NIS more effectively captures spatial relationships among local objects corresponding to semantic concepts, e.g., "adjacent" and "contact". In contrast, FIS strengthens the understanding of long-range dependency, e.g., instantiated by "the farthest".
+
+(2) Inter-modality interactions. The spatial scanning exhibits channel independence, failing to capture multi-modal
+
+interactions. To establish the correspondence between 3D vision and task prompts, we first construct a hybrid feature chain by channel-wisely concatenating 3D instance queries and prompt embeddings, bridging 3D representations with task-specific contexts. The channel twisting enables intermodality fusion through channel-wise scanning of hybrid feature chains, thereby injecting task conditions into 3D instances. We organize channels as an ordered sequence, and presents selective scanning that allows mamba to be "aware" of the task prompts at every spatial location and dynamically adapt spatial reasoning based on task-related conditions.
+
+Formally, the total process of Mamba Mixer is written as:
+
+$$
+\mathbf {Q} _ {l + 1} = \operatorname {S S M} _ {\text {c h a n n e l}} \left[ \operatorname {S S M} _ {\text {s p a t i a l}} \left(\mathbf {Q} _ {l}\right), \mathbf {T} \right], \tag {1}
+$$
+
+$\mathrm{SSM}_{\mathrm{spatial}}$  models relation-prioritized spatial dependencies in 3D instance queries, while  $\mathrm{SSM}_{\mathrm{channel}}$  treats hybrid feature chain as an ordered sequence along the channel axis.
+
+# 3.3. Instance-aware Dynamic Position Adapter
+
+While Mamba Mixer's relation-prioritized reordering policy does model spatial relationship of 3D objects, it is, however, still relies on global positional embeddings. They exhibit limited dynamic adaptability to instance-wise object features, essential for 3D-VL tasks. Inspired by this, we introduce an Instance-aware Dynamic Position Adapter (IDPA) to provide fine-grained, instance-specific positional embeddings for Mamba Mixer with enhanced spatial relation modeling.
+
+As depicted in Fig. 3 (c), we derive local object proxies for 3D objects using KNN algorithm based on their spatial positions:  $\mathcal{Q}_i = \mathrm{KNN}(\mathbf{Q},\mathbf{q}_i)$  ( $i\in [1,|\mathbf{Q}|]$ ).  $\mathcal{Q}_i,|\mathbf{Q}|$  denote object proxy of  $\mathbf{q}_i$  and total number of query instances. Each  $\mathcal{Q}_i$  includes  $K$  closest object queries  $\{\mathbf{q}_{i_j}|j\in [1,K]\}$  of  $\mathbf{q}_i$ . This local proxy effectively captures geometric structure and spatial layout of objects in 3D scene. The predefined spatial geometric priors are then formulated as:
+
+$$
+\mathbf {q} _ {i _ {j}} ^ {\prime} = \operatorname {c o n c a t} \left(\mathbf {q} _ {i} - \mathbf {q} _ {i _ {j}}, \mathbf {q} _ {i}, \| \mathbf {q} _ {i} - \mathbf {q} _ {i _ {j}} \|\right), j \in [ 1, K ] \tag {2}
+$$
+
+$\mathbf{q}_i^{\prime}$  embeds relative orientation and distance of 3D objects.
+
+Further, the low-level geometric priors are embedded as positional embedding  $h_{i_j}$  with an EdgeConv layer  $\mathcal{E}[\cdot]$ :
+
+$$
+\mathbf {h} _ {i _ {j}} = \mathbf {q} _ {i _ {j}} ^ {\prime} + \mathcal {E} [ \mathbf {q} _ {i _ {j}} ^ {\prime} ], \forall j \in \mathcal {Q} _ {i}. \tag {3}
+$$
+
+In each layer of IDPA, we regenerate the object proxy using nearest neighbors. In this manner, EdgeConv dynamically captures local geometric relationships among 3D objects and their high-level dependencies in the feature space.
+
+Beyond refining spatial relationships, our LISA serves to further modulate instance-specific positional embeddings via a geometric-semantic warping mechanism. Specifically, it adjusts spatial features of each 3D object instance to align with contexts provided by promptable features  $\mathbf{T}$ :
+
+$$
+\mathbf {r} _ {i} = \left\{\mathbf {r} _ {i _ {j}} \mid j \in [ 1, K ] \right\} = \alpha (\mathbf {T}) \odot \frac {\left\{\mathbf {h} _ {i _ {j}} \right\} - \mathbf {h} _ {i}}{\Omega + \varepsilon} + \beta (\mathbf {T}) \tag {4}
+$$
+
+<table><tr><td rowspan="2">Method</td><td colspan="3">ScanRefer</td><td colspan="4">Nr3D</td><td colspan="4">Sr3D</td><td colspan="4">Multi3DRefer</td><td></td><td></td></tr><tr><td>Unique</td><td>Multiple</td><td>Average</td><td>Easy</td><td>Hard</td><td>VD</td><td>VID</td><td>Average</td><td>Easy</td><td>Hard</td><td>VD</td><td>VID</td><td>Average</td><td>ZT</td><td>ST</td><td>MT</td><td>Average</td></tr><tr><td>3DVG [69]</td><td>60.6</td><td>28.4</td><td>34.7</td><td>48.5</td><td>34.8</td><td>34.8</td><td>43.7</td><td>40.8</td><td>54.2</td><td>44.9</td><td>44.6</td><td>51.7</td><td>51.4</td><td>45.8</td><td>16.7</td><td>26.5</td><td>25.5</td></tr><tr><td>M3DRef-CLIP [67]</td><td>77.2</td><td>36.8</td><td>44.7</td><td>55.6</td><td>43.4</td><td>—</td><td>—</td><td>49.4</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>39.4</td><td>30.6</td><td>37.9</td><td>38.4</td></tr><tr><td>BUTD-DETR [24]</td><td>65.0</td><td>34.0</td><td>38.6</td><td>60.7</td><td>48.4</td><td>46.0</td><td>58.0</td><td>54.6</td><td>68.6</td><td>63.2</td><td>53.0</td><td>67.6</td><td>67.0</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>EDA [61]</td><td>68.6</td><td>37.6</td><td>42.3</td><td>58.2</td><td>46.1</td><td>50.2</td><td>53.1</td><td>52.1</td><td>70.3</td><td>62.9</td><td>54.1</td><td>68.7</td><td>68.1</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>G3-LQ [59]</td><td>72.7</td><td>40.8</td><td>45.6</td><td>62.0</td><td>50.7</td><td>53.8</td><td>57.1</td><td>57.0</td><td>74.7</td><td>66.3</td><td>57.2</td><td>74.0</td><td>73.1</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>LIBA [60]</td><td>74.3</td><td>44.1</td><td>49.0</td><td>72.4</td><td>57.2</td><td>60.3</td><td>66.6</td><td>64.5</td><td>78.9</td><td>70.2</td><td>61.7</td><td>77.3</td><td>75.8</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>ViL3DRel [8]</td><td>68.6</td><td>30.7</td><td>37.7</td><td>70.2</td><td>57.4</td><td>62.0</td><td>64.5</td><td>64.4</td><td>74.9</td><td>67.9</td><td>63.8</td><td>73.2</td><td>72.8</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>3DJCG [4]</td><td>64.3</td><td>30.8</td><td>37.3</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>66.9</td><td>16.7</td><td>26.2</td><td>26.6</td></tr><tr><td>UniT3D [12]</td><td>73.1</td><td>31.1</td><td>39.1</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>3D-VisTA [73]</td><td>75.1</td><td>39.1</td><td>45.8</td><td>72.1</td><td>56.7</td><td>61.5</td><td>65.1</td><td>64.2</td><td>78.8</td><td>71.3</td><td>58.9</td><td>77.3</td><td>76.4</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>PQ3D [74]</td><td>78.2</td><td>46.2</td><td>51.2</td><td>75.0</td><td>58.7</td><td>62.8</td><td>68.6</td><td>66.7</td><td>82.7</td><td>72.8</td><td>62.9</td><td>80.5</td><td>79.7</td><td>57.7</td><td>43.6</td><td>40.9</td><td>50.1</td></tr><tr><td>Mamba-3VL (Ours)</td><td>79.9</td><td>48.9</td><td>53.5</td><td>77.8</td><td>62.9</td><td>66.7</td><td>71.5</td><td>69.7</td><td>84.4</td><td>75.8</td><td>69.1</td><td>81.8</td><td>81.7</td><td>69.5</td><td>45.7</td><td>43.5</td><td>53.2</td></tr></table>
+
+Table 1. Grounding accuracy (%) on 3D-VG benchmarks. Results on ScanRefer and Multi3DRefer are reported under IoU@0.5. The ZT and ST results from Multi3DRefer are with distractors of the same class. "VD" and "VID" means "View-Dependent" and "View-Independent".  
+
+<table><tr><td>Model</td><td>AP</td><td>\( AP_{50} \)</td><td>\( AP_{25} \)</td><td>head</td><td>common</td><td>tail</td></tr><tr><td colspan="7">Closed-Vocabulary</td></tr><tr><td>Mask3D [52]</td><td>26.9</td><td>36.2</td><td>41.4</td><td>39.8</td><td>21.7</td><td>17.9</td></tr><tr><td>PQ3D (w/cls) [74]</td><td>27.0</td><td>38.9</td><td>46.3</td><td>35.8</td><td>24.2</td><td>20.0</td></tr><tr><td>Mamba-3VL (w/cls)</td><td>27.9</td><td>39.6</td><td>48.2</td><td>37.5</td><td>26.4</td><td>23.3</td></tr><tr><td colspan="7">Open-Vocabulary</td></tr><tr><td>OpenScene [46]</td><td>11.7</td><td>15.2</td><td>17.8</td><td>13.4</td><td>11.6</td><td>9.9</td></tr><tr><td>SAM3D [63]</td><td>6.1</td><td>14.2</td><td>21.3</td><td>7.0</td><td>6.2</td><td>4.6</td></tr><tr><td>OVIR-3D [40]</td><td>13.0</td><td>24.9</td><td>32.3</td><td>14.4</td><td>12.7</td><td>11.7</td></tr><tr><td>OpenMask3D [54]</td><td>15.4</td><td>19.9</td><td>23.1</td><td>17.1</td><td>14.1</td><td>14.9</td></tr><tr><td>OpenIns3D [23]</td><td>8.8</td><td>10.3</td><td>14.4</td><td>16.0</td><td>6.5</td><td>4.2</td></tr><tr><td>PQ3D [74]</td><td>20.2</td><td>28.0</td><td>32.5</td><td>30.9</td><td>17.0</td><td>11.3</td></tr><tr><td>OV-3DIS [44]</td><td>23.7</td><td>29.4</td><td>32.8</td><td>27.8</td><td>21.2</td><td>21.8</td></tr><tr><td>Mamba-3VL (Ours)</td><td>22.4</td><td>30.5</td><td>34.7</td><td>33.6</td><td>20.4</td><td>13.8</td></tr></table>
+
+Table 2. Instance segmentation results on ScanNet200 [51], with w/cls indicating the closed-vocabulary classification head.  
+
+<table><tr><td rowspan="2">Method</td><td colspan="7">Test Dataset</td></tr><tr><td>What</td><td>Is</td><td>How</td><td>Can</td><td>Which</td><td>Other</td><td>Overall</td></tr><tr><td>ClipBERT [30]</td><td>30.2</td><td>60.1</td><td>38.7</td><td>63.3</td><td>42.5</td><td>42.7</td><td>43.3</td></tr><tr><td>SQA3D [42]</td><td>31.6</td><td>63.8</td><td>46.0</td><td>69.5</td><td>43.9</td><td>45.3</td><td>46.6</td></tr><tr><td>3D-VisTA [73]</td><td>34.8</td><td>63.3</td><td>45.4</td><td>69.8</td><td>47.2</td><td>48.1</td><td>48.5</td></tr><tr><td>PQ3D [74]</td><td>37.1</td><td>61.3</td><td>44.5</td><td>60.9</td><td>47.0</td><td>45.1</td><td>47.1</td></tr><tr><td>Mamba-3VL</td><td>44.5</td><td>65.7</td><td>48.3</td><td>68.4</td><td>48.9</td><td>52.8</td><td>52.4</td></tr></table>
+
+$\varepsilon$  is a small constant for numerical stability.  $\alpha (\mathbf{T})$  and  $\beta (\mathbf{T})$  are learnable factors modulated by promptable features  $\mathbf{T}$  (e.g., texts).  $\Omega$  is standard deviation across all object proxies.
+
+Further, instance features  $\mathbf{x}_i$  are semantically "warped" according to their prompt-modulated spatial features  $\mathbf{r}_i$ :
+
+$$
+\mathbf {x} _ {i} ^ {\prime} = \operatorname {W a r p i n g} \left(\mathbf {r} _ {i}, \mathbf {x} _ {i}\right) = \operatorname {L i n e a r} \left(\operatorname {c o n c a t} \left(\mathbf {r} _ {i}, \mathbf {x} _ {i}\right)\right), \tag {5}
+$$
+
+The instance-specific positional embeddings  $\mathbf{x}_i^{\prime}$  encodes rich object-centric attributes. Finally,  $\mathbf{x}_i^{\prime}$  is updated by aggregating the neighboring queries with softmax-based weights:
+
+$$
+\bar {\mathbf {x}} _ {i} = \sum \operatorname {s o f t m a x} \left(\mathbf {x} _ {i j} ^ {\prime}\right) \cdot \mathbf {x} _ {i j} ^ {\prime} \in \mathcal {R} ^ {d}, \forall j \in [ 1, K ], \tag {6}
+$$
+
+Table 3. Answer accuracy on the SQA3D [42] by question types.  
+
+<table><tr><td>Method</td><td>C@25</td><td>C@50</td><td>B-4@50</td><td>M@50</td><td>R@50</td><td>Avg.</td></tr><tr><td>Scan2Cap [11]</td><td>53.7</td><td>35.2</td><td>22.4</td><td>21.4</td><td>43.6</td><td>30.7</td></tr><tr><td>3DJCG [4]</td><td>60.8</td><td>47.7</td><td>31.5</td><td>24.3</td><td>51.1</td><td>38.7</td></tr><tr><td>3D-VisTA [73]</td><td>71.0</td><td>66.9</td><td>34.0</td><td>27.1</td><td>54.3</td><td>45.6</td></tr><tr><td>X-Trans2Cap [64]</td><td>61.8</td><td>43.8</td><td>25.1</td><td>22.5</td><td>45.3</td><td>34.2</td></tr><tr><td>MORE [26]</td><td>58.9</td><td>38.9</td><td>23.0</td><td>21.7</td><td>44.3</td><td>32.0</td></tr><tr><td>PQ3D [74]</td><td>87.1</td><td>80.3</td><td>36.0</td><td>29.1</td><td>57.9</td><td>50.8</td></tr><tr><td>3D-VLP [28]</td><td>64.1</td><td>50.0</td><td>31.8</td><td>24.5</td><td>51.2</td><td>39.4</td></tr><tr><td>Mamba-3VL (Ours)</td><td>91.2</td><td>84.0</td><td>39.5</td><td>30.8</td><td>59.8</td><td>53.5</td></tr></table>
+
+Table 4. Results of 3D dense captioning on Scan2Cap [11] under IoU@50, evaluated by text similarity scores.
+
+The output  $\bar{\mathbf{x}}_i$  of IDPA is incorporated into the mamba-based query decoder branch via a residual connection. IDPA supplements instance-specific positional embeddings to Mamba Mixer for precise spatial relationship modeling of objects.
+
+# 3.4. Output Heads and Losses
+
+As shown in Fig. 3, our Mamba-3VL utilizes three output heads to support 3D-VL tasks. We compute the cross entropy loss  $\mathcal{L}_{\mathrm{grd}}$  and  $\mathcal{L}_{\mathrm{gen}}$  for both grounding and generation heads, while the dice loss  $\mathcal{L}_{\mathrm{mask}}$  is applied to the mask head. The model is optimized through multi-task learning with combined losses. Further details are provided in Appendix C.1.
+
+# 4. Experimental Results
+
+# 4.1. Implementation Details
+
+We evaluate the Mamba-3VL on multiple 3D-VL datasets, i.e., ScanNet200 [51], Multi3DRefer [67], ScanRefer [6], Sr3D/Nr3D [1], SQA3D [42], ScanQA [2], Scan2Cap [11]. We utilize AdamW [38] optimizer with a base learning rate of 1e-4 and a warm-up cosine scheduler. Our method implements two-stage optimization: (1) 800-epoch ScanNet200-based initialization through instance segmentation training with a classification head. (2) 80-epoch full-task training on all benchmark datasets with promptable queries. The hidden dimension and query decoder layer is set to  $d = 768$ ,  $L = 4$ .
+
+![](images/caad829e6cb1e0532a107f838ec95d16a5e1dd09fe7fe8c24ba38b23b8e34099.jpg)  
+you are looking for the chair closest to the front. it is on the right side, right behind the chair leg.
+
+![](images/8cdb664c6782826f007a2f360eff805300e25bd81b5312794edb41667e98450c.jpg)  
+The bookshelf you want to pick is on the end of middle bookshelves, next to the shorter strip of orange carpet.
+
+![](images/8d5f6c17942754c1377a975f276472dfa9907f1f983f32328ea29cbd8e45e4cb.jpg)  
+the floral printed chair is in front of glass double doors. the floral printed chair is to the left of grey desk.
+
+![](images/a85ba4a21789b09b3f9b2ba264e731076211ba80840c07ff265536f6ac5bfb3b.jpg)  
+this desk is longer and located at the foot of the bed. It is located on the opposite wall from the door.
+
+![](images/e790c9d765916202ba290ee23a97577161f2d82e0580ef43f0b386203cdeedad.jpg)  
+(a) Visual Grounding (ScanRefer, Nr3D, and Multi3DRefer)  
+it is a dark colored two-sealer futon located by the door. it is located underneath a whiteboard.
+
+![](images/ebd8165e08ba42145a3c22a9fa051fbf7360f02be2f5b44296f936c124d82d44.jpg)  
+the chair is part of a row of chairs and is surrounded by one on its left and one on its right.
+
+![](images/acbecf840f3a14a8f3d0f4b4822aa345ec67ee365a3161fdf1a511cb6b76ac31.jpg)  
+the towel is hanging on the bar of shower door, and it is the left most towel. The one closest to the door.
+
+![](images/d4aa38e578299918561bd94488cc45fc3d32417a21f2544af0e7c0bd0c224381.jpg)  
+a library boasts a pristine white window. The windows located on the wall above the bookshelf.
+
+![](images/6e5f947da6c056b12a81af8ecc960fd92ec0358ea9f9f7ce0bd166d9725baade.jpg)  
+the chair the closes to the bed in the northeast corner from entrance the chair has a curved backside.
+
+![](images/d4f31e6fcc0b948b3af07b8b39f9940c9ba3e7898b9b3a76b48bc502b8c84739.jpg)  
+the back of the room holds a chair, with another chair in front of this one.
+
+![](images/eb31e8ebefda8cd91d876933bcaea512c2f6b5924e650cdd4b1a2f7cf84c2dca.jpg)  
+the window is the leftmost placed on the side of the wall. The window is rectangular as a clear middle area.
+
+![](images/a843e691a44d2dcdc6f331d5245d77cb2542c34f760992c9e0eeed1cadd26d25.jpg)  
+a blue armchair is perpendicular to blue-green couch on right, opposite the rectangular white coffee table.
+
+![](images/c0a485ff36e6292e243efafbb21108dd416701791bd10feeed8095379ad75e65.jpg)  
+Where is the red pillow located? GT on bed  
+Ours: on top of the bed
+
+![](images/a1497fd3d0ffacdb45afaa93f05f744e3a3efb0ec5a3f7e19568951eb8ef13f2.jpg)  
+What color is trash can near printer? GT: black trash can  
+Ours: black
+
+![](images/1b786349fd1ba060d4248649c777267609179d23df74bf3c18a1fd3d875c8e6a.jpg)  
+(b) Question Answering (ScanQA and SQA)  
+How many chairs surround the table? GT: two chairs.  
+Ours: two
+
+![](images/50d845b54b17ac8c10195fd17e59d1a6b20f3ecebca7174105328d84cc39b253.jpg)  
+red backpack is on right side, an d I am facing wall. Are all the chairs in the room the same color?
+
+![](images/12844b9aa0b6644a2817255ade68c69f5d69ccf459917becf9bd9b7bd999c31c.jpg)  
+I am facing the table. There is no more chair on my left. Is the amount of oven I am facing odd or even?
+
+![](images/f2edd2b98d6a8ce15a671a7f8d8373b4f42f595d41541752c1061ef802875d59.jpg)  
+I am facing a whiteboard and there are thrash cans on my right. Which wall has a clock hanging on it?
+
+![](images/ed3cc3e89fff24dfcdf0913d2a18bc4c581d90b5a92dbe30e0f221e625189a7a.jpg)  
+this is a long white tablethe right of another table . itin the middle of the room  
+Figure 5. Visualizations on several 3D-VL tasks. (a) Visual grounding on ScanRefer [6] and Multi3DRefer [67]. (b) Question answering on ScanQA [2] and SQA [42]. (c) Dense captioning on Scan2Cap [11]. Comparisons with PQ3D shows our Mamba-3VL's superior ability.
+
+![](images/275862b23894af54fe127a1c664e038bdb1ce5b90ea2bd4809fc0c37115f57e3.jpg)  
+the table is in center of the room. the table is a brown rectangle and surrounded by chairs
+
+![](images/64f4ad75da2830ba0870b34aad5118883c4d51eb4cadf8677a71274c4b981d49.jpg)  
+(c) Dense Captioning (Scan2Cap)  
+It is a white shower curtain on top of the bathtub. it is to the right of toilet when you walk in.
+
+![](images/9eefed52115d72427a9369b28a8fb0865c2af8f1be3229f182be157402af8193.jpg)  
+this is a desk covered with itemsIt is a light brown rectangle. It isin front of the bed .
+
+![](images/0ddcce705a9de502a866a4484a51bb5c6756bc04d17b97b26341c24a019ebd9f.jpg)  
+this is a bookshelf. it is rectangular with many items. It is to the left of the blue chair.
+
+![](images/2781b2f149fcf487580bcb463a364d8b2f9ce8ee3259543f680004bdad634508.jpg)  
+this is a bed with a white matted . It is sitting on the left ofanother bed
+
+<table><tr><td>Method</td><td>EM@1</td><td>EM@10</td><td>BLEU-1</td><td>BLEU-2</td><td>BLEU-4</td><td>ROUGE</td><td>METEOR</td><td>CIDEr</td><td>SPICE</td></tr><tr><td>ScanQA [2]</td><td>23.5/20.9</td><td>56.5/54.1</td><td>31.6/30.7</td><td>21.4/21.2</td><td>12.0/10.8</td><td>34.3/31.1</td><td>13.6/12.6</td><td>67.3/60.2</td><td>12.0/11.3</td></tr><tr><td>3D-VisTA [73]</td><td>27.0/23.0</td><td>57.9/53.5</td><td>34.4/30.2</td><td>—</td><td>—</td><td>38.6/32.8</td><td>15.2/12.9</td><td>76.6/62.6</td><td>—</td></tr><tr><td>Image+MCAN [42]</td><td>22.3/20.8</td><td>53.1/51.2</td><td>26.7/26.3</td><td>18.5/17.9</td><td>14.3/9.7</td><td>31.3/29.2</td><td>12.1/11.5</td><td>60.4/55.6</td><td>9.1/8.9</td></tr><tr><td>VoteNet+MCAN [42]</td><td>19.7/18.1</td><td>50.7/48.6</td><td>29.5/29.6</td><td>17.2/17.8</td><td>6.1/7.1</td><td>30.9/29.1</td><td>12.1/11.7</td><td>58.2/53.3</td><td>10.4/10.3</td></tr><tr><td>ScanRefer+MCAN [42]</td><td>20.6/19.0</td><td>52.4/49.7</td><td>27.9/27.0</td><td>17.3/16.2</td><td>7.5/7.8</td><td>30.7/28.6</td><td>12.0/11.4</td><td>57.4/53.4</td><td>10.6/10.6</td></tr><tr><td>PQ3D [74]</td><td>26.1/20.0</td><td>—</td><td>43.0/36.1</td><td>—</td><td>—</td><td>42.9/34.0</td><td>17.8/13.9</td><td>87.8/65.2</td><td>—</td></tr><tr><td>3D-VLP [28]</td><td>24.6/21.6</td><td>55.9/53.9</td><td>33.2/31.5</td><td>22.7/23.6</td><td>11.2/15.8</td><td>36.0/31.8</td><td>14.2/13.1</td><td>70.2/63.4</td><td>12.7/12.5</td></tr><tr><td>Mamba-3VL (Ours)</td><td>27.6/23.4</td><td>58.2/54.1</td><td>45.8/38.4</td><td>24.7/26.4</td><td>14.8/16.2</td><td>44.6/36.7</td><td>19.2/15.8</td><td>89.3/67.9</td><td>13.8/13.7</td></tr></table>
+
+Table 5. Answer accuracy on the ScanQA [2] benchmark, with separate evaluations for "test w/ object" and "test w/o object". Metrics include top-1/top-10 exact match (EM@1, EM@10) and text similarity scores (BLEU, METEOR, CIDEr, SPICE) for predicted answers.
+
+The neighbor size of object proxy is configured to  $K = 10$ . For embodied AI tasks, we replace the T5-small [49] model of generation head with Vicuna-7B [13] using the instruction-following dataset [21]. See Appendix C.3 for more details.
+
+# 4.2. Results on 3D Vision-Language Tasks
+
+3D Visual Grounding. As outlined in Tab. 1, our Mamba-3VL dominates 3D visual grounding benchmarks, surpassing state-of-the-arts by significant margins. The model achieves landmark accuracies of  $79.9\%$  (Unique) and  $48.9\%$  (Multiple) on the ScanRefer [6], outperforming PQ3D [74] by  $1.7\%$  and  $2.7\%$ , respectively. The Mamba Mixer's unique relation-prioritized spatial scanning policy effectively models spatial dependencies, which excels in handling cluttered 3D
+
+scenes. Our method exhibits view-invariant robustness with  $3.9\%$  and  $6.2\%$  improvements over PQ3D on VD subsets of  $\mathrm{Nr3D / Sr3D}$  benchmarks. On Multi3DRefer [67], Mamba-3VL attains  $69.5\%$ ,  $45.7\%$ , and  $43.5\%$  scores, showing a substantial improvement in different object referencing levels. To explain, IDPA provides instance-specific positional embeddings and refines spatial relationships among different object instances, showing superiority in multi-object scenarios. Visual comparisons in Fig. 5 (a) intuitively confirms our remarkable results across visual grounding benchmarks. In the challenging MT referencing task of Multi3DRefer [67], we conclude Mamba-3VL's exceptional ability to align texts with 3D referrals in complex scenes, even amidst distractors.
+
+Instance Segmentation on ScanNet200. As evidenced
+
+<table><tr><td rowspan="3">Methods</td><td colspan="2">separating</td><td colspan="2">pack-google</td><td colspan="2">put-blocks</td></tr><tr><td colspan="2">-piles</td><td colspan="2">-objects-seq</td><td colspan="2">-in-bowls</td></tr><tr><td>seen</td><td>unseen</td><td>seen</td><td>unseen</td><td>seen</td><td>unseen</td></tr><tr><td>Transporter</td><td>48.4</td><td>52.3</td><td>46.3</td><td>37.3</td><td>64.7</td><td>18.7</td></tr><tr><td>CLIP-only</td><td>90.2</td><td>71.0</td><td>95.8</td><td>57.8</td><td>97.7</td><td>44.5</td></tr><tr><td>RN50-BERT</td><td>46.5</td><td>44.9</td><td>94.0</td><td>56.1</td><td>91.8</td><td>23.8</td></tr><tr><td>CLIPort</td><td>89.0</td><td>62.8</td><td>84.4</td><td>70.3</td><td>100</td><td>45.8</td></tr><tr><td>LEO</td><td>98.8</td><td>75.2</td><td>76.6</td><td>79.8</td><td>86.2</td><td>35.2</td></tr><tr><td>Mamba-3VL</td><td>98.8</td><td>81.5</td><td>80.1</td><td>83.9</td><td>88.3</td><td>41.7</td></tr></table>
+
+Table 7. Results on the object navigation task in the Habitat-web [50] and CortexBench [43].
+
+<table><tr><td rowspan="2">Methods</td><td colspan="2">MP3D-val</td><td colspan="2">HD3M-val</td></tr><tr><td>Success</td><td>SPL</td><td>Success</td><td>SPL</td></tr><tr><td>Habitat (shortest)</td><td>4.4</td><td>2.2</td><td>—</td><td>—</td></tr><tr><td>Habitat (70k demo)</td><td>35.4</td><td>10.4</td><td>—</td><td>—</td></tr><tr><td>VC-1 (ViT-B)</td><td>—</td><td>—</td><td>57.1</td><td>31.4</td></tr><tr><td>LEO</td><td>23.1</td><td>15.2</td><td>23.1</td><td>19.1</td></tr><tr><td>PQ3D</td><td>—</td><td>—</td><td>80.0</td><td>50.0</td></tr><tr><td>Mamba-3VL</td><td>24.5</td><td>19.7</td><td>83.9</td><td>53.4</td></tr></table>
+
+Table 8. Comparison with other linear-complexity works on 3D-VL datasets.
+
+![](images/9d00f978d9c853f699b30b077ab90e270f9f3f229ffa9b1aee3fed334b745413.jpg)
+
+Table 6. Results on CLIPort robot manipulation.  
+
+<table><tr><td>ID</td><td>Spatial.</td><td>Channel.</td><td>IDPA</td><td>ScanRef.</td><td>Multi3D.</td><td>Scan2C.</td><td>SQA3D</td></tr><tr><td>(a)</td><td>✓</td><td></td><td>X</td><td>50.1</td><td>50.4</td><td>50.7</td><td>47.5</td></tr><tr><td>(b)</td><td>X</td><td></td><td>✓</td><td>49.2</td><td>49.7</td><td>50.1</td><td>47.0</td></tr><tr><td>(c)</td><td>X</td><td></td><td>X</td><td>49.5</td><td>50.1</td><td>50.5</td><td>47.2</td></tr><tr><td>(d)</td><td>✓</td><td></td><td>✓</td><td>52.4</td><td>52.0</td><td>52.6</td><td>51.0</td></tr><tr><td>(e)</td><td>✓</td><td></td><td>X</td><td>52.1</td><td>51.9</td><td>51.8</td><td>50.6</td></tr><tr><td>(f)</td><td>X</td><td></td><td>✓</td><td>51.8</td><td>51.6</td><td>51.1</td><td>50.1</td></tr><tr><td>(g)</td><td>✓</td><td></td><td>✓</td><td>53.5</td><td>53.2</td><td>53.5</td><td>52.4</td></tr></table>
+
+Table 9. Ablation study of proposed modules' effectiveness, with average performance evaluated under IoU@0.5.
+
+in Tab. 2, Mamba-3VL establishes new competitive benchmarks for 3D instance segmentation on ScanNet200 [51]. In open-vocabulary setting, we integrate CLIP [48] text embeddings in a promptable way and compute class logits from the grounding head after full-category prompting. Impressively, Mamba-3VL achieves comparable performance, recording an AP of  $22.4\%$ , showcasing its potential as a versatile interface for 3D open-vocabulary instance segmentation. When incorporating the classification head, Mamba-3VL mitigates the class imbalance issue inherent in the feature space of CLIP and outperforms PQ3D by  $3.3\%$  on tail classes. Fig. 4 vividly demonstrates that our Mamba-3VL accommodates a variety of prompts as inputs, e.g., texts or visual interactions to facilitate single-target and multiple-target referring segmentation. These findings also highlight the Mamba-3VL's flexibility in advancing interactive 3D scene understanding.
+
+3D Dense Captioning. As evidenced in Tab. 4, the results underscore Mamba-3VL's notable superiority on the 3D dense captioning task, securing a top position on the leaderboard. Mamba-3VL achieves Cider scores of 91.2 (IoU@25) and 84.0 (IoU@50), significantly outperforming PQ3D (87.1 and 80.3), which demonstrates its superior capacity to generate contextually accurate captions. Further, our method surpasses other leading approaches in both Bleu-4 and Meteor scores, reflecting improved fluency and better semantic alignment with contextual details of 3D scenes. Such performance gain is attributed to the channel twisting, which enriches the cross-modality interaction by preserving crucial spatial context and integrating task-relevant cues. In the last row of Fig. 5, the captions generated by our method are not only rich in detail and contextually appropriate but accurately convey colors (white table), geometric shape (rectangular), and spatial relations (on top of, the right of) of 3D objects.
+
+<table><tr><td rowspan="2">Spatial 
+Scanning</td><td rowspan="2">Channel 
+Scanning</td><td colspan="2">Nr3D</td><td colspan="2">Scan2Cap</td><td colspan="2">ScanQA (EM@1)</td></tr><tr><td>Easy</td><td>Hard</td><td>Cider</td><td>Bleu</td><td>w/ obj.</td><td>w/o. obj.</td></tr><tr><td>Random</td><td>Twisting</td><td>74.6</td><td>58.3</td><td>80.8</td><td>35.7</td><td>24.1</td><td>20.5</td></tr><tr><td>w/o. FIS</td><td>Twisting</td><td>75.3</td><td>60.5</td><td>82.6</td><td>36.9</td><td>26.4</td><td>21.9</td></tr><tr><td>w/o. NIS</td><td>Twisting</td><td>76.9</td><td>61.7</td><td>83.4</td><td>38.1</td><td>27.0</td><td>22.8</td></tr><tr><td>Relation</td><td>AdaIN</td><td>77.6</td><td>62.4</td><td>83.1</td><td>39.0</td><td>26.8</td><td>22.4</td></tr><tr><td>Relation</td><td>Attention</td><td>77.1</td><td>61.2</td><td>82.8</td><td>38.7</td><td>26.5</td><td>22.1</td></tr><tr><td>Relation</td><td>Twisting</td><td>77.8</td><td>62.9</td><td>84.0</td><td>39.5</td><td>27.6</td><td>23.4</td></tr></table>
+
+Table 10. Ablations of the selection of scanning mechanism.
+
+3D Question Answering. For the SQA3D [42], Mamba-3VL outperforms all existing state-of-the-arts across different challenging question types as illustrated in Tab. 3. Our Mamba-3VL excels with  $48.3\%$  accuracy on "How" and  $48.9\%$  on "Which" questions, demonstrating its profound capability in commonsense and multi-hop reasoning. To explain, the FIS mechanism enhances Mamba-3VL's ability to capture long-range dependencies and interpret multi-hop relationships. On ScanQA [2] benchmark in Tab. 5, Mamba-3VL also sets a new standard for performance, demonstrating improvements of  $2.8\% / 2.3\%$  in BLEU-1,  $1.7\% / 2.7\%$  in ROUGE, and  $1.5\% / 2.7\%$  in CIDEr on the "w/ object" and "w/o object" sub-sets. It validates Mamba-3VL's exceptional ability to generate semantically rich and contextually coherent responses for question answering. Mamba-3VL delivers distinguished EM@1/10 scores without conventional QA classification heads, attributed to the NIS policy and the IDPA module that model spatial relation via proximity-based spatial scanning and the instance-aware positional encoding.
+
+Task Planning and 3D Dialog. In this part, we fine-tune the pre-trained Mamba-3VL to complete dialogue and planning tasks on 3RPlan [21] and 3RDialog [21]. Qualitative examples in Fig. 6 indicate Mamba-3VL's abilities in generating well-structured task planning and scene-grounded dialogue. Mamba-3VL's response incorporates rich informative spatial relations and concrete 3D objects, performing admirably in multi-step reasoning and contextual dialogue.
+
+Embodied Navigation and Robotic Manipulation. We benchmark Mamba-3VL's generalizability of embodied acting and interacting in 3D physical world. We present results of CLIPort [53] manipulation and 3D object navigation in Tab. 6 and 7. Our findings are as follows: (1) For robotic manipulation, Mamba-3VL consistently outperforms com
+
+![](images/fa5b5e866f5402a394b11867550677462a3af29e83b0093587e3bae269a6e002.jpg)
+
+![](images/3e775418a3cdba20bef4f0ef50188d2427d6f0569e5cb9b5744aee59fc056c66.jpg)
+
+# Tidy up and arrange the kitchen.
+
+![](images/bbad80e68ca3052a66d19ab7f528d05d80d50c95fab1ba3f3c9ba031aec70d4c.jpg)
+
+![](images/0b0bc748fddad10f2f184e6453e5eadf4fcea9557335b32f4031348b5d084d33.jpg)
+
+# Arrange and adorn the living room.
+
+![](images/59d4377ee4f635eb5d28027f6e4a630e2debda8bf2f431ce94b75640ec0b4ef2.jpg)
+
+![](images/27b57c284f5857a0f4b96918fd9ef90f4b09763be48ed685fc854b9d38e23b27.jpg)
+
+![](images/3cc45c0f0af212101f665d374c9d587f6c8d578f9f293bb172404ae8a76b576e.jpg)
+
+# I want to add some decorations to my bedroom. What do you suggest?
+
+![](images/9f1ee6e1a01b266fb4f98053b7a638a167231516bc32e0a563fc9122db963858.jpg)
+
+How about adding some artwork to the wall? It can bring color and personality to the room. You can consider hanging a picture or two on the wall.
+
+![](images/c978928b7e6f57f21ef54a7668e8f6b681c89b9c1d51a7439c2de895bc2fda8c.jpg)
+
+# Can you describe the objects in the bedroom?
+
+![](images/eecf178b970caac22683bcfeb7059bc9e7721a37d4f4bede08e57a5a9fd7352d.jpg)
+
+Sure! The bedroom contains a bed, an ottoman, a picture on the wall, two lamp, two nightstand, a commode, some clothes, a window with blinds, a shelf and a bin
+
+![](images/3797c6d7ed490aa54b6005eb0b95089c3f20fa2813e147f58381e73656d2be1d.jpg)
+
+# Thank you for the information.
+
+![](images/83751c9e33a5be56265e0fa2171d988ada00792688afd1387c78aba9145d059c.jpg)  
+Figure 6. Visualization results of the scene-aware task planning and embodied conversation on the 3RPlan [21] and 3RDialog [21] datasets.
+
+You're welcome! Let me know if you need any further assistance.
+
+1. Clean the kitchen counter by wiping it down with a cleaning solution
+
+2. Clear the clutter by removing any items lying on the kitchen counter  
+3. Clean the stove by wiping off any grease or food residue.  
+4. Clean the windows by spraying a glass cleaner and a cloth.  
+5. Clean the heater by dusting it off
+
+![](images/0cfc6256a8a43cc6b56a017593a7b01256f18dbc52c591ef637724c4901caf3f.jpg)
+
+1. Arrange the pillows on the sofa and making sure they are spaced evenly.  
+2. Dust and wipe down the surfaces of the TV stand, shelves, and chest.  
+3. Switch on the TV and set the volume or change the channel as desired.  
+4. Tidy up the items on the shelves, making sure they are organized.  
+5. Take blanket off sofa and fold neatly
+
+petitors on challenging unseen tasks, while achieving seen performance on par with SOTA baselines. (2) On object navigation task, Mamba-3VL attains remarkable success rate and SPL [50] on MP3Dval and HD3M-val compared with transformer-based generalists LEO and PQ3D, suggesting Mamba-3VL prioritizes spatial-semantic relationships, e.g., obstacle avoidance and long-range path planning. Mamba-3VL excels in delivering generalized object-centric representations to LLMs, which is conceptualized as a mamba-based visual policy analogous to RT-series models [3, 45].
+
+# 4.3. Ablation Study and In-depth Analysis
+
+Compared with Linear-complexity Methods. We substitute the Mamba Mixer block with alternative linear attention approaches, i.e., Linformer [55], SOFT [39], and Focused-Linear [20] to highlight the superiority of Mamba-3VL in 3D-VL tasks. Tab. 8 demonstrates that the synergistic integration of explicit relation-prioritized scanning policy and dynamic cross-modality interaction positions Mamba-3VL as an adaptable solution for 3D-VL tasks, achieving SOTA performance while maintaining computational efficiency.
+
+Component-wise Ablation. We herein present several alternative designs to highlight advantages yielded by various modules as shown in Tab. 9. When comparing (b) and (d), the relation-prioritized spatial scanning brings substantial gains on the SQA3D [42]  $(2.3\%)$  and Multi3DRefer [67]  $(1.6\%)$  datasets, underscoring capabilities to explicitly capture intricate spatial relationships. The transition of (a) to (d) reveals a marked improvement when integrating spatial scanning with channel twisting. To explain, it propagates task-related cues across channels which facilitates multi-modality interaction. Comparing (d) and (g), IDPA with enhanced spatial relationship modeling and instance-specific positional embedding solidifies Mamba-3VL's performance in 3D-VL reasoning. As shown in Tab. 8, linear positional encoding substitutions for IDPA (Ours w/ Linear PE) produce performance degeneration, proving IDPA's superiority. Combining all modules,
+
+synergistic benefits yield a significant uplift, achieving an accuracy of 53.5 in ScanRefer [6] and 52.4 in SQA3D [42].
+
+Selection of the Scanning Mechanism. In Tab. 10, we further offer a detailed analysis to investigate the effectiveness of Mamba Mixer's scanning mechanism. Compared to random-ordering scanning, our relation-prioritized spatial scanning policy achieves significant gains across all tasks. This highlights the necessity of explicitly modeling hierarchical (local&global) relation for 3D-VL reasoning. Removing either component (i.e., w/o. FIS/NIS) results in performance degradation, suggesting their complementary roles. Moreover, channel twisting mechanism outperforms alternative fusion methods in cross-modal features interaction. Cross-attention treats all tokens within a sequence equally, failing to capture the hierarchical dependencies within 3D scenes. While AdaIN provides global feature normalization, it is not explicitly designed for task-oriented 3D spatial reasoning.
+
+We detail hyper-parameter selection (Tab. A), more framework ablations of Mamba-3VL (Tab. B, F, H), holistic results (Tab. C-E, G), and visualizations (Fig. A-D) in Appendix.
+
+# 5. Conclusion
+
+In this paper, we first investigate capabilities of State Space Model (SSM) in 3D-VL tasks. We present Mamba-3VL, a lightweight yet powerful method to perform spatial reasoning with enhanced multi-modal interaction to tackle 3D-VL tasks. Compared with transformer-based models, Mamba-3VL attains linear complexity, guaranteeing top-tier performance and generalization potential. At its core, multi-modal Mamba Mixer explicitly formulates 3D vision-language interaction via a tailored channel twisting mechanism and a spatial relation-prioritized scanning policy to model spatial relationships of 3D objects. The IDPA is proposed to capture instance-specific features and further strengthen local relation of 3D objects. Extensive experiments and visualizations demonstrate the superiority of Mamba-3VL, performing admirably on multiple 3D-VL and Embodied AI benchmarks.
+
+# References
+
+[1] Panos Achlioptas, Ahmed Abdelreheem, Fei Xia, Mohamed Elhoseiny, and Leonidas Guibas. Referit3d: Neural listeners for fine-grained 3d object identification in real-world scenes. In European Conference on Computer Vision, pages 422-440. Springer, 2020. 2, 5  
+[2] Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, and Motoaki Kawanabe. Scanqa: 3d question answering for spatial scene understanding. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 19129-19139, 2022. 2, 3, 5, 6, 7  
+[3] Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al. Rt-2: Vision-language-action models transfer web knowledge to robotic control. arXiv preprint arXiv:2307.15818, 2023. 8  
+[4] Daigang Cai, Lichen Zhao, Jing Zhang, Lu Sheng, and Dong Xu. 3djcg: A unified framework for joint dense captioning and visual grounding on 3d point clouds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 16464-16473, 2022. 2, 3, 5  
+[5] Chi-Lam Cheang, Guangzeng Chen, Ya Jing, Tao Kong, Hang Li, Yifeng Li, Yuxiao Liu, Hongtao Wu, Jiafeng Xu, Yichu Yang, et al. Gr-2: A generative video-language-action model with web-scale knowledge for robot manipulation. arXiv preprint arXiv:2410.06158, 2024. 3  
+[6] Dave Zhenyu Chen, Angel X Chang, and Matthias Nießner. Scanrefer: 3d object localization in rgb-d scans using natural language. In European Conference on Computer Vision, pages 202-221. Springer, 2020. 2, 5, 6, 8  
+[7] Dave Zhenyu Chen, Qirui Wu, Matthias Nießner, and Angel X Chang. D 3 net: A unified speaker-listener architecture for 3d dense captioning and visual grounding. In European Conference on Computer Vision, pages 487-505. Springer, 2022. 3  
+[8] Shizhe Chen, Pierre-Louis Guhur, Makarand Tapaswi, Cordelia Schmid, and Ivan Laptev. Language conditioned spatial relation reasoning for 3d object grounding. Advances in Neural Information Processing Systems, 35:20522-20535, 2022. 5  
+[9] Sijin Chen, Hongyuan Zhu, Xin Chen, Yinjie Lei, Gang Yu, and Tao Chen. End-to-end 3d dense captioning with vote2capdetr. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 11124-11133, 2023. 3  
+[10] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European Conference on Computer Vision, pages 104-120. Springer, 2020. 2  
+[11] Zhenyu Chen, Ali Gholami, Matthias Nießner, and Angel X Chang. Scan2cap: Context-aware dense captioning in rgb-d scans. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3193-3203, 2021. 2, 3, 5, 6  
+[12] Zhenyu Chen, Ronghang Hu, Xinlei Chen, Matthias Nießner, and Angel X Chang. Unit3d: A unified transformer for 3d dense captioning and visual grounding. In Proceedings of the
+
+IEEE International Conference on Computer Vision, pages 18109-18119, 2023. 2, 3, 5  
+[13] Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. Vicuna: An open-source chatbot impressing gpt-4 with  $90\%$  chatgpt quality. See https://vicuna.lmsys.org (accessed 14 April 2023), 2(3):6, 2023. 6  
+[14] Xiaoyi Dong, Jianmin Bao, Yinglin Zheng, Ting Zhang, Dongdong Chen, Hao Yang, Ming Zeng, Weiming Zhang, Lu Yuan, Dong Chen, et al. Maskclip: Masked self-distillation advances contrastive language-image pretraining. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 10995-11005, 2023. 2  
+[15] Daniel Y Fu, Tri Dao, Khaled K Saab, Armin W Thomas, Atri Rudra, and Christopher Ré. Hungry hungry hippos: Towards language modeling with state space models. arXiv preprint arXiv:2212.14052, 2022. 3  
+[16] Golnaz Ghiasi, Xiuye Gu, Yin Cui, and Tsung-Yi Lin. Scaling open-vocabulary image segmentation with image-level labels. In European Conference on Computer Vision, pages 540-557. Springer, 2022. 3  
+[17] Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023. 3  
+[18] Albert Gu, Karan Goel, and Christopher Ré. Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396, 2021.  
+[19] Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré. Combining recurrent, convolutional, and continuous-time models with linear state space layers. Advances in Neural Information Processing Systems, 34:572-585, 2021. 3  
+[20] Dongchen Han, Xuran Pan, Yizeng Han, Shiji Song, and Gao Huang. Flatten transformer: Vision transformer using focused linear attention. In Proceedings of the IEEE International Conference on Computer Vision, pages 5961-5971, 2023. 2, 8  
+[21] Jiangyong Huang, Silong Yong, Xiaojian Ma, Xiongkun Linghu, Puhao Li, Yan Wang, Qing Li, Song-Chun Zhu, Baoxiong Jia, and Siyuan Huang. An embodied generalist agent in 3d world. arXiv preprint arXiv:2311.12871, 2023. 6, 7, 8  
+[22] Shijia Huang, Yilun Chen, Jiaya Jia, and Liwei Wang. Multiview transformer for 3d visual grounding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 15524-15533, 2022. 3  
+[23] Zhening Huang, Xiaoyang Wu, Xi Chen, Hengshuang Zhao, Lei Zhu, and Joan Lasenby. Openins3d: Snap and lookup for 3d open-vocabulary instance segmentation. In European Conference on Computer Vision, pages 169-185. Springer, 2024. 5  
+[24] Ayush Jain, Nikolaos Gkanatsios, Ishita Mediratta, and Katerina Fragkiadaki. Bottom up top down detection transformers for language grounding in images and point clouds. In European Conference on Computer Vision, pages 417-433. Springer, 2022. 3, 5  
+[25] Baoxiong Jia, Yixin Chen, Huangyue Yu, Yan Wang, Xuesong Niu, Tengyu Liu, Qing Li, and Siyuan Huang. Sceneverse:
+
+Scaling 3d vision-language learning for grounded scene understanding. In European Conference on Computer Vision, pages 289-310. Springer, 2024. 2, 3  
+[26] Yang Jiao, Shaoxiang Chen, Zequn Jie, Jingjing Chen, Lin Ma, and Yu-Gang Jiang. More: Multi-order relation mining for dense captioning in 3d scenes. In European Conference on Computer Vision, pages 528-545. Springer, 2022. 5  
+[27] Bu Jin, Yupeng Zheng, Pengfei Li, Weize Li, Yuhang Zheng, Sujie Hu, Xinyu Liu, Jinwei Zhu, Zhijie Yan, Haiyang Sun, et al. Tod3cap: Towards 3d dense captioning in outdoor scenes. In European Conference on Computer Vision, pages 367-384. Springer, 2024. 3  
+[28] Zhao Jin, Munawar Hayat, Yuwei Yang, Yulan Guo, and Yinjie Lei. Context-aware alignment and mutual masking for 3d-language pre-training. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 10984-10994, 2023. 2, 3, 5, 6  
+[29] Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Openvla: An open-source vision-language-action model. arXiv preprint arXiv:2406.09246, 2024.3  
+[30] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7331-7341, 2021. 5  
+[31] Fei-Fei Li. With spatial intelligence, ai will understand the real world, 2024. Accessed: 2024-04. 1  
+[32] Heng Li, Yuenan Hou, Xiaohan Xing, Xiao Sun, and Yanyong Zhang. Occmamba: Semantic occupancy prediction with state space models. arXiv preprint arXiv:2408.09859, 2024. 3  
+[33] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in Neural Information Processing Systems, 34:9694-9705, 2021. 2  
+[34] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, pages 12888-12900. PMLR, 2022. 2  
+[35] Kunchang Li, Xinhao Li, Yi Wang, Yinan He, Yali Wang, Limin Wang, and Yu Qiao. Videomamba: State space model for efficient video understanding. In European Conference on Computer Vision, pages 237-255. Springer, 2024. 3  
+[36] Dingkang Liang, Xin Zhou, Wei Xu, Xingkui Zhu, Zhikang Zou, Xiaoqing Ye, Xiao Tan, and Xiang Bai. Pointmamba: A simple state space model for point cloud analysis. arXiv preprint arXiv:2402.10739, 2024. 3, 4  
+[37] Xiongkun Linghu, Jiangyong Huang, Xuesong Niu, Xiaojian Ma, Baoxiong Jia, and Siyuan Huang. Multi-modal situated reasoning in 3d scenes. Advances in Neural Information Processing Systems, 2024. 3  
+[38] I Loshchilov. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5  
+[39] Jiachen Lu, Jinghan Yao, Junge Zhang, Xiatian Zhu, Hang Xu, Weiguo Gao, Chunjing Xu, Tao Xiang, and Li Zhang. Soft:
+
+Softmax-free transformer with linear complexity. Advances in Neural Information Processing Systems, 34:21297-21309, 2021. 2, 8  
+[40] Shiyang Lu, Haonan Chang, Eric Pu Jing, Abdeslam Boullarias, and Kostas Bekris. Ovir-3d: Open-vocabulary 3d instance retrieval without training on 3d data. In Conference on Robot Learning, pages 1610–1620. PMLR, 2023. 5  
+[41] Jun Ma, Feifei Li, and Bo Wang. U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722, 2024. 3  
+[42] Xiaojian Ma, Silong Yong, Zilong Zheng, Qing Li, Yitao Liang, Song-Chun Zhu, and Siyuan Huang. Sqa3d: Situated question answering in 3d scenes. arXiv preprint arXiv:2210.07474, 2022. 2, 3, 5, 6, 7, 8  
+[43] Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Tingfan Wu, Jay Vakil, et al. Where are we in the search for an artificial visual cortex for embodied intelligence? Advances in Neural Information Processing Systems, 36:655-677, 2023. 3, 7  
+[44] Phuc Nguyen, Tuan Duc Ngo, Evangelos Kalogerakis, Chuang Gan, Anh Tran, Cuong Pham, and Khoi Nguyen. Open3dis: Open-vocabulary 3d instance segmentation with 2d mask guidance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4018-4028, 2024. 5  
+[45] Abby O'Neill, Abdul Rehman, Abhinav Gupta, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, et al. Open x-embodiment: Robotic learning datasets and rt-x models. arXiv preprint arXiv:2310.08864, 2023. 8  
+[46] Songyou Peng, Kyle Genova, Chiyu Jiang, Andrea Tagliasacchi, Marc Pollefeys, Thomas Funkhouser, et al. Openscene: 3d scene understanding with open vocabularies. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 815-824, 2023. 5  
+[47] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in Neural Information Processing Systems, 30, 2017. 3  
+[48] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PmLR, 2021. 7  
+[49] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1-67, 2020. 6  
+[50] Ram Ramrakhya, Eric Undersander, Dhruv Batra, and Abhishek Das. Habitat-web: Learning embodied object-search strategies from human demonstrations at scale. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5173-5183, 2022. 3, 7, 8  
+[51] David Rozenberszki, Or Litany, and Angela Dai. Language-grounded indoor 3d semantic segmentation in the wild. In
+
+European Conference on Computer Vision, pages 125-141. Springer, 2022. 2, 5, 7  
+[52] Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, and Bastian Leibe. Mask3d: Mask transformer for 3d semantic instance segmentation. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 8216-8223. IEEE, 2023. 3, 5  
+[53] Mohit Shridhar, Lucas Manuelli, and Dieter Fox. CIport: What and where pathways for robotic manipulation. In Conference on Robot Learning, pages 894–906. PMLR, 2022. 3, 7  
+[54] Ayca Takmaz, Elisabetta Fedele, Robert W Sumner, Marc Pollefeys, Federico Tombari, and Francis Engelmann. Openmask3d: Open-vocabulary 3d instance segmentation. arXiv preprint arXiv:2306.13631, 2023. 5  
+[55] Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma. Linformer: Self-attention with linear complexity. arXiv preprint arXiv:2006.04768, 2020. 2, 8  
+[56] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (tog), 38(5):1-12, 2019. 2  
+[57] Yuan Wang, Kun Yu, Chen Chen, Xiyuan Hu, and Silong Peng. Dynamic graph learning with content-guided spatial-frequency relation reasoning for deepfake detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7278-7287, 2023.  
+[58] Yuan Wang, Chen Chen, Ning Zhang, and Xiyuan Hu. Watcher: Wavelet-guided texture-content hierarchical relation learning for deepfake detection. International Journal of Computer Vision, 132(10):4746-4767, 2024. 2  
+[59] Yuan Wang, Yali Li, and Shengjin Wang. G3-lq: Marrying hyperbolic alignment with explicit semantic-geometric modeling for 3d visual grounding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 13917–13926, 2024. 2, 3, 5  
+[60] Yuan Wang, Yali Li, and Shengjin Wang. Liba: Language instructed multi-granularity bridge assistant for 3d visual groundingg. In Proceedings of the AAAI Conference on Artificial Intelligence, 2025. 5  
+[61] Yanmin Wu, Xinhua Cheng, Renrui Zhang, Zesen Cheng, and Jian Zhang. Eda: Explicit text-decoupling and dense alignment for 3d visual grounding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 19231-19242, 2023. 3, 5  
+[62] Mengjiao Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Thompson, Dale Schuurmans, and Pieter Abbeel. Learning interactive real-world simulators. arXiv preprint arXiv:2310.06114, 2023. 3  
+[63] Yunhan Yang, Xiaoyang Wu, Tong He, Hengshuang Zhao, and Xihui Liu. Sam3d: Segment anything in 3d scenes. arXiv preprint arXiv:2306.03908, 2023. 5  
+[64] Zhihao Yuan, Xu Yan, Yinghong Liao, Yao Guo, Guanbin Li, Shuguang Cui, and Zhen Li. X-trans2cap: Cross-modal knowledge transfer using transformer for 3d dense captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8563-8573, 2022. 5
+
+[65] Guowen Zhang, Lue Fan, Chenhang He, Zhen Lei, Zhaoxiang Zhang, and Lei Zhang. Voxel mamba: Group-free state space models for point cloud based 3d object detection. arXiv preprint arXiv:2406.10700, 2024. 3, 4  
+[66] Tao Zhang, Xiangtai Li, Haobo Yuan, Shunping Ji, and Shuicheng Yan. Point could mamba: Point cloud learning via state space model. arXiv preprint arXiv:2403.00762, 2024. 3, 4  
+[67] Yiming Zhang, ZeMing Gong, and Angel X Chang. Multi3drefer: Grounding text description to multiple 3d objects. In Proceedings of the IEEE International Conference on Computer Vision, pages 15225-15236, 2023. 2, 5, 6, 8  
+[68] Zeyu Zhang, Akide Liu, Ian Reid, Richard Hartley, Bohan Zhuang, and Hao Tang. Motion mamba: Efficient and long sequence motion generation. In European Conference on Computer Vision, pages 265-282. Springer, 2024. 3  
+[69] Lichen Zhao, Daigang Cai, Lu Sheng, and Dong Xu. 3dvg-transformer: Relation modeling for visual grounding on point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2928-2937, 2021. 2, 3, 5  
+[70] Duo Zheng, Shijia Huang, Lin Zhao, Yiwu Zhong, and Liwei Wang. Towards learning a generalist model for embodied navigation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 13624-13634, 2024. 3  
+[71] Gengze Zhou, Yicong Hong, and Qi Wu. Navgpt: Explicit reasoning in vision-and-language navigation with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 7641-7649, 2024. 3  
+[72] Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417, 2024. 3  
+[73] Ziyu Zhu, Xiaojian Ma, Yixin Chen, Zhidong Deng, Siyuan Huang, and Qing Li. 3d-vista: Pre-trained transformer for 3d vision and text alignment. In Proceedings of the IEEE International Conference on Computer Vision, pages 2911-2921, 2023. 2, 3, 5, 6  
+[74] Ziyu Zhu, Zhuofan Zhang, Xiaojian Ma, Xuesong Niu, Yixin Chen, Baoxiong Jia, Zhidong Deng, Siyuan Huang, and Qing Li. Unifying 3d vision-language understanding via promptable queries. In European Conference on Computer Vision, pages 188-206. Springer, 2024. 3, 4, 5, 6  
+[75] Xueyan Zou, Zi-Yi Dou, Jianwei Yang, Zhe Gan, Linjie Li, Chunyuan Li, Xiyang Dai, Harkirat Behl, Jianfeng Wang, Lu Yuan, et al. Generalized decoding for pixel, image, and language. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 15116-15127, 2023.  
+[76] Xueyan Zou, Jianwei Yang, Hao Zhang, Feng Li, Linjie Li, Jianfeng Wang, Lijuan Wang, Jianfeng Gao, and Yong Jae Lee. Segment everything everywhere all at once. Advances in Neural Information Processing Systems, 36:19769-19782, 2023.  
+[77] Xueyan Zou, Linjie Li, Jianfeng Wang, Jianwei Yang, Mingyu Ding, Junyi Wei, Zhengyuan Yang, Feng Li, Hao Zhang, Shilong Liu, et al. Interfacing foundation models' embeddings. Advances in Neural Information Processing Systems, 37:40164-40183, 2025. 3

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+# MambaML: Exploring State Space Models for Multi-Label Image Classification
+
+Xuelin Zhu $^{1}$  Jian Liu $^{2}$  Jieuxin Cao $^{3}$  Bing Wang $^{1*}$
+
+$^{1}$ The Hong Kong Polytechnic University  $^{2}$ Ant Group  $^{3}$ Southeast University
+
+{xuelin.zhu,bingwang}@polyu.edu.hk，rex.lj@antgroup.com，jx.cao@seu.edu.cn
+
+# Abstract
+
+Mamba, a selective state-space model, has recently seen widespread application across various visual tasks due to its exceptional ability to capture long-range dependencies. While promising results have been demonstrated in image classification, its potential in multi-label image classification remains underexplored. To bridge this gap, we propose a novel Mamba-based decoder, which utilizes the intrinsic attention of Mamba to aggregate visual information from image features into label embeddings, yielding label-specific visual representations. Building upon this, a MambaML framework is developed for multi-label image classification, which models the self-correlations of image features and label embeddings with bi-directional Mamba, as well as their cross-correlations with the Mamba-based decoder, allowing visual spatial relationships, label semantic dependencies, and cross-modal associations to be explored in a unified system. In this way, robust label-specific visual representations are acquired, facilitating the training of binary classifiers towards accurate label recognition. Experiments on public benchmarks suggest that our MambaML achieves performance comparable to state-of-the-art methods in multi-label image classification, while requiring fewer parameters and computational overhead.
+
+# 1. Introduction
+
+Multi-label image classification is one of the fundamental tasks in computer vision, which requires identifying multiple objects from a input image. Unlike single-label classification, where only one salient object is identified, multi-label classification faces the challenge of dealing with images containing various objects and combinations. This complexity makes the task more intractable. Multi-label image classification has garnered sustained research enthusiasm for many years due to its importance in a wide range of downstream applications, such as scene understanding [44] and human attribute recognition [35].
+
+![](images/b98fa60b0ab7d35139391003f07d910225260934c381a3ea0dcca22fa80ff6f9.jpg)  
+Figure 1. A brief illustration of the proposed MambaML framework. It incorporates a bi-directional Mamba to enhance the convolutional feature maps with the global context of the input image, and a Mamba-based decoder to acquire label-specific visual representations for classification. The decoder is instantiated by stacking multiple layers of Mamba-based cross-attention module and bidirectional Mamba module. The former aggregates visual information from the feature maps into the query tokens, while the latter captures semantic dependencies among these tokens.
+
+One of the central challenges in multi-label image classification is effectively modeling the complex correlations among labels. Existing approaches can be broadly divided into three categories: those based on recurrent neural networks (RNNs) [4, 49, 58], those utilizing graph networks [6, 8, 51, 59, 67], and those employing multi-head self-attention (MHA) [9, 33, 37, 66, 68]. While promising performance has been achieved, these studies face substantial challenges in terms of time and computational efficiency. In particular, RNN-based and graph network-based methods suffer from the time-consuming process of iterative propagation, whereas MHA-based approaches are burdened by quadratic complexity concerning image size. These inherent limitations motivate the development of an innovative architecture that offers linear computational complexity and parallel acceleration capability, thereby enhancing the efficiency of multi-label image classification.
+
+In light of the above issue, we focus on Mamba [20], renowned for its exceptional ability to capture long-range dependencies in sequential data across various natural language processing tasks. Mamba enhances state space models (SSMs) [22, 46] with a selective mechanism that retains or discards information from each sequential token based on its significance. Coupled with hardware-aware optimizations for parallel computing, Mamba generates robust representations with linear computational complexity, encouraging extensive exploration for image classification. Existing methods [32, 39, 55, 57, 65] divide input images into a set of non-overlapping patches, which are then linearly projected into vector sequence and processed using vanilla Mamba to capture contextual dependencies through linear scanning. Additionally, diverse scanning strategies, such as multi-directional raster scan [39, 65], local scan [32], continuous scan [57] and tree topology scan [55], are explored to enhance Mamba's ability to model spatial relationships among image patches. Despite achieving impressive performance, these architectures fail to process sequences of differing lengths, such as label embeddings and patch features. Consequently, the potential of Mamba for multi-label image classification remains unexplored.
+
+In this study, we begin by introducing the intrinsic attention mechanism of Mamba during linear scanning, providing intuition for the design of Mamba-based cross-attention. Next, we present a novel MambaML framework, which takes label embeddings as learnable query tokens to acquire label-specific visual representations for multi-label image classification. As illustrated in Figure 1, MambaML initially incorporates a bi-directional Mamba to enhance visual features with global image context. Subsequently, a Mamba-based decoder is developed, with each layer consisting of a Mamba-based cross-attention module and a bidirectional Mamba module. The former aggregates visual information from image features into query tokens, while the latter explores semantic dependencies between these tokens. Specifically, we propose state space sharing and label-wise pooling strategies for Mamba-based cross-attention, addressing the challenge posed by sequences of different lengths. Consequently, each decoder layer effectively captures self-correlations among query tokens and their cross-correlations with image features. After multiple layers of updating, the output query tokens adeptly extract essential information from the input image, producing robust visual representations for multi-label classification. Overall, our main contributions are summarized as follows:
+
+- We introduce a Mamba-based cross-attention mechanism, which endows Mamba with the ability to capture cross-correlations between label embeddings and image features. To the best of our knowledge, this work represents the first exploration of the Mamba architecture for multi-label image classification.
+
+- We develop an advanced MambaML framework, which is capable of jointly modeling spatial relationships between image features, semantic dependencies among label embeddings, as well as their cross-correlations, thereby producing robust label-specific visual representations.  
+- We conduct extensive experiments on public benchmarks to show the superiority of our MambaML framework in multi-label image classification, which achieves comparable performance to state-of-the-art methods while requiring fewer parameters and FLOPs.
+
+# 2. Related Work
+
+# 2.1. Multi-label Image Classification
+
+Multi-label image classification has garnered increasing attention over the years in computer vision and supports many downstream applications [18, 19]. To capture label correlations, existing studies can be categorized into sequence-based and graph-based methods. Sequence-based methods [4, 49, 58] aim to exploit semantic correlations of label representations by sequential models, while graph-based methods model label dependencies through graph message propagation. For example, Chen et al. [8] employed graph convolutional networks to map label graphs into separate label classifiers for prediction. Wang et al. [51], Chen et al. [6] and Zhu et al. [67] constructed directed label graphs using data co-occurrence statistics to guide interactions among label representations. Ye et al. [59] modeled instance-level label graphs as a parameterized module, incorporating them into end-to-end training within the overall framework.
+
+On the other hand, spatial dependencies among objects within images are critical in multi-label image classification. Wang et al. [52] used a spatial transformation layer to pinpoint target regions and LSTM units [29] to capture global dependencies among these regions, sequentially predicting label confidences. Chen et al. [5] proposed a recurrent attention reinforcement learning framework that detects informative regions iteratively and explicitly models their long-term dependencies, using these attention regions for label prediction. Wu et al. [53] introduced a graph matching mechanism to explore potential instance-label matches for multi-label classification. However, the iterative processes inherent in sequential models and graph models cannot be parallelized, resulting in significant time overhead.
+
+Recently, Vision Transformer (ViT) [14] has fueled research in multi-label image classification. Cheng et al. [9] developed a multi-label Transformer that utilizes window partitioning, intra-window pixel attention, and cross-window attention for label recognition. Lanchantin et al. [33] designed a framework based on the Transformer encoder to effectively model the complex dependencies between visual features and labels. Zhu et al. [66] proposed a dual-stream Transformer network to simultaneously ex
+
+plore label correlations and cross-modal textual-visual interactions. Liu et al. [37] employed label embeddings as queries, using the Transformer decoder to selectively extract class-related features from visual feature maps. Zhu et al. [68] extracted pixel-level representations using multihead self-attention for enhanced classification.
+
+Although current methods have achieved considerable performance gains in multi-label image classification, they either suffer from iterative propagation latency of sequential models and graph networks or face challenges of quadratic computational complexity in multi-head attention. These limitations motivate the development of a novel architecture capable of modeling label correlations, while providing linear computational complexity and parallel acceleration.
+
+# 2.2. State Space Models
+
+As pioneers in this domain, Gu et al. [23] were the first to integrate the linear state space layer into the HiPPO framework [21], addressing the challenge of modeling long-range dependencies in an efficient way. Building on this foundation, Gu et al. [22] further improved the efficiency of SSMs by parameterizing them as a diagonal plus low-rank matrix. The breakthrough success of the so-called S4 model has since spurred significant interest and ushered in a new era for SSMs [16, 25, 30]. To achieve Transformer-like modeling capabilities with linear complexity, Gu & Dao [20] introduced Mamba, which integrates a data-dependent selection mechanism into the S4 layer, streamlining both computation and architecture in a hardware-friendly manner. Further simplifying the parameter matrix into a scalar representation, Dao & Gu [12] developed Mamba2, revealing intrinsic connections between SSMs and attention mechanisms through a specific structured matrix. This innovation enables the exploration of larger, more expressive state spaces without compromising efficiency.
+
+However, challenges persist when processing visual data due to its inherent complexity and multi-dimensional spatial structures, which have hindered the progress of visual SSMs. One of the pioneering approaches was S4ND [42], which applied independent 1D SSMs to each dimension of multi-dimensional data. Inspired by this, Baron et al. [2] later extended the idea into a discrete multi-axial framework by introducing the 2D-SSM spatial layer, effectively adapting 1D SSMs for two-dimensional operation. To date, visual SSMs have explored various scanning orders and patterns to preserve spatial consistency, including bidirectional scanning [39], four-way scanning [65], continuous scanning [45], zigzag scanning [31], window-based methods [32], and topology-based scanning [26]. Such diverse approaches reveal the promising potential of SSMs in enhancing visual understanding, as evidenced by their successful application in multimodal foundation models [41], image restoration [24], and various other visual tasks [3, 34].
+
+Existing Mamba-based architectures are limited to processing a single sequence of image patches, concentrating mainly on modeling relationships among these patches for image classification. They struggle to manage multiple sequences of varying lengths, highlighting the need for an extended Mamba architecture that can effectively capture cross-correlations between label embeddings and image features for multi-label image classification.
+
+# 3. Methodology
+
+In this section, we present details of the proposed method, covering background of Mamba in Sec. 3.1, Mamba attention in Sec. 3.2, and the MambaML framework in Sec. 3.3.
+
+# 3.1. Background
+
+SSMs in continuous systems map a input signal  $x_{t} \in \mathbb{R}^{D}$  into the output  $y_{t} \in \mathbb{R}^{D}$  by a hidden state vector  $h_t \in \mathbb{R}^N$ , where  $t, D$  and  $N$  are the time step, channel number and state size, respectively. The procedure is formulated as:
+
+$$
+h _ {t} ^ {\prime} = \mathbf {A} h _ {t} + \mathbf {B} x _ {t}, \quad y _ {t} = \mathbf {C} h _ {t}, \tag {1}
+$$
+
+where  $\mathbf{A} \in \mathbb{R}^{N \times N}$ ,  $\mathbf{B} \in \mathbb{R}^{N \times D}$ ,  $\mathbf{C} \in \mathbb{R}^{D \times N}$ . Structured state space sequence models (S4) integrate such continuous-time SSMs into deep learning community by the technique discretization based on Zero-Order Hold (ZOH) rule. Given a timescale parameter  $\Delta \in \mathbb{R}^D$ , the discretization process is formulated as:
+
+$$
+\begin{array}{l} \overline {{\mathbf {A}}} = \exp (\boldsymbol {\Delta} \mathbf {A}), \\ - \end{array} \tag {2}
+$$
+
+$$
+\overline {{\mathbf {B}}} = (\boldsymbol {\Delta} \mathbf {A}) ^ {- 1} (\exp (\boldsymbol {\Delta} \mathbf {A}) - \mathbf {I}) \cdot \boldsymbol {\Delta} \mathbf {B}.
+$$
+
+Mamba [20] is developed to address the limitation of linear time invariance in SSMs. With this purpose, it obtains input-conditioned parameters  $(\Delta, \mathbf{B}, \mathbf{C})$  by mapping linearly from the input sequence, allowing a time-varying system to focus more on relevant inputs for yielding more accurate representations. Providing the whole input sequence  $\{x_i\}_{i=1}^L$  with  $L$  being the sequence length, the per-time mapping process is denoted as follows:
+
+$$
+\mathbf {B} _ {i} = f _ {B} \left(x _ {i}\right),
+$$
+
+$$
+\mathbf {C} _ {i} = f _ {C} \left(x _ {i}\right), \tag {3}
+$$
+
+$$
+\boldsymbol {\Delta} _ {i} = \operatorname {s o f t p l u s} (f _ {\Delta} (x _ {i})),
+$$
+
+where  $f_{B}(\cdot), f_{C}(\cdot)$  and  $f_{\Delta}(\cdot)$  are linear layers; softplus  $(\cdot)$  is an element-wise function that smoothly approximates the ReLU activation. Then, following the ZOH-based discretization rule in Eq. (2), the parameters  $\overline{\mathbf{A}}_i, \overline{\mathbf{B}}_i$  can be obtained, leading to the discretized version of the Eq. (1):
+
+$$
+h _ {i} = \overline {{\mathbf {A}}} _ {i} h _ {i - 1} + \overline {{\mathbf {B}}} _ {i} x _ {i}, \quad y _ {i} = \mathbf {C} _ {i} h _ {i}. \tag {4}
+$$
+
+Mamba has been demonstrated to offer performance comparable to Transformer while requiring significantly lower computational and memory complexity.
+
+# 3.2. Attention in Mamba
+
+By rolling out the recurrent procedure in Eq. (4) within Mamba, we are able to obtain the following matrix multiplication form:
+
+$$
+\left[ y _ {1}, y _ {2}, \dots , y _ {L} \right] ^ {\top} = \boldsymbol {\alpha} \left[ x _ {1}, x _ {2}, \dots , x _ {L} \right] ^ {\top}, \tag {5}
+$$
+
+where  $\alpha \in \mathbb{R}^{L\times L}$  is a lower triangular attention matrix. Each entity  $\alpha_{i,j}$  is computed as follows:
+
+$$
+\alpha_ {i, j} = \left\{ \begin{array}{l l} 0, & \text {i f} i <   j, \\ \mathbf {C} _ {i} \prod_ {k = j + 1} ^ {i} \overline {{\mathbf {A}}} _ {k} \overline {{\mathbf {B}}} _ {j}, & \text {i f} i \geq j. \end{array} \right. \tag {6}
+$$
+
+Then, following previous methods [39, 55], we use an approximation of  $\overline{\mathbf{B}}_j$  based on the first-order Taylor Series:
+
+$$
+\begin{array}{l} \overline {{\mathbf {B}}} _ {j} = (\pmb {\Delta} _ {j} \mathbf {A}) ^ {- 1} (\exp (\pmb {\Delta} _ {j} \mathbf {A}) - \mathbf {I}) \cdot \pmb {\Delta} _ {j} \mathbf {B} _ {j} \\ \approx (\boldsymbol {\Delta} _ {j} \mathbf {A}) (\boldsymbol {\Delta} _ {j} \mathbf {A}) ^ {- 1} \boldsymbol {\Delta} _ {j} \mathbf {B} _ {j} \tag {7} \\ = \boldsymbol {\Delta} _ {j} \mathbf {B} _ {j}. \\ \end{array}
+$$
+
+Hence, by substituting the Eq. (2), Eq. (3) and Eq. (7) into the Eq. (6), the  $\alpha_{i,j}$  ( $i \geq j$ ) can be reformulated as follows:
+
+$$
+\alpha_ {i, j} \approx Q _ {i} H _ {i, j} K _ {j}, \tag {8}
+$$
+
+where  $Q_{i},H_{i,j}$  and  $K_{j}$  are defined as:
+
+$$
+\begin{array}{l} Q _ {i} = f _ {C} \left(x _ {i}\right), \\ H _ {i, j} = \prod_ {k = j + 1} ^ {i} \exp (\operatorname {s o f t p l u s} \left(f _ {\Delta} \left(x _ {k}\right)\right) \mathbf {A}), \tag {9} \\ K _ {j} \approx \mathrm {s o f t p l u s} (f _ {\Delta} (x _ {j})) f _ {B} (x _ {j}). \\ \end{array}
+$$
+
+Specifically,  $H_{i,j}$  underscores the significance of the consecutive input tokens from  $x_i$  to  $x_j$ , which is similar to the discussion in [1]. The above formulation inspires us that the attention in Mamba measures the correlation between  $x_i$  and  $x_j$  by mapping them into query and key vectors respectively, exhibiting similar behavior to the self-attention mechanism in Transformer [14, 48]. Notably, when both  $x_i$  and  $x_j$  originate from the same sequence, Mamba captures the self-correlations within the sequence. Conversely, when  $x_i$  and  $x_j$  are from different sequences, Mamba models the cross-correlations between two sequences.
+
+# 3.3. The MambaML Framework
+
+Based on the discussion in Sec. 3.2, we present the details of the MambaML framework developed for multi-label image classification. For convenience, we denote the candidate label set as  $\{l_1, l_2, \dots, l_N\}$ , where  $N$  is the total number of labels. Image features extracted by visual backbones are denoted as  $\mathcal{F} \in \mathbb{R}^{M \times D}$  with  $D$  being the channel dimension.  $M$  represents the number of patches for ViT [14] and Mamba-based backbones [65] while the flattened convolutional regions for CNN-based backbones [27].
+
+![](images/0f8bd6cbbd04e53110a596dcfa7c04d9d50c6672e7e8accaea43740f65e00530.jpg)  
+Figure 2. A detailed illustration of the proposed Mamba-based decoder. The set  $\{f_1, f_2, \dots, f_M\}$  results from unpacking the image feature maps  $F$ . Similarly,  $\{q_1^l, q_2^l, \dots, q_N^l\}$  is derived by unpacking the query matrix  $Q_l$ , where each  $q_i^l$  represents the visual representation of the label  $l_i$  at the  $l$ -th layer of the decoder.
+
+Mamba-based Context Modeling. It is well known that, due to the limited size of convolutional kernels, the convolutional feature maps lack the ability to perceive the global image context. Therefore, MambaML first incorporates a bi-directional Mamba for data-dependent global visual context modeling, formulated as:
+
+$$
+F = \operatorname {M a m b a} _ {f w d} (\mathcal {F}) + \operatorname {M a m b a} _ {b w d} (\mathcal {F}), \tag {10}
+$$
+
+where  $\mathrm{Mamba}_{\mathrm{fwd}}(\cdot)$  and  $\mathrm{Mamba}_{\mathrm{bwd}}(\cdot)$  represent the Mamba-based sequence modeling processes for forward scanning and backward scanning, respectively.
+
+Mamba-based Decoder. Once the global context-aware image features are encoded, we further develop a Mamba-based decoder for label-specific visual representation learning. As illustrated in Figure 2, label embeddings act as queries to probe and gather visual information of interest from the feature maps  $F$ , yielding precise representations for subsequent binary classification. Specifically, candidate labels are embedded by  $N$  randomly initialized  $D$ -dimensional vectors, which are packed into a query matrix  $Q_0 \in \mathbb{R}^{N \times D}$  for joint learning with the entire framework in an end-to-end manner. Then, the query matrix  $Q_0$  and feature maps  $F$  are input into the Mamba-based decoder for acquiring label-specific visual representations.
+
+The decoding process for the  $l$ -th layer of the Mamba-based decoder is detailed in Algo. 1. Concretely, in the Mamba-based decoder, the input control matrix  $\mathbf{B}$  and the output control matrix  $\mathbf{C}$  are linearly mapped from the image features and label embeddings, respectively, as shown in line 3 and line 7 of Algo. 1. Recalling the attention mechanism in Mamba discussed in Eq. (8) and Eq. (9), this decoding process is essentially a cross-attention between the label embeddings  $Q_{l - 1}$  and image features  $F$ , where the former acts as the query and the latter serves as the key. Consequently, label embeddings and image features are cor
+
+Algorithm 1 Mamba-based Decoder  
+Input: label embedding matrix  $Q_{l-1}$ : (B, N, D) image feature matrix  $F$ : (B, M, D)  
+Output: updated label embedding matrix  $Q_l$ : (B, N, D)  
+1:  $F': (B, M, D) \gets f_{in}(F)$   
+2: A: (D, E)  $\leftarrow$  Parameter  $\triangleright$  Represents structured E × E matrix  
+3: B: (B, M, E)  $\leftarrow$ $f_B(F')$   
+4:  $\Delta$ : (B, M, D)  $\leftarrow$  softmax  $(f_{\Delta}(F'))$   
+5:  $\overline{A}, \overline{B}$ : (B, M, D, E)  $\leftarrow$  discretize  $(\Delta, A, B)$   
+6: h: (B, D, E, M)  $\leftarrow$  SSM  $(\overline{A}, \overline{B}, \text{None})(F)$   
+7: C: (B, N, E)  $\leftarrow$ $f_C(Q_{l-1})$   
+8: h': (B, D, E, M, 1)  $\leftarrow$  reshape(h)  
+9: C': (B, 1, E, 1, N)  $\leftarrow$  reshape(C)  
+10:  $Q_l': (B, D, E, M, N) \gets C' \cdot h'$   
+11:  $Q_l''': (B, D, M, N) \gets \text{sum}(Q_l', \dim = 2)$ $\triangleright$  Summation along the third (hidden state) dimension  
+12:  $Q_l'''': (B, D, N) \gets \max(Q_l'', \dim = 2)$ $\triangleright$  Max-pooling along the third (spatial) dimension  
+13:  $Q_l'''': (B, N, D) \gets f_{out}(Q_{l-1} + \text{Norm}(Q_l'')')$   
+14: Ql: (B, N, D)  $\leftarrow$  Mamba_fdw(Ql'') + Mamba_bwd(Ql''')  
+15: return Ql
+
+related in a linear scanning manner, allowing the visual information of the input image to flow into the label embeddings via Mamba's state space. Additionally, the semantic dependencies among label embeddings are further explored through a bi-directional Mamba, as shown in line 14 of Algo. 1. In this way, self-correlations among label embeddings and their cross-correlations with image features are both explored in a purely Mamba-based architecture with linear complexity. Through iterative updates across layers, label-specific visual representations are finally obtained for subsequent training of binary classifiers.
+
+Specifically, line 8 in Algo. 1 replicates the hidden states  $N$  times to align with the length of label embedding sequence<sup>1</sup>. The intuition behind this is that all labels share the same state space, thus obviating the need of linear scanning for each label, which significantly improves model's efficiency. Similarly, line 9 in Algo. 1 replicates the control vector of each label  $M$  times. This aligns them with the length of the image feature sequence, allowing the linear scanning in Mamba to proceed smoothly and aggregate the visual information into the replicas of each label embedding. Owing to the Mamba-based context modeling in Eq. (10), each token in the image feature sequence  $F$  is rich in global information of the input image, thus enabling each label embedding replica to aggregate meaningful visual information fully. Naturally, as seen in line 12 of Algo. 1, we further apply a max pooling operation to those replicas, cap
+
+turing essential features and yielding robust label-specific visual representations. Finally, a bi-directional Mamba is applied to these label representations to model their semantic dependencies, as shown in line 14 of Algo. 1.
+
+Consequently, by integrating the Mamba-based context modeling module and Mamba-based decoder, the comprehensive MambaML framework is constructed for multi-label image classification. With this framework, the spatial relationships among image features, the semantic dependencies among label representations, as well as their cross-correlations are simultaneously explored, thereby producing robust label-specific visual representations and facilitating subsequent multi-label image classification.
+
+Classification. Following scanning and modeling by the Mamba-based decoder across  $L$  layers, we derive a label embedding matrix  $Q_{L}\in \mathbb{R}^{N\times D}$ . This matrix is then unpacked into a sequence of vectors  $\{q_i\}_{i = 1}^N$ , where each vector  $q_{i}$  encapsulates the visual representation of the label  $l_{i}$  with respect to the input image. Subsequently, a linear classifier is used to compute the probability:
+
+$$
+p _ {i} = \sigma \left(w _ {i} ^ {\top} q _ {i}\right), \tag {11}
+$$
+
+where  $\sigma (\cdot)$  is sigmoid function;  $w_{i}\in \mathbb{R}^{D}$  is a learnable vector. Following previous approaches [6, 37], we employ the asymmetric loss [43] to train binary classifiers for each label for prediction, formulated as:
+
+$$
+\mathcal {L} = - \frac {1}{N} \sum_ {i = 0} ^ {N - 1} \left\{ \begin{array}{l l} (1 - p _ {i}) ^ {\gamma^ {+}} \log p _ {i}, & y _ {i} = 1, \\ p _ {i} ^ {\gamma^ {-}} \log (1 - p _ {i}), & y _ {i} = 0, \end{array} \right. \tag {12}
+$$
+
+where  $y_{i}$  is a binary indicator;  $y_{i} = 1$  if the label  $l_{i}$  appears in the input image and 0 otherwise. The hyper-parameters  $\gamma^{+}$  and  $\gamma^{-}$  regulate the weights of positive and negative labels, respectively.
+
+# 4. Experiments
+
+# 4.1. Implementation Details
+
+In our experiments, we choose the widely used ResNet-101 [27] as vision backbone network for multi-label image classification. Unless otherwise specified, the resolution of input images is resized into  $448 \times 448$  in both the training and testing phases throughout all experiments. The whole framework is trained for 80 epochs using AdamW [40] optimizer with a batch size of 128 and 1-cycle policy [47] with a maximum learning rate of 0.0001. Following the previous works [37, 43], we adopt the RandAugment [11] and Cutout [13] for data augmentation, and apply exponential moving average to model parameters with a default decay of 0.9997. The hyper-parameters  $\gamma^{+}$  and  $\gamma^{-}$  of the asymmetric loss [43] are set to 0 and 1, respectively. All experiments are conducted on a server with 8 NVIDIA A800 GPUs.
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">Resolution</td><td rowspan="2">#param. (M)</td><td rowspan="2">FLOPs (G)</td><td rowspan="2">mAP</td><td colspan="6">All</td><td colspan="6">Top-3</td></tr><tr><td>CP</td><td>CR</td><td>CF1</td><td>OP</td><td>OR</td><td>OF1</td><td>CP</td><td>CR</td><td>CF1</td><td>OP</td><td>OR</td><td>OF1</td></tr><tr><td>ML-GCN [8]</td><td>448×448</td><td>42.5</td><td>31.5</td><td>83.0</td><td>85.1</td><td>72.0</td><td>78.0</td><td>85.8</td><td>75.4</td><td>80.3</td><td>89.2</td><td>64.1</td><td>74.6</td><td>90.5</td><td>66.5</td><td>76.7</td></tr><tr><td>CMA [60]</td><td>448×448</td><td>-</td><td>-</td><td>83.4</td><td>82.1</td><td>73.1</td><td>77.3</td><td>83.7</td><td>76.3</td><td>79.9</td><td>87.2</td><td>64.6</td><td>74.2</td><td>89.1</td><td>66.7</td><td>76.3</td></tr><tr><td>TSGCN [56]</td><td>448×448</td><td>-</td><td>-</td><td>83.5</td><td>81.5</td><td>72.3</td><td>76.7</td><td>84.9</td><td>75.3</td><td>79.8</td><td>84.1</td><td>67.1</td><td>74.6</td><td>89.5</td><td>69.3</td><td>69.3</td></tr><tr><td>MCAR [17]</td><td>448×448</td><td>-</td><td>-</td><td>83.8</td><td>85.0</td><td>72.1</td><td>78.0</td><td>88.0</td><td>73.9</td><td>80.3</td><td>88.1</td><td>65.5</td><td>75.1</td><td>91.0</td><td>66.3</td><td>76.7</td></tr><tr><td>CSRA [64]</td><td>448×448</td><td>43.2</td><td>31.6</td><td>83.5</td><td>84.1</td><td>72.5</td><td>77.9</td><td>85.6</td><td>75.7</td><td>80.3</td><td>88.5</td><td>64.2</td><td>74.4</td><td>90.4</td><td>66.4</td><td>76.5</td></tr><tr><td>ASL [43]</td><td>448×448</td><td>42.7</td><td>31.5</td><td>85.0</td><td>-</td><td>-</td><td>80.3</td><td>-</td><td>-</td><td>82.3</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Q2L-R101 [37]</td><td>448×448</td><td>143.1</td><td>43.1</td><td>84.9</td><td>84.8</td><td>74.5</td><td>79.3</td><td>86.6</td><td>76.9</td><td>81.5</td><td>78.0</td><td>69.1</td><td>73.3</td><td>80.7</td><td>70.6</td><td>75.4</td></tr><tr><td>DATran [62]</td><td>448×448</td><td>133.0</td><td>48.0</td><td>84.9</td><td>84.8</td><td>74.9</td><td>79.6</td><td>86.0</td><td>77.6</td><td>81.6</td><td>88.8</td><td>66.2</td><td>75.8</td><td>90.7</td><td>67.7</td><td>77.5</td></tr><tr><td>C-TMS [54]</td><td>448×448</td><td>217.0</td><td>31.4</td><td>85.1</td><td>87.2</td><td>74.2</td><td>79.1</td><td>88.7</td><td>76.5</td><td>81.4</td><td>90.1</td><td>67.5</td><td>76.0</td><td>92.3</td><td>71.2</td><td>78.3</td></tr><tr><td>MambaML</td><td>448×448</td><td>85.0</td><td>36.9</td><td>85.7</td><td>86.1</td><td>75.1</td><td>80.3</td><td>87.7</td><td>77.6</td><td>82.3</td><td>89.4</td><td>66.5</td><td>76.3</td><td>91.5</td><td>68.2</td><td>78.1</td></tr><tr><td>SSGRL [6]</td><td>576×576</td><td>87.9</td><td>68.7</td><td>83.6</td><td>89.5</td><td>68.3</td><td>76.9</td><td>91.2</td><td>70.7</td><td>79.3</td><td>91.9</td><td>62.1</td><td>73.0</td><td>93.6</td><td>64.2</td><td>76.0</td></tr><tr><td>C-Tran [33]</td><td>576×576</td><td>67.9</td><td>62.2</td><td>85.1</td><td>86.3</td><td>74.3</td><td>79.9</td><td>87.7</td><td>76.5</td><td>81.7</td><td>90.1</td><td>65.7</td><td>76.0</td><td>92.1</td><td>71.4</td><td>77.6</td></tr><tr><td>ADD-GCN [59]</td><td>576×576</td><td>48.2</td><td>53.0</td><td>85.2</td><td>84.7</td><td>75.9</td><td>80.1</td><td>84.9</td><td>79.4</td><td>82.0</td><td>88.8</td><td>66.2</td><td>75.8</td><td>90.3</td><td>68.5</td><td>77.9</td></tr><tr><td>Q2L-R101 [37]</td><td>576×576</td><td>143.1</td><td>67.8</td><td>86.5</td><td>85.8</td><td>76.7</td><td>81.0</td><td>87.0</td><td>78.9</td><td>82.8</td><td>90.4</td><td>66.3</td><td>76.5</td><td>92.4</td><td>67.9</td><td>78.3</td></tr><tr><td>C-TMS [54]</td><td>576×576</td><td>217.0</td><td>-</td><td>86.2</td><td>88.2</td><td>75.3</td><td>80.4</td><td>89.6</td><td>77.2</td><td>82.5</td><td>91.1</td><td>68.7</td><td>77.0</td><td>93.3</td><td>72.5</td><td>79.4</td></tr><tr><td>DATran [62]</td><td>576×576</td><td>133.0</td><td>-</td><td>86.3</td><td>86.6</td><td>76.1</td><td>81.0</td><td>87.2</td><td>78.9</td><td>82.8</td><td>90.2</td><td>66.9</td><td>76.8</td><td>91.6</td><td>68.5</td><td>78.4</td></tr><tr><td>MambaML</td><td>576×576</td><td>85.0</td><td>59.6</td><td>86.7</td><td>86.9</td><td>76.4</td><td>81.3</td><td>88.0</td><td>79.0</td><td>83.2</td><td>90.0</td><td>67.1</td><td>76.9</td><td>91.9</td><td>68.8</td><td>78.7</td></tr></table>
+
+# 4.2. Comparisons with State-of-the-Arts
+
+In this subsection, we compare the performance of our proposed MambaML framework with its counterparts on the Microsoft COCO 2014 [36], NUS-WIDE [10] and Pascal VOC 2007 [15] datasets.
+
+Comparison on Microsoft COCO. As another prevalent benchmark in the multi-label image classification task, Microsoft COCO 2014 [36] (MS-COCO) has 82,081 images for training set and 40,137 images for validation set, covering 80 labels with almost 2.9 labels per image. Similar to previous approaches [37, 43, 64], Table 1 presents the precision, recall and F1-measure under the settings of all and top-3 labels of the listed models. Specifically, the upper part compares the performance of the proposed MambaML framework with other competitors using the standard input resolution of  $448 \times 448$ . Our MambaML framework achieves  $85.7\%$  in mAP, leading C-TMS [54] by  $0.6\%$  in mAP, a considerable improvement in multi-label image classification. When using larger input resolution of  $576 \times 576$ , MambaML also implements best performance in mAP, suggesting its robustness and scalability in terms of the input image size. In addition, The efficiencies of all models are also reported in Table 1. As shown, our MambaML outperforms several recent models—including DATran [62] and C-TMS [54]—by achieving higher mAP scores while simultaneously requiring fewer parameters (#param.) and computation (FLOPs) across both input resolutions. Overall, our MambaML framework strikes an excellent balance between performance and efficiency in multi-label image classification. We leave the comparisons with more recent methods to be discussed in Appendix B.
+
+Table 1. Experimental results on the Microsoft COCO dataset under the settings of all and top-3 labels (mAP in %). The optimal scores are highlighted in bold.  
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">mAP</td><td colspan="2">All</td><td colspan="2">Top-3</td></tr><tr><td>CF1</td><td>OF1</td><td>CF1</td><td>OF1</td></tr><tr><td>CNN-RNN [49]</td><td>56.1</td><td>-</td><td>-</td><td>34.7</td><td>55.2</td></tr><tr><td>SRN [63]</td><td>62.0</td><td>58.5</td><td>73.4</td><td>48.9</td><td>62.2</td></tr><tr><td>S-CLs [38]</td><td>60.1</td><td>58.7</td><td>73.7</td><td>53.8</td><td>71.1</td></tr><tr><td>CMA [60]</td><td>61.4</td><td>60.5</td><td>73.7</td><td>55.5</td><td>70.0</td></tr><tr><td>GM-MLIC [53]</td><td>62.2</td><td>61.0</td><td>74.1</td><td>55.3</td><td>72.5</td></tr><tr><td>ICME [7]</td><td>62.8</td><td>60.7</td><td>74.1</td><td>56.3</td><td>70.6</td></tr><tr><td>ASL [43]</td><td>63.9</td><td>62.7</td><td>74.6</td><td>-</td><td>-</td></tr><tr><td>Q2L [37]</td><td>65.0</td><td>63.1</td><td>75.0</td><td>-</td><td>-</td></tr><tr><td>C-TMS [54]</td><td>62.8</td><td>61.4</td><td>74.6</td><td>55.2</td><td>72.8</td></tr><tr><td>MambaML</td><td>65.9</td><td>63.7</td><td>75.0</td><td>59.8</td><td>70.7</td></tr></table>
+
+Table 2. Experimental results on the NUS-WIDE dataset under the settings of all and top-3 labels (mAP in%). The optimal scores are highlighted in bold.
+
+Comparison on NUS-WIDE. As a web dataset from the Flickr, the original NUS-WIDE [10] dataset contains a training set of 125,449 images and a testing set of 83,898 images. We follow the official split and that use 150,000 images for training and the remaining 59,347 images for testing. Experimental results are presented in Table 2. Compared with the Q2L model [37], our proposed MambaML framework achieves pronounced performance gain, improving mAP score from  $65.0\%$  to  $65.9\%$ , a total of  $0.9\%$  improvement. On other significant metrics, such as CF1 and OF1, our MambaML framework also achieves best scores in most cases. The remarkable merits suggest the superior capacity of our MambaML in learning from the large-scale image dataset for multi-label image classification.
+
+<table><tr><td>Method</td><td>plane</td><td>bike</td><td>bird</td><td>boat</td><td>bottle</td><td>bus</td><td>car</td><td>cat</td><td>chair</td><td>cow</td><td>table</td><td>dog</td><td>horse</td><td>motor</td><td>persn</td><td>plant</td><td>sheep</td><td>sofa</td><td>train</td><td>tv</td><td>mAP</td></tr><tr><td>RMIC [28]</td><td>97.1</td><td>91.3</td><td>94.2</td><td>57.1</td><td>86.7</td><td>90.7</td><td>93.1</td><td>63.3</td><td>83.3</td><td>76.4</td><td>92.8</td><td>94.4</td><td>91.6</td><td>95.1</td><td>92.3</td><td>59.7</td><td>86.0</td><td>69.5</td><td>96.4</td><td>79.0</td><td>84.5</td></tr><tr><td>RLSD [61]</td><td>96.4</td><td>92.7</td><td>93.8</td><td>94.1</td><td>71.2</td><td>92.5</td><td>94.2</td><td>95.7</td><td>74.3</td><td>90.0</td><td>74.2</td><td>95.4</td><td>96.2</td><td>92.1</td><td>97.9</td><td>66.9</td><td>93.5</td><td>73.7</td><td>97.5</td><td>87.6</td><td>88.5</td></tr><tr><td>RDAR [52]</td><td>98.6</td><td>97.4</td><td>96.3</td><td>96.2</td><td>75.2</td><td>92.4</td><td>96.5</td><td>97.1</td><td>76.5</td><td>92.0</td><td>87.7</td><td>96.8</td><td>97.5</td><td>93.8</td><td>98.5</td><td>81.6</td><td>93.7</td><td>82.8</td><td>98.6</td><td>89.3</td><td>91.9</td></tr><tr><td>RARL [5]</td><td>98.6</td><td>97.1</td><td>97.1</td><td>95.5</td><td>75.6</td><td>92.8</td><td>96.8</td><td>97.3</td><td>78.3</td><td>92.2</td><td>87.6</td><td>96.9</td><td>96.5</td><td>93.6</td><td>98.5</td><td>81.6</td><td>93.1</td><td>83.2</td><td>98.5</td><td>89.3</td><td>92.0</td></tr><tr><td>RCP [50]</td><td>99.3</td><td>97.6</td><td>98.0</td><td>96.4</td><td>79.3</td><td>93.8</td><td>96.6</td><td>97.1</td><td>78.0</td><td>88.7</td><td>87.1</td><td>97.1</td><td>96.3</td><td>95.4</td><td>99.1</td><td>82.1</td><td>93.6</td><td>82.2</td><td>98.4</td><td>92.8</td><td>92.5</td></tr><tr><td>SSGRL†[6]</td><td>99.5</td><td>97.1</td><td>97.6</td><td>97.8</td><td>82.6</td><td>94.8</td><td>96.7</td><td>98.1</td><td>78.0</td><td>97.0</td><td>85.6</td><td>97.8</td><td>98.3</td><td>96.4</td><td>98.8</td><td>84.9</td><td>96.5</td><td>79.8</td><td>98.4</td><td>92.8</td><td>93.4</td></tr><tr><td>ML-GCN [8]</td><td>99.5</td><td>98.5</td><td>98.6</td><td>98.1</td><td>80.8</td><td>94.6</td><td>97.2</td><td>98.2</td><td>82.3</td><td>95.7</td><td>86.4</td><td>98.2</td><td>98.4</td><td>96.7</td><td>99.0</td><td>84.7</td><td>96.7</td><td>84.3</td><td>98.9</td><td>93.7</td><td>94.0</td></tr><tr><td>TSGCN [56]</td><td>98.9</td><td>98.5</td><td>96.8</td><td>97.3</td><td>87.5</td><td>94.2</td><td>97.4</td><td>97.7</td><td>84.1</td><td>92.6</td><td>89.3</td><td>98.4</td><td>98.0</td><td>96.1</td><td>98.7</td><td>84.9</td><td>96.6</td><td>87.2</td><td>98.4</td><td>93.7</td><td>94.3</td></tr><tr><td>ASL [43]</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>94.4</td></tr><tr><td>CSRA [64]</td><td>99.9</td><td>98.4</td><td>98.1</td><td>98.9</td><td>82.2</td><td>95.3</td><td>97.8</td><td>97.9</td><td>84.6</td><td>94.8</td><td>90.8</td><td>98.1</td><td>97.6</td><td>96.2</td><td>99.1</td><td>86.4</td><td>95.9</td><td>88.3</td><td>98.9</td><td>94.4</td><td>94.7</td></tr><tr><td>DATran [62]</td><td>99.9</td><td>98.7</td><td>98.6</td><td>98.4</td><td>82.6</td><td>96.0</td><td>97.7</td><td>98.6</td><td>85.0</td><td>96.2</td><td>84.7</td><td>98.5</td><td>98.2</td><td>96.9</td><td>98.8</td><td>85.0</td><td>97.9</td><td>86.7</td><td>99.2</td><td>95.2</td><td>94.6</td></tr><tr><td>C-TMS [54]</td><td>99.5</td><td>96.2</td><td>97.6</td><td>96.5</td><td>86.4</td><td>95.8</td><td>95.2</td><td>98.9</td><td>88.7</td><td>97.5</td><td>87.6</td><td>99.2</td><td>99.2</td><td>97.3</td><td>99.0</td><td>85.0</td><td>98.1</td><td>86.1</td><td>99.1</td><td>94.2</td><td>94.8</td></tr><tr><td>MambaML</td><td>99.8</td><td>98.6</td><td>97.8</td><td>98.0</td><td>82.8</td><td>96.3</td><td>98.1</td><td>98.3</td><td>84.0</td><td>96.7</td><td>88.3</td><td>98.2</td><td>98.6</td><td>96.8</td><td>99.0</td><td>87.5</td><td>96.8</td><td>89.8</td><td>99.2</td><td>95.1</td><td>95.0</td></tr></table>
+
+Comparison on Pascal VOC 2007. Pascal VOC 2007 [15] is one of the most widely used datasets in multi-label image classification task. It contains 20 labels and a total of 9,963 images, in which 5,011 images form train-val set and remaining 4,952 images constitute test set for model evaluation. Following the setting of existing works [37, 43, 66], our MambaML framework is trained on the train-val set and evaluated on the test set. Experimental results including the average precision (AP) of each label and mAP over all labels are reported in Table 3. Notably, our MambaML framework achieves state-of-the-art performance on both pre-training settings. Specifically, our MambaML shows prominent merits in recognizing potted plant and sofa, outperforming the sub-optimal results by  $1.1\%$  and  $1.5\%$  in AP. Overall, our MambaML framework implements the highest AP on nearly quarter of the label categories.
+
+# 4.3. Ablation Study
+
+In this subsection, we explore the effectiveness of key components within the MambaML framework.
+
+Effect of Key Components. We begin by examining the effectiveness of key components within the MambaML framework, specifically the Mamba-based context modeling (MCM) module and the Mamba-based semantic dependency modeling (MSDM) module in the decoder. The MCM module is designed to capture global visual contexts for each image feature, while the MSDM module focuses on modeling semantic dependencies among label representations. Both modules utilize architectures of bidirectional Mamba to achieve these objectives in a linear scanning manner. Experimental results are reported in Table 4. Clearly, both MCM and MSDM substantially improve the performance of the MambaML framework in multi-label image classification task. Absence of these modules results in a decline in mAP scores by  $0.4\%$ ,  $0.5\%$ , and  $0.7\%$  on the Pascal VOC 2007, MS-COCO, and NUS-WIDE
+
+Table 3. Experiments results on the Pascal VOC 2007 dataset in terms of class-wise precision (AP in %) and mean average precision (mAP in %). The symbol † denotes using a large input resolution (576×576).  
+
+<table><tr><td>MCM</td><td>MSDM</td><td>VOC 2007</td><td>MS-COCO</td><td>NUS-WIDE</td></tr><tr><td>×</td><td>×</td><td>94.6</td><td>85.2</td><td>65.2</td></tr><tr><td>✓</td><td>×</td><td>94.8</td><td>85.4</td><td>65.5</td></tr><tr><td>×</td><td>✓</td><td>94.8</td><td>85.5</td><td>65.6</td></tr><tr><td>✓</td><td>✓</td><td>95.0</td><td>85.7</td><td>65.9</td></tr></table>
+
+Table 4. The effects of key modules on the performance of the proposed MambaML framework (mAP in %).
+
+datasets, respectively, highlighting their crucial role in enhancing the performance of the framework on multi-label image classification. Moreover, the findings demonstrate the effectiveness of the bi-directional Mamba architecture in modeling correlations in sequence data.
+
+Effect of Mamba-based Decoder. We further investigate the effectiveness of the Mamba-based decoder, whose performance is susceptible to the number of decoder layers and hidden states within Mamba. Ablation studies on these two critical factors are illustrated in Figure 3, respectively. It is worth noting that when the number of hidden states or layers is zero, we present the performance of the backbone network. Interestingly, the performance of the MambaML framework in multi-label image classification does not benefit much from an increased number of hidden states and even deteriorate as the number of layers in the decoder grows. Nonetheless, in the initial stages, all performance curves show a marked increase, underscoring the importance of the Mamba-based decoder to the performance of the while MambaML framework in multi-label image classification. Benefiting from the exceptional capability of the Mamba-based decoder in aggregating pertinent visual information from input images to label embeddings, the MambaML framework is capable of acquiring robust label-specific visual representations, thereby achieving impressive performance in multi-label image classification.
+
+![](images/2127b46595d86a797fe866c389e90654a77cbd56ffd0951861dbae7e4ffbc580.jpg)
+
+![](images/4fd83b3c552c57e013922029b38416cff8ebe00a4931b6a7ceef7423778e555d.jpg)
+
+![](images/e24937f6ba0037ab384fd27863891f83ffdeab39f49332d45c97b6dee561867d.jpg)
+
+![](images/4c275448e9b986a7716b755eb2df485d3edc02d32fc8fe8394ede5ab64124117.jpg)  
+Figure 3. The impact of the number of hidden states and decoder layers on the performance of the MambaML framework on the Pascal VOC 2007, Microsoft COCO and NUS-WIDE datasets, respectively.
+
+![](images/1765a858527a35b6b8e228d84caeb37d3bf5cd3171a5d4e407f3208376fce8a0.jpg)
+
+![](images/f8ea325e08b245cf4d39efbe1f96486309614baf5328d106af3b8a0a3bf89a53.jpg)
+
+# 4.4. Visualization and Analysis
+
+In this subsection, we present a visualization of the attention maps within the MambaML framework to provide an in-depth analysis of its functionality in multi-label image classification. To achieve this, we randomly select several images from the MS-COCO testing set and depict their attention maps generated by MambaML using heatmaps. The visualization results are exhibited in Figure 4. It can be observed that most labels highlighted in blue, such as bed, book, chair, cat, cup person and truck, effectively concentrate on their semantically related image regions, thereby providing precise visual cues for classification and resulting in high prediction accuracies. Nevertheless, our MambaML framework also encounters challenges when dealing with small, occluded, or hidden objects of labels marked in red, such as bottle and backpack, motivating areas for potential improvement. Overall, our MambaML demonstrates proficiency in localizing and recognizing labels in most scenarios, achieving performance comparable to the state-of-the-art methods in multi-label image classification.
+
+# 5. Conclusion
+
+In this work, we propose an advanced MambaML framework for multi-label image classification. In this framework, the Mamba-based decoder leverages Mamba's inherent attention mechanism to implement cross-attention between image features and label embeddings. Together with bi-directional Mamba for scanning image features and label representations, the spatial relationships of image features, semantic dependencies among label embeddings, as well
+
+![](images/01e178ba2ef8df87e022c12982a0acb80563dbedcb15a560fc74e55edf526e70.jpg)  
+Figure 4. Visualization of attention maps in the MambaML framework on several examples from the MS-COCO testing set.
+
+as their cross-correlations are jointly explored in a unified Mamba-based architecture, producing robust label-specific visual representations for classification. Extensive experiments have demonstrated the effectiveness of our MambaML framework in multi-label image classification.
+
+Beyond a simple endeavor, we anticipate that our pioneering exploration on Mamba-based multi-label classification framework will encourage the research community to develop more effective Mamba architectures, which are expected to deliver superior classification performance while preserving commendable computational efficiency in terms of the multi-label image classification task.
+
+# Acknowledgments
+
+We thank Jiawei Ge for his insightful discussions and suggestions. This work was jointly supported by the Young Scientists Fund of the National Natural Science Foundation of China (42301520), the Research Grants Council of Hong Kong (25206524).
+
+# References
+
+[1] Ameen Ali, Itamar Zimerman, and Lior Wolf. The hidden attention of mamba models. arXiv preprint arXiv:2403.01590, 2024. 4  
+[2] Ethan Baron, Itamar Zimerman, and Lior Wolf. A 2-dimensional state space layer for spatial inductive bias. In The Twelfth International Conference on Learning Representations, 2023. 3  
+[3] Guo Chen, Yifei Huang, Jilan Xu, Baoqi Pei, Zhe Chen, Zhiqi Li, Jiahao Wang, Kunchang Li, Tong Lu, and Limin Wang. Video mamba suite: State space model as a versatile alternative for video understanding. arXiv preprint arXiv:2403.09626, 2024. 3  
+[4] Shang-Fu Chen, Yi-Chen Chen, Chih-Kuan Yeh, and Yu-Chiang Frank Wang. Order-free rnns with visual attention for multi-label classification. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018. 1, 2  
+[5] Tianshui Chen, Zhouxia Wang, Guanbin Li, and Liang Lin. Recurrent attentional reinforcement learning for multi-label image recognition. In Proceedings of the AAAI conference on artificial intelligence, 2018. 2, 7  
+[6] Tianshui Chen, Muxin Xu, Xiaolu Hui, Hefeng Wu, and Liang Lin. Learning semantic-specific graph representation for multi-label image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 522-531, 2019. 1, 2, 5, 6, 7  
+[7] Zhao-Min Chen, Xiu-Shen Wei, Xin Jin, and Yanwen Guo. Multi-label image recognition with joint class-aware map disentangling and label correlation embedding. In 2019 IEEE International Conference on Multimedia and Expo (ICME), pages 622-627. IEEE, 2019. 6  
+[8] Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, and Yanwen Guo. Multi-label image recognition with graph convolutional networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5177-5186, 2019. 1, 2, 6, 7  
+[9] Xing Cheng, Hezheng Lin, Xiangyu Wu, Dong Shen, Fan Yang, Honglin Liu, and Nian Shi. Mltr: Multi-label classification with transformer. In 2022 IEEE International Conference on Multimedia and Expo (ICME), pages 1-6. IEEE, 2022. 1, 2  
+[10] Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. Nus-wide: a real-world web image database from national university ofsingapore. In Proceedings of the ACM international conference on image and video retrieval, pages 1-9, 2009. 6  
+[11] Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the
+
+IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 702-703, 2020. 5
+
+[12] Tri Dao and Albert Gu. Transformers are ssms: Generalized models and efficient algorithms through structured state space duality. arXiv preprint arXiv:2405.21060, 2024. 3
+
+[13] Terrance DeVries and Graham W Taylor. Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552, 2017. 5
+
+[14] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. 2, 4
+
+[15] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 6, 7  
+[16] Daniel Y Fu, Tri Dao, Khaled K Saab, Armin W Thomas, Atri Rudra, and Christopher Ré. Hungry hungry hippos: Towards language modeling with state space models. arXiv preprint arXiv:2212.14052, 2022. 3  
+[17] Bin-Bin Gao and Hong-Yu Zhou. Learning to discover multi-class attentional regions for multi-label image recognition. IEEE Transactions on Image Processing, 30:5920-5932, 2021. 6  
+[18] Jiawei Ge, Jiqin Cao, Xuelin Zhu, Xinyu Zhang, Chang Liu, Kun Wang, and Bo Liu. Consistencies are all you need for semi-supervised vision-language tracking. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 1895-1904, 2024. 2  
+[19] Jiawei Ge, Jiu Xin Cao, Xiangmei Chen, Xuelin Zhu, Wei-jia Liu, Chang Liu, Kun Wang, and Bo Liu. Beyond visual cues: Synchronously exploring target-centric semantics for vision-language tracking. ACM Transactions on Multimedia Computing, Communications and Applications, 21(5):1-21, 2025. 2  
+[20] Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752, 2023. 2, 3  
+[21] Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, and Christopher Ré. Hippo: Recurrent memory with optimal polynomial projections. Advances in neural information processing systems, 33:1474-1487, 2020. 3  
+[22] Albert Gu, Karan Goel, and Christopher Ré. Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396, 2021. 2, 3  
+[23] Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré. Combining recurrent, convolutional, and continuous-time models with linear state space layers. Advances in neural information processing systems, 34:572-585, 2021. 3  
+[24] Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia. Mambair: A simple baseline for image restoration with state-space model. In European conference on computer vision, pages 222-241. Springer, 2024. 3
+
+[25] Ankit Gupta, Albert Gu, and Jonathan Berant. Diagonal state spaces are as effective as structured state spaces. Advances in Neural Information Processing Systems, 35:22982-22994, 2022. 3  
+[26] Haoyang He, Yuhu Bai, Jiangning Zhang, Qingdong He, Hongxu Chen, Zhenye Gan, Chengjie Wang, Xiangtai Li, Guanzhong Tian, and Lei Xie. Mambaad: Exploring state space models for multi-class unsupervised anomaly detection. Advances in Neural Information Processing Systems, 37:71162-71187, 2025. 3  
+[27] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 4, 5  
+[28] Shiyi He, Chang Xu, Tianyu Guo, Chao Xu, and Dacheng Tao. Reinforced multi-label image classification by exploring curriculum. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018. 7  
+[29] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997. 2  
+[30] Jiacong Hu, Anda Cao, Zunlei Feng, Shengxuming Zhang, Yi Wang, Lingxiang Jia, and Mingli Song. Vision mamba mender. Advances in Neural Information Processing Systems, 37:51905-51929, 2024. 3  
+[31] Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui, Olga Grebenkova, Pingchuan Ma, Johannes Fischer, and Bjorn Ommer. *Zigma: Zigzag mamba diffusion model.* arXiv eprints, pages arXiv-2403, 2024. 3  
+[32] Tao Huang, Xiaohuan Pei, Shan You, Fei Wang, Chen Qian, and Chang Xu. Localmamba: Visual state space model with windowed selective scan. arXiv preprint arXiv:2403.09338, 2024. 2, 3  
+[33] Jack Lanchantin, Tianlu Wang, Vicente Ordonez, and Yanjun Qi. General multi-label image classification with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16478-16488, 2021. 1, 2, 6  
+[34] Kunchang Li, Xinhao Li, Yi Wang, Yinan He, Yali Wang, Limin Wang, and Yu Qiao. Videomamba: State space model for efficient video understanding. In European Conference on Computer Vision, pages 237-255. Springer, 2024. 3  
+[35] Yining Li, Chen Huang, Chen Change Loy, and Xiaou Tang. Human attribute recognition by deep hierarchical contexts. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VI 14, pages 684-700. Springer, 2016. 1  
+[36] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 6  
+[37] Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, and Jun Zhu. Query2label: A simple transformer way to multi-label classification. arXiv preprint arXiv:2107.10834, 2021. 1, 3, 5, 6, 7  
+[38] Yongcheng Liu, Lu Sheng, Jing Shao, Junjie Yan, Shiming Xiang, and Chunhong Pan. Multi-label image classifica
+
+tion via knowledge distillation from weakly-supervised detection. In Proceedings of the 26th ACM international conference on Multimedia, pages 700-708, 2018. 6  
+[39] Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, Jianbin Jiao, and Yunfan Liu. Vmamba: Visual state space model. Advances in neural information processing systems, 37:103031-103063, 2025. 2, 3, 4  
+[40] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5  
+[41] Shentong Mo and Yapeng Tian. Scaling diffusion mamba with bidirectional ssms for efficient image and video generation. arXiv preprint arXiv:2405.15881, 2024. 3  
+[42] Eric Nguyen, Karan Goel, Albert Gu, Gordon Downs, Preey Shah, Tri Dao, Stephen Baccus, and Christopher Ré. S4nd: Modeling images and videos as multidimensional signals with state spaces. Advances in neural information processing systems, 35:2846-2861, 2022. 3  
+[43] Tal Ridnik, Emanuel Ben-Baruch, Nadav Zamir, Asaf Noy, Itamar Friedman, Matan Protter, and Lihi Zelnik-Manor. Asymmetric loss for multi-label classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 82-91, 2021. 5, 6, 7  
+[44] Jing Shao, Kai Kang, Chen Change Loy, and Xiaogang Wang. Deeply learned attributes for crowded scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4657-4666, 2015. 1  
+[45] Yuan Shi, Bin Xia, Xiaoyu Jin, Xing Wang, Tianyu Zhao, Xin Xia, Xuefeng Xiao, and Wenming Yang. Vmambair: Visual state space model for image restoration. IEEE Transactions on Circuits and Systems for Video Technology, 2025. 3  
+[46] Jimmy TH Smith, Andrew Warrington, and Scott W Linderman. Simplified state space layers for sequence modeling. arXiv preprint arXiv:2208.04933, 2022. 2  
+[47] Leslie N Smith. A disciplined approach to neural network hyper-parameters: Part 1-learning rate, batch size, momentum, and weight decay. arXiv preprint arXiv:1803.09820, 2018.5  
+[48] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing systems, pages 5998-6008, 2017. 4  
+[49] Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, and Wei Xu. Cnn-rnn: A unified framework for multi-label image classification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2285-2294, 2016. 1, 2, 6  
+[50] Meng Wang, Changzhi Luo, Richang Hong, Jinhui Tang, and Jiashi Feng. Beyond object proposals: Random crop pooling for multi-label image recognition. IEEE Transactions on Image Processing, 25(12):5678-5688, 2016. 7  
+[51] Ya Wang, Dongliang He, Fu Li, Xiang Long, Zhichao Zhou, Jinwen Ma, and Shilei Wen. Multi-label classification with label graph superimposing. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 12265-12272, 2020. 1, 2
+
+[52] Zhouxia Wang, Tianshui Chen, Guanbin Li, Ruijia Xu, and Liang Lin. Multi-label image recognition by recurrently discovering attentional regions. In Proceedings of the IEEE international conference on computer vision, pages 464-472, 2017. 2, 7  
+[53] Yanan Wu, He Liu, Songhe Feng, Yi Jin, Gengyu Lyu, and Zizhang Wu. Gm-mlic: Graph matching based multi-label image classification. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 1179-1185, 2021. 2, 6  
+[54] Yanan Wu, Songhe Feng, Gongpei Zhao, and Yi Jin. Transformer driven matching selection mechanism for multi-label image classification. IEEE Transactions on Circuits and Systems for Video Technology, 2023. 6, 7  
+[55] Yicheng Xiao, Lin Song, Jiangshan Wang, Siyu Song, Yixiao Ge, Xiu Li, Ying Shan, et al. Mambatree: Tree topology is all you need in state space model. Advances in Neural Information Processing Systems, 37:75329-75354, 2025. 2, 4  
+[56] Jiahao Xu, Hongda Tian, Zhiyong Wang, Yang Wang, Wenxiong Kang, and Fang Chen. Joint input and output space learning for multi-label image classification. IEEE Transactions on Multimedia, 23:1696-1707, 2020. 6, 7  
+[57] Chenhongyi Yang, Zehui Chen, Miguel Espinosa, Linus Ericsson, Zhenyu Wang, Jiaming Liu, and Elliot J Crowley. Plainmamba: Improving non-hierarchical mamba in visual recognition. arXiv preprint arXiv:2403.17695, 2024. 2  
+[58] Vacit Oguz Yazici, Abel Gonzalez-Garcia, Arnau Ramisa, Bartlomiej Twardowski, and Joost van de Weijer. Orderless recurrent models for multi-label classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13440-13449, 2020. 1, 2  
+[59] Jin Ye, Junjun He, Xiaojiang Peng, Wenhao Wu, and Yu Qiao. Attention-driven dynamic graph convolutional network for multi-label image recognition. In European Conference on Computer Vision, pages 649-665. Springer, 2020. 1, 2, 6  
+[60] Renchun You, Zhiyao Guo, Lei Cui, Xiang Long, Yingze Bao, and Shilei Wen. Cross-modality attention with semantic graph embedding for multi-label classification. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 12709-12716, 2020. 6  
+[61] Junjie Zhang, Qi Wu, Chunhua Shen, Jian Zhang, and Jianfeng Lu. Multilabel image classification with regional latent semantic dependencies. IEEE Transactions on Multimedia, 20(10):2801-2813, 2018. 7  
+[62] Wei Zhou, Zhijie Zheng, Tao Su, and Haifeng Hu. Datran: Dual attention transformer for multi-label image classification. IEEE Transactions on Circuits and Systems for Video Technology, 34(1):342-356, 2023. 6, 7  
+[63] Feng Zhu, Hongsheng Li, Wanli Ouyang, Nenghai Yu, and Xiaogang Wang. Learning spatial regularization with image-level supervisions for multi-label image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5513-5522, 2017. 6  
+[64] Ke Zhu and Jianxin Wu. Residual attention: A simple but effective method for multi-label recognition. In Proceedings
+
+of the IEEE/CVF International Conference on Computer Vision, pages 184-193, 2021. 6, 7  
+[65] Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision mamba: efficient visual representation learning with bidirectional state space model. In Proceedings of the 41st International Conference on Machine Learning, pages 62429-62442, 2024. 2, 3, 4  
+[66] Xuelin Zhu, Jiuxin Cao, Jiawei Ge, Weijia Liu, and Bo Liu. Two-stream transformer for multi-label image classification. In Proceedings of the 30th ACM International Conference on Multimedia, pages 3598-3607, 2022. 1, 2, 7  
+[67] Xuelin Zhu, Jian Liu, Weijia Liu, Jiawei Ge, Bo Liu, and Jiuxin Cao. Scene-aware label graph learning for multi-label image classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1473-1482, 2023. 1, 2  
+[68] Xuelin Zhu, Jianshu Li, Jieuxin Cao, Dongqi Tang, Jian Liu, and Bo Liu. Semantic-guided representation enhancement for multi-label image classification. IEEE Transactions on Circuits and Systems for Video Technology, 34(10):10036-10049, 2024. 1, 3

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+# Manual-PA: Learning 3D Part Assembly from Instruction Diagrams
+
+Jiahao Zhang<sup>1</sup> Anoop Cherian<sup>2</sup> Cristian Rodriguez<sup>3</sup> Weijian Deng<sup>1</sup> Stephen Gould<sup>1</sup>  
+<sup>1</sup>The Australian National University, <sup>2</sup>Mitsubishi Electric Research Labs  
+<sup>3</sup>The Australian Institute for Machine Learning
+
+$^{1}\{\mathrm{first.last}\} @ \mathrm{anu.edu.au}^{2}$ cherian@merl.com  $^{3}$ crodriguezop@gmail.com
+
+# Abstract
+
+Assembling furniture amounts to solving the discrete-continuous optimization task of selecting the furniture parts to assemble and estimating their connecting poses in a physically realistic manner. The problem is hampered by its combinatorially large yet sparse solution space thus making learning to assemble a challenging task for current machine learning models. In this paper, we attempt to solve this task by leveraging the assembly instructions provided in diagrammatic manuals that typically accompany the furniture parts. Our key insight is to use the cues in these diagrams to split the problem into discrete and continuous phases. Specifically, we present Manual-PA, a transformer-based instruction Manual-guided 3D Part Assembly framework that learns to semantically align 3D parts with their illustrations in the manuals using a contrastive learning backbone towards predicting the assembly order and infers the 6D pose of each part via relating it to the final furniture depicted in the manual. To validate the efficacy of our method, we conduct experiments on the benchmark PartNet dataset. Our results show that using the diagrams and the order of the parts lead to significant improvements in assembly performance against the state of the art. Further, Manual-PA demonstrates strong generalization to real-world IKEA furniture assembly on the IKEA-Manual dataset.
+
+# 1. Introduction
+
+With the rise of the DIY trend, nowadays it is the consumers who are increasingly assembling products like IKEA furniture [2, 3, 21, 57, 58], LEGO structures [47, 49], and exercise equipments [45, 61]. This paradigm shift can reduce the product cost, but often poses challenges for untrained consumers, as an incorrect assembly in any step may lead to irreparable outcomes. Thus, automating the assembly or providing active help in the process is often desired.
+
+Automated assembly faces the dual challenge of large combinatorial solution space, parameterized by the number of discrete parts in the assembly, and the need for a precise 6D pose estimation that correctly connects parts between
+
+![](images/c46dd605da9836f4abcdb5ad77eda97ba6cb4ac33d7b23009f2c84c2b253fd1d.jpg)  
+(a)
+
+![](images/1ee31adc01153b10617b0df4629a028eac2b1bba3a690de82b47ac33f648ae19.jpg)  
+(b)  
+(c)  
+Figure 1. An illustration of the manual-guided 3D part assembly task. Given (a) a diagrammatic manual book demonstrating the step-by-step assembly process and (b) a set of texture-less furniture parts, the goal is to (c) infer the order of parts for the assembly from the manual sequence and predict the 6DoF pose for each part such that the spatially transformed parts assemble the furniture described in the manual.
+
+each other. The limited number of feasible, stable assembly sequences makes this discrete-continuous optimization problem extremely challenging, even for advanced neural networks, especially as the number of parts increases [54]. Fortunately, most assembly tasks include language-agnostic instruction manuals, such as diagrams, delineating the assembly. While such manuals help shrink the solution search space, the problem of correctly matching the 2D manual diagrams with their corresponding 3D object parts and inferring their assembly pose and order remains a significant challenge for automated systems.
+
+This paper introduces a method to enable machines to learn how to assemble shapes by following a visual instruction manual, a task we call manual-guided 3D part assembly. As illustrated in Fig. 1, given a set of parts and a manual, our approach interprets the manual's step-by-step diagrams to predict the precise 6D pose of each part, assembling them into the target shape. There are two lines of prior approaches that have similarities to our proposed task, namely methods that focus on the geometry of individual parts and inter-part relationships [8, 9, 23, 56, 59, 60],
+
+and methods such as MEPNet [49] that learns the assembly in specific conditions, such as LEGO shapes. The former rely on priors like peg-hole joints [25] or sequence generation [54] to reduce search complexity but are generative and may yield unstable assemblies due to occlusions [23]. The latter often simplifies the task by assuming that parts are provided step-by-step, e.g., LEGO manuals. However, furniture manuals, such as IKEA, may not have such information, and it is necessary to determine which parts are used at each step before assembly. Additionally, while LEGO tasks feature standardized "stud" joints, general furniture assembly is more complex and lacks such constraints.
+
+Based on the above observations, we identify two key challenges in our task. First, how to detect which part or parts are added at each step in the assembly diagrams. Given that these diagrams depict incremental assembly stages, the core challenge is to recognize newly added parts at each step. This requires aligning 2D assembly diagrams with the 3D parts to establish a correct assembly sequence. Second, how to effectively align the assembly process with the learned order. A straightforward approach is to directly use the learned sequence as the order for predicting poses, e.g., by adopting an auto-regressive prediction method similar to MEPNet [49]. However, this approach carries the risk of accumulating errors—if a mistake occurs in an earlier step, subsequent steps are likely to fail as well. On the other hand, completely disregarding the sequence forces the assembly model to implicitly find correspondences, which makes both learning and prediction significantly more difficult. Therefore, the inferred sequence should serve as a soft guidance for the assembly process, helping each part focus more on the relevant step diagram.
+
+To address these challenges, we present Manual-guided Part Assembly (Manual-PA), a novel transformer-based neural network that predicts correspondences between step diagrams and parts by computing their semantic similarity, which is learned using contrastive learning. Using this similarity matrix, we establish an assembly order via solving an optimal assignment problem between the aligned features of the 3D object parts and the ordered manual diagram steps, followed by permuting each part's positional encoding according to the learned order. Finally, a transformer decoder fuses the two feature modalities to predict the final 6DoF pose for each part. Notably, the cross-attention between step diagrams and parts is guided by the positional encoding, ensuring that each step diagram receives a higher attention score for its corresponding part.
+
+To empirically validate the effectiveness of Manual-PA, we conduct experiments on the standard PartNet benchmark dataset [27], on the categories of assembling the chair, table and storage classes. Our experiments and ablation studies clearly show that Manual-PA leads to state-of-the-art results on multiple metrics, including success rate, outper
+
+forming prior methods by a significant margin. Results further showcase the strong generalizability of our approach to real-world IKEA furniture assembly through experiments using the IKEA-Manual dataset [50].
+
+We summarize our key contributions below:
+
+1. We introduce a new problem setup of assembling 3D parts using diagrammatic manuals for guidance.  
+2. We present a novel framework, Manual-PA, for solving this task that learns the assembly order of the 3D parts, which is then used as soft guidance for the assembly.  
+3. We present experiments on the benchmark PartNet and IKEA-Manual datasets demonstrating state-of-the-art performances and cross-category generalization.
+
+# 2. Related Works
+
+3D Part Assembly involves reconstructing complete shapes from sets of 3D parts, a task originally assisted by leveraging semantic part information [6, 11, 15, 43, 52, 55]. With the introduction of the PartNet dataset [27], recent works [8, 9, 28, 56, 59, 60] leverage geometric features. For instance, 3DHPA [9] uses part symmetry for a hierarchical assembly, SPAFormer [54] circumvents combinatorial explosion by generating a part order, and Joint-PA [25] introduces a peg-hole constraint to facilitate assembly. However, these methods are generative towards producing plausible final shapes without aiming for preciseness. Approaches like GPAT [24] and Image-PA [23] add guidance from point clouds or rendered images, respectively. Our work extends this direction by introducing guidance through instruction manuals, similar to how humans would do.
+
+Permutation Learning is often used in self-supervised methods for learning image representations [4, 16, 31, 35, 40]. However, in this work, we seek to align the 3D parts to instruction steps, for which we adopt a contrastive learning [14, 17, 22, 39] using a novel cross-modal alignment setup. 6DoF Object Pose Estimation methods can be classified into instance-level [36, 38, 53], category-level [44, 48], and unseen [20, 51]. Our approach relates to the unseen object category as furniture typically comprises novel parts. When the 3D models of these parts are available, many methods [7, 13, 26, 30, 34] rely on feature matching followed by the PnP algorithm to infer pose. However, IKEA-style manuals are represented as line drawings making accurate feature extraction for matching to 3D parts challenging. Alternatively, some approaches [20, 51] use end-to-end models to directly predict the 6D pose. A common approach is to use CNOS [29] for object segmentation. However, in assembly diagrams, where multiple parts are connected and depicted together, SAM-based methods [18] often struggle with accurate segmentation. Our approach addresses these challenges using an end-to-end framework that uses a cross-attention scheme to enable each part to attend to its relevant region in the step diagram.
+
+![](images/d0e8f5448eca3a7e4fa28f985b4f2c072c18d2a4cf46675a96be002251ebcbd2.jpg)  
+Figure 2. Overview of our proposed method Manual-PA. (a) Feature extraction (Sec. 3.2): we extract semantic and geometrical features from both the step diagrams of the assembly manual and the corresponding part point clouds using the image encoder and point encoder, respectively. (b) Manual-guided part permutation learning (Sec. 3.3): we compute a similarity matrix  $\mathbf{S}$  between the two modalities, and subsequently apply the Hungarian algorithm to obtain the permutation matrix  $\mathbf{P}$  for the parts. (c) Manual-guided part pose estimation (Sec. 3.4): we add positional encodings (PE)  $\Phi$  to both the step diagrams and parts, where the PE order for parts is determined by the order predictions from (b). This is followed by a transformer decoder to enable multimodal feature fusion and interaction, along with a pose prediction head to determine the rotation  $R_{i}$  and translation  $t_i$  of each part. The predicted poses are then applied to the corresponding parts to obtain the final assembled shape  $\mathbf{S}$ .
+
+# 3. Manual-Guided Part Assembly
+
+In this section, we first provide a concrete formal definition of the manual-based 3D part assembly task in Sec. 3.1. We then present a detailed explanation in Secs. 3.2 to 3.4 of the proposed learning pipeline as depicted in Fig. 2, followed by a description of the training process and the loss functions used for each stage in Sec. 3.5.
+
+# 3.1. Problem Formulation
+
+Our task involves two different input modalities: a set of 3D parts and a corresponding instruction manual. The 3D parts are represented as a multiset<sup>1</sup> of unordered point clouds, denoted by  $\{\mathcal{P}_i\}_{i=1}^N$ . Note that the number of parts  $N$  may differ from furniture to furniture. The manual consists of an ordered sequence of  $N$  step diagram images, denoted by  $(\mathcal{I}_1, \mathcal{I}_2, \ldots, \mathcal{I}_N)$ ; each diagram depicting an incremental step in the assembly process of a 3D furniture shape  $S$ . These step diagrams are 2D line drawings, which are 2D projections of textureless 3D CAD models of these parts captured from a camera  $\mathcal{C}$ , and we assume for each step, only one additional part is attached to the furniture assembled thus far. Our goal is to align the 3D parts with their
+
+delineations in the respective manual steps towards predicting the assembly order of the parts and the part poses  $\{(R_i,t_i)\in SE(3)\}_{i = 1}^N$  for the final assembled furniture, where  $R_{i},t_{i}$  represent the  $3\times 3$  rotation matrix and the three-dimensional translation vector respectively for the  $i$ -th part in the SE(3) transformation space. That is, these parts undergo rigid transformations such that their combination constructs the complete 3D furniture shape  $\mathcal{S}$  as seen from the viewpoint of camera  $\mathcal{C}$ .
+
+# 3.2. Feature Extraction
+
+We first use off-the-shelf encoders to extract semantic latent features from the inputs. For the 3D part point clouds  $\{\mathcal{P}_i\}_{i = 1}^N$ , we utilize a lightweight version of PointNet [37], similar to the one used in Image-PA [23], followed by a linear layer to map the features to a common dimension  $D$ , resulting in geometric part features  $\mathbf{f}^{\mathrm{P}}\in \mathbb{R}^{N\times D}$ . For sequences of step diagrams  $(\mathcal{I}_j)_{j = 1}^N$ , given that the assembly instructions are provided step-by-step in an incremental manner, our primary focus is on the differences between consecutive steps. For any two consecutive diagram images  $\mathcal{I}_j$  and  $\mathcal{I}_{j + 1}$ , for  $j = 1,2,\ldots ,N - 1$ , we compute the difference image given by  $|\mathcal{I}_j - \mathcal{I}_{j + 1}|$  that shows which new part is added to the thus-far assembled furniture. Next, this
+
+difference image is patched into  $K$  image patches, that are then fed to DINOv2 image encoder [5, 33] to extract features, followed by a linear layer to map these features to the same dimensionality  $D$ , resulting in semantic image features  $\mathbf{f}^{\mathrm{I}} \in \mathbb{R}^{N \times K \times D}$ .
+
+# 3.3. Manual-Guided Part Permutation Learning
+
+In our task, we seek to learn the order of a set of parts conditioned on an ordered sequence of step diagrams. This problem is similar to learning the feature correspondences between two modalities in order to align them, with an ideal alignment corresponding to a permutation matrix. Specifically, our objective is to derive a similarity matrix between the two modalities such that each modality feature is unambiguously assigned to its corresponding feature in the other modality and we desire this assignment matches with the ground truth order in the step sequence in the manual. Formally, we construct the similarity matrix  $\mathbf{S} \in \mathbb{R}^{N \times N}$  as:
+
+$$
+\mathbf {S} _ {i j} = \operatorname {s i m} \left(\mathbf {f} _ {i} ^ {\mathrm {P}}, \mathbf {g} _ {j} ^ {\mathrm {I}}\right), \tag {1}
+$$
+
+where  $\mathbf{g}^{\mathrm{I}}\in \mathbb{R}^{N\times D}$  is derived from the  $N\times K\times D$  dimensional step-diagram features  $\mathbf{f}^{\mathrm{I}}$  by performing a max-pooling operation on the patch dimension  $K$ . We measure the similarity in the feature space using the dot product as  $\operatorname {sim}(\mathbf{a},\mathbf{b}) = \mathbf{a}\cdot \mathbf{b}$
+
+Next, we obtain the permutation matrix  $\mathbf{P} \in \{0,1\}^{N \times N}$  by solving a bipartite matching optimization problem using the Hungarian matching algorithm [19] with cost  $\mathbf{C} = -\mathbf{S}$ :
+
+$$
+\text {m i n i m i z e} \quad \sum_ {i = 1} ^ {N} \sum_ {j = 1} ^ {N} \mathbf {C} _ {i j} \mathbf {P} _ {i j}
+$$
+
+$$
+\text {s u b j e c t} \quad \sum_ {j = 1} ^ {N} P _ {i j} = 1, \quad \forall i \in \{1, \dots , N \} \tag {2}
+$$
+
+$$
+\sum_ {i = 1} ^ {N} P _ {i j} = 1, \quad \forall j \in \{1, \dots , N \}
+$$
+
+$$
+P _ {i j} \in \{0, 1 \}, \quad \forall i, j \in \{1, \dots , N \},
+$$
+
+where we minimize the cost, subject to constraints ensuring  $\mathbf{P}$  is a permutation matrix, i.e., each row and column of  $\mathbf{P}$  contains exactly one entry equal to 1, with all other entries being 0. The final part order can be obtained by  $\sigma = \operatorname{argmax}(\mathbf{P})$  where  $\operatorname{argmax}$  is applied columnwise.
+
+# 3.4. Manual-Guided Part Pose Estimation
+
+Positional Encoding. Since the attention mechanism in the transformer is permutation-invariant, we follow Vaswani et al. [46] in using positional encoding to convey the order of both step diagrams and parts to the model. Formally, the positional encoding is defined as a function that maps a scalar to a sinusoidal encoding,  $\phi : \mathbb{N} \to \mathbb{R}^D$ :
+
+$$
+\phi (x) _ {2 i} = \sin \left(\frac {x}{\tau_ {p} ^ {2 i / d}}\right), \phi (x) _ {2 i - 1} = \cos \left(\frac {x}{\tau_ {p} ^ {2 i / d}}\right), \tag {3}
+$$
+
+where  $i = 1,\dots ,d / 2$  indexes the dimension and  $\tau_{p}$  is a temperature hyperparameter. By applying  $\phi$  to encode the sequential order of step diagrams as they appear in the manual (i.e., using  $x$  as a simple increasing sequence  $1,2,\ldots ,N$ ), we obtain a positional encoding  $\Phi \in \mathbb{R}^{N\times D}$ , where  $\mathbf{p}^{\mathrm{I}}\coloneqq \Phi$ . Given the permutation matrix  $\mathbf{P}$  that corresponds to the order of the parts, we can compute the positional encoding for the parts as  $\mathbf{p}^{\mathrm{P}} = \mathbf{P}^{T}\Phi$ . During training, we use the ground truth part order, whereas at inference time, we replace it with the predicted permutation matrix  $\hat{\mathbf{P}}$ , yielding  $\hat{\mathbf{p}}^{\mathrm{P}} = \hat{\mathbf{P}}^{T}\Phi$ . These positional encodings are then added to the respective features before being fed into the transformer decoder and provide soft guidance on the sequence order. We adopt RoPE [42] for better performance.
+
+Transformer Decoder. The modality interaction and fusion between step diagrams and parts occur through a stack of  $L$  transformer [46] decoder layers. In each layer, the self-attention module receives the output from the previous layer (initially, the part features  $\mathbf{f}^{\mathrm{P}} + \mathbf{p}^{\mathrm{P}}$  in the first layer) to perform part-to-part interactions. This is followed by a cross-attention module that injects and fuses the step diagram features  $\mathbf{f}^{\mathrm{I}} + \mathbf{p}^{\mathrm{I}}$  with the part features processed by the self-attention module, enabling step-to-part interactions. Notably, for the step diagram features, we concatenate the features from all diagrams from the same manual along the patch dimension, resulting in a tensor of shape  $\mathbb{R}^{NK\times D}$ .
+
+Pose Prediction Head. Finally, the output  $O_{\mathrm{Dec}}$  from the transformer decoder is fed into a pose prediction head, which predicts the translation and rotation for each part, denoted as  $(\hat{R}_i,\hat{t}_i)$ . In our implementation, the rotation matrix  $\hat{R}_i$  is represented as a unit quaternion  $\hat{q}_i\in \mathbb{R}^4$ . We initialize its bias to an identity quaternion  $(1,0,0,0)$  and normalize the prediction to ensure a unit norm.
+
+# 3.5. Training and Losses
+
+First, we train the manual-guided part order learning model until it successfully aligns the two modalities and provides an optimal permutation for the parts. Subsequently, we train the transformer decoder-based part pose estimation model using the predicted order from the first model to infer the part poses. Our empirical results indicate that having an accurate part order is crucial for enhancing the inference performance of the pose estimation network.
+
+Loss for Permutation Learning. Borrowing the notation from Secs. 3.2 and 3.3, we define the contrastive-based order loss as:
+
+$$
+\mathcal {L} _ {\text {o r d e r}} = - \frac {1}{B} \sum_ {i = 1} ^ {B} \log \frac {\exp \left(\sin \left(\mathbf {f} _ {\sigma (i)} ^ {\mathrm {P}}, \mathbf {g} _ {i} ^ {\mathrm {I}}\right) / \tau\right)}{\sum_ {j = 1} ^ {B} \exp \left(\sin \left(\mathbf {f} _ {\sigma (i)} ^ {\mathrm {P}}, \mathbf {g} _ {j} ^ {\mathrm {I}}\right) / \tau\right)}, \tag {4}
+$$
+
+where  $B$  is the mini-batch size,  $\tau$  is the temperature, and  $\sigma(i)$  denotes the index for the  $\sigma(i)$ -th part that corresponds to the  $j$ -th step according to the ground truth part order  $\sigma$ . Here, mini-batches are constructed by sampling pairs
+
+of step-diagram and part features,  $(\mathbf{f}_{\sigma(i)}^{\mathrm{P}}, \mathbf{g}_j^{\mathrm{I}})$  for optimizing the InfoNCE loss [12, 32]. Note that we randomly sample one part from their geometric-equivalent group. In this setup, pairs  $(\mathbf{f}_{\sigma(i)}^{\mathrm{P}}, \mathbf{g}_i^{\mathrm{I}})$  are considered as positive, otherwise negative. This loss encourages positive pairs to have higher similarity while negative pairs have lower similarity, allowing the similarity matrix to be used for generating the permutation matrix via Hungarian matching, as discussed in Sec. 3.3.
+
+Losses for Pose Estimation. Given a set of furniture parts, there may be subsets of parts that are geometrically identical to each other, such as the four legs of a table, the armrests on both sides of an armchair, or the support rods on the back of a chair. We denote geometrically equivalent parts as a part group  $\mathcal{G}$ . Within each group, we calculate the chamfer distance between these parts and the ground truth. Then, we apply Hungarian Matching (see Sec. 3.3) again with cost:
+
+$$
+\mathbf {C} _ {i j} = \mathrm {C D} \left(\hat {R} _ {i} \mathcal {P} _ {i} + \hat {t} _ {i}, R _ {j} \mathcal {P} _ {j} + t _ {j}\right), \tag {5}
+$$
+
+where CD denotes the chamfer distance measuring the similarity between unordered two point clouds [10],  $i$  and  $j$  index the parts within  $\mathcal{G}$ , and  $\hat{R}$  and  $\hat{t}$  denote the respective predicted rotation and translation. The optimal matching sequence is denoted as  $\mathcal{M}$ .
+
+For the 3D part assembly task, following Li et al. [23], we supervise translation and rotation separately. We compute the  $\ell_2$ -distance for translation as follows:
+
+$$
+\mathcal {L} _ {T} = \frac {1}{N} \sum_ {i = 1} ^ {N} \| \hat {t} _ {[ i ]} - t _ {i} \| _ {2}, \tag {6}
+$$
+
+where  $[i]$  denotes the index of the matched part corresponding to the  $i$ -th part under optimal matching  $\mathcal{M}$ .
+
+In addition to the geometric similarity between the parts mentioned above, each part itself may also have intrinsic symmetries. For instance, a cylindrical table leg has axial symmetry. Thus, we cannot simply use the absolute distance  $(e.g., \ell_2)$  on rotation as a supervision signal. Instead we use chamfer distance to supervise the rotation per part:
+
+$$
+\mathcal {L} _ {C} = \frac {1}{N} \sum_ {i = 1} ^ {N} \mathrm {C D} \left(\hat {R} _ {[ i ]} \mathcal {P} _ {i}, R _ {i} \mathcal {P} _ {i}\right). \tag {7}
+$$
+
+However, some parts may not be perfectly symmetric, e.g., due to small holes in different locations. To address these cases, we still encourage the model to penalize the  $\ell_2$  distance for the rotation as a regularization term, formally expressed as:
+
+$$
+\mathcal {L} _ {E} = \frac {1}{N} \sum_ {i = 1} ^ {N} \| \hat {R} _ {[ i ]} \mathcal {P} _ {i} - R _ {i} \mathcal {P} _ {i} \| _ {2}. \tag {8}
+$$
+
+Additionally, to ensure that the predicted assembled shape closely matches the ground truth, we compute the
+
+chamfer distance between the predicted and ground truth assembled shapes:
+
+$$
+\mathcal {L} _ {S} = \mathrm {C D} \left(\bigcup_ {i = 1} ^ {N} \left(\hat {R} _ {[ i ]} \mathcal {P} _ {i} + \hat {t} _ {[ i ]}\right), \bigcup_ {i = 1} ^ {N} \left(R _ {i} \mathcal {P} _ {i} + t _ {i}\right)\right), \tag {9}
+$$
+
+where  $\cup_{i=1}^{N}$  indicates the union of  $N$  parts to form the assembled shape.
+
+Finally, the overall loss for pose estimation is computed as a weighted sum of the aforementioned losses:
+
+$$
+\mathcal {L} _ {\text {p o s e}} = \lambda_ {T} \mathcal {L} _ {T} + \lambda_ {C} \mathcal {L} _ {C} + \lambda_ {E} \mathcal {L} _ {E} + \lambda_ {S} \mathcal {L} _ {S}. \tag {10}
+$$
+
+# 4. Experiments
+
+# 4.1. Datasets
+
+Following [8, 9, 23, 28, 54, 56, 59, 60], we use PartNet [27], a comprehensive 3D shape dataset with fine-grained, hierarchical part segmentation, for both training and evaluation. In our work, we adopt the train/validation/test split used by Li et al. [23], where shapes with more than 20 parts are filtered out, and we use the finest granularity, Level-3, of PartNet. We focus on the three largest furniture categories: chair, table and storage, containing 40,148, 21,517 and 9,258 parts, respectively. Besides that, to demonstrate the generalizability of our method, we also evaluate its zero-shot capabilities on the IKEA-Manual [50] dataset, which features real-world IKEA furniture at the part level that are aligned with IKEA manuals. For consistency with PartNet, we evaluate both chair category with 351 parts, and table, with 143 parts. Storage is excluded due to only 3 samples.
+
+For each individual part, we sample 1,000 points from its CAD model and normalize them to a canonical space. Geometrically-equivalent part groups are identified based on the size of their Axis-Aligned Bounding Box (AABB). To generate the assembly manual, we render diagrammatic images of parts using Blender's Freestyle functionality. More details of the preprocessing and rendering process are provided in the supplementary material.
+
+# 4.2. Evaluation Metrics
+
+To evaluate the effectiveness of 3D part assembly of different models, we employ three key metrics: Shape Chamfer Distance (SCD) [23], Part Accuracy (PA) [23], and Success Rate (SR) [24]. SCD quantifies the overall chamfer distance between predicted and ground truth shapes, providing a direct measure of assembly quality, scaled by a factor of  $10^{3}$  for interpretability. PA assesses the correctness of individual part poses by determining whether the chamfer distance for a part falls below a threshold of 0.01, reflecting the accuracy of each part's alignment. Finally, SR evaluates whether all parts in an assembled shape are accurately predicted, offering a strict criterion for complete assembly success.
+
+Table 1. 3D part assembly results on the PartNet test split and Ikea-Manual.  $\dagger$ : We re-trained the Image-PA model using diagrams (2D line drawing images) as the conditioning input instead of the original RGB images. The values in bold represent the best results, while underlined indicate the second.  $\ddagger$ : Average over five runs.  
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">Condition</td><td colspan="3">SCD↓</td><td colspan="3">PA↑</td><td colspan="3">SR↑</td></tr><tr><td>Chair</td><td>Table</td><td>Storage</td><td>Chair</td><td>Table</td><td>Storage</td><td>Chair</td><td>Table</td><td>Storage</td></tr><tr><td colspan="11">Fully-Supervised on PartNet [27]</td></tr><tr><td>DGLNIPS&#x27;20 [56]</td><td>-</td><td>9.1</td><td>5.0</td><td>-</td><td>39.00</td><td>49.51</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>IETRA-L&#x27;22 [59]</td><td>-</td><td>5.4</td><td>3.5</td><td>-</td><td>62.80</td><td>61.67</td><td></td><td>-</td><td>-</td><td>-</td></tr><tr><td>Score-PABMVC&#x27;23 [8]</td><td>-</td><td>7.4</td><td>4.5</td><td>-</td><td>42.11</td><td>51.55</td><td>-</td><td>8.320</td><td>11.23</td><td>-</td></tr><tr><td>CCSAAAI&#x27;24 [60]</td><td>-</td><td>7.0</td><td>-</td><td>-</td><td>53.59</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>3DHPACVPR&#x27;24 [9]</td><td>-</td><td>5.1</td><td>2.8</td><td>-</td><td>64.13</td><td>64.83</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>RGLWACV&#x27;22 [28]</td><td>Sequence</td><td>8.7</td><td>4.8</td><td>-</td><td>49.06</td><td>54.16</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>SPAFormerArXiv&#x27;24 [54]</td><td>Sequence</td><td>6.7</td><td>3.8</td><td>4.5</td><td>55.88</td><td>64.38</td><td>56.11</td><td>16.40</td><td>33.50</td><td>7.850</td></tr><tr><td>Joint-PACVPR&#x27;24 [25]</td><td>Joint</td><td>6.0</td><td>7.0</td><td>-</td><td>72.80</td><td>67.40</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Image-PAECCV&#x27;20 [23]</td><td>Image</td><td>6.7</td><td>3.7</td><td>5.0</td><td>45.40</td><td>71.60</td><td>40.20</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Image-PA†ECCV&#x27;20</td><td>Diagram</td><td>5.9</td><td>3.9</td><td>3.7</td><td>62.67</td><td>70.10</td><td>47.79</td><td>19.97</td><td>32.83</td><td>4.730</td></tr><tr><td>Manual-PA w/o Order</td><td>Manual</td><td>3.0</td><td>3.6</td><td>4.2</td><td>79.07</td><td>74.03</td><td>48.54</td><td>34.13</td><td>37.71</td><td>4.054</td></tr><tr><td>Manual-PA (Ours)</td><td>Manual</td><td>1.7</td><td>1.8</td><td>2.9</td><td>89.06</td><td>87.41</td><td>56.51</td><td>58.03</td><td>56.66</td><td>4.730</td></tr><tr><td>Manual-PA (Ours)</td><td>Multi-Part</td><td>3.4</td><td>3.5</td><td>3.3</td><td>84.63</td><td>78.95</td><td>53.41</td><td>38.31</td><td>40.15</td><td>5.405</td></tr><tr><td>Manual-PA (Ours)</td><td>Multi-View‡</td><td>3.6</td><td>3.2</td><td>3.0</td><td>82.04</td><td>75.49</td><td>54.37</td><td>38.31</td><td>39.96</td><td>4.054</td></tr><tr><td colspan="11">Zero-Shot on IKEA-Manual [50]</td></tr><tr><td>3DHPACVPR&#x27;24</td><td>-</td><td>34.3</td><td>37.8</td><td>-</td><td>1.914</td><td>4.027</td><td>-</td><td>0.000</td><td>0.000</td><td>-</td></tr><tr><td>Image-PA†ECCV&#x27;20</td><td>Diagram</td><td>17.3</td><td>14.7</td><td>-</td><td>19.07</td><td>36.74</td><td>-</td><td>0.000</td><td>10.53</td><td>-</td></tr><tr><td>Manual-PA w/o Order</td><td>Manual</td><td>12.8</td><td>8.9</td><td>-</td><td>38.36</td><td>42.01</td><td>-</td><td>1.754</td><td>10.53</td><td>-</td></tr><tr><td>Manual-PA (Ours)</td><td>Manual</td><td>11.4</td><td>4.8</td><td>-</td><td>42.51</td><td>49.72</td><td>-</td><td>3.509</td><td>15.79</td><td>-</td></tr></table>
+
+# 4.3. Main Results
+
+Fully Supervised on PartNet. In this setting, we train our model on PartNet for each category and test it on unseen shapes in the test split, which share similar patterns with the training set. As shown in Tab. 1, our method, Manual-PA, significantly outperforms all previous approaches in all three categories. Specifically, Manual-PA achieves superior performance compared to all other conditioned methods. Notably, when compared to Image-PA, we observe improvements of 26, 17 and 8 part accuracy for the chair, table and storage categories, respectively. This shows that for the 3D Part Assembly task, instruction manuals provide far better guidance than a single image or other conditioning inputs. "Manual-PA w/o Order" refers to our model without access to the part assembly order information, requiring it to learn this implicitly. Although the instruction manual provides strong visual guidance, performance improvements remain limited in this case, highlighting the challenges of implicit learning. By explicitly learning the correspondences between step diagrams and parts and applying positional encoding as soft guidance, we achieve PA improvements of approximately 10, 13 and 8 in the chair, table and storage categories, respectively. Note that all other methods, except for Image-PA and ours, are generative and follow the setting proposed by [56], where multiple assembly results are generated with varying Gaussian noise for the
+
+same set of parts. Only the most faithful assembly (based on Minimum Matching Distance [1]) is evaluated.
+
+Extension to More Realistic Assembly Settings. Our problem setting arises from the lack of a standardized format in real-world instruction manuals, which makes it difficult to define a universal learning framework. Our model is designed with scalability and generalizability in mind, allowing it to be extended to more realistic assembly scenarios. To demonstrate this, we implement and evaluate our framework under two challenging settings: 1) Multi-Part Assembly, where multiple new parts are introduced in a single step. In this scenario, parts are grouped based on their geometric similarity, and their positional encodings are adjusted accordingly to reflect their associations. 2) Multi-View, where each step diagram is captured from a randomly chosen viewpoint among eight predefined angles, simulating the viewpoint variations commonly observed in real manuals. To handle these variations, we modify the vision encoder to encode consecutive step diagrams separately before concatenating their features as input. As shown in Tab. 1, while these more complex settings introduce additional challenges and result in some performance degradation compared to the original setup, our method still significantly outperforms baseline approaches. These results highlight the robustness and versatility of our framework, demonstrating its ability to generalize beyond the
+
+![](images/6021ebf01050470f1082bf9b9ded031293f318874da6eb8811c4487718f495cd.jpg)  
+Figure 3. Results trained on chair and tested table and storage. Solid bars: cross-category; lighter bars: same-category. Arrows show performance drop with percentages above.
+
+single-part-per-step assumption and handle more practical assembly tasks. Further details on these experiments are provided in the supplementary material.
+
+Cross-Category Generalization. We evaluate cross-category generalization by training both Manual-PA and Image-PA on the chair category and testing on chair, table, and storage. As shown in Fig. 3, the performance drop of Manual-PA when tested on unseen categories is minimal compared to that of Image-PA, indicating stronger cross-category generalization. This result suggests that Manual-PA does not solely rely on shape priors but effectively extracts assembly-relevant information from manuals, enabling strong performance even on unseen shapes. These findings reinforce the advantage of using stepwise instruction diagrams as guidance for 3D part assembly.
+
+Zero-shot on IKEA-Manual. We use the same model, pretrained on PartNet, to directly estimate the poses of furniture in IKEA-Manual, where all shapes are 3D models of real-world IKEA furniture, and each part corresponds to illustrations in their official assembly manuals. As shown in Tab. 1, our method, significantly outperforms strong competitors such as 3DHPA and Image-PA, demonstrating the strong generalizability of our approach to real world.
+
+# 4.4. Ablation Studies
+
+Effect of the Order for 3D Part Assembly. We conduct experiments to explore various methods for incorporating order information in a 3D part assembly model, as shown in Tab. 2. First, by comparing lines 1 and 2, we observe that adding sequential information solely to part features improves assembly performance. We attribute this improvement to two factors: (1) distinguishing geometrically equivalent components, and (2) mitigating the combinato
+
+Table 2. Ablation results for various order designs on the PartNet chair test split. We use GT order for both training and inference.  
+
+<table><tr><td>#</td><td>Type</td><td>Step</td><td>Parts</td><td>SCD↓</td><td>PA↑</td><td>SR↑</td></tr><tr><td>1</td><td>PE</td><td></td><td></td><td>4.3</td><td>72.22</td><td>22.50</td></tr><tr><td>2</td><td>PE</td><td></td><td>✓</td><td>3.0</td><td>79.31</td><td>33.88</td></tr><tr><td>3</td><td>OneHot</td><td></td><td>✓</td><td>3.0</td><td>79.07</td><td>34.13</td></tr><tr><td>4</td><td>OneHot</td><td>✓</td><td>✓</td><td>2.7</td><td>81.22</td><td>36.92</td></tr><tr><td>5</td><td>PE (Ours)</td><td>✓</td><td>✓</td><td>1.7</td><td>95.38</td><td>73.07</td></tr></table>
+
+Table 3. Ablation results for various component designs in permutation learning on the PartNet chair test split. KT denotes the Kendall-tau coefficient that measure the ordinal correlation between predicted and GT order.  
+
+<table><tr><td>Variants</td><td>KT↑</td><td>SCD↓</td><td>PA↑</td><td>SR↑</td></tr><tr><td>Manual-PA (Ours)</td><td>0.789</td><td>1.7</td><td>89.06</td><td>58.03</td></tr><tr><td>w/o batch-level sampling</td><td>0.410</td><td>2.3</td><td>56.93</td><td>15.68</td></tr><tr><td>w/ Sinkhorn</td><td>0.774</td><td>1.7</td><td>88.25</td><td>56.26</td></tr><tr><td>w/ GT Order</td><td>1.000</td><td>1.7</td><td>95.38</td><td>73.07</td></tr></table>
+
+rial explosion problem, as claimed in SPAFormer [54]. Second, by comparing lines 2 and 5, where the latter provides both the step diagram and part features with correct correspondence, we achieve a significant performance boost. This indicates that explicitly establishing correspondence between steps and parts is beneficial, as learning these correspondences implicitly remains challenging for the model. Last, our empirical results show that, when position information is solely relevant to self-attention (lines 2 and 3), both PE and OneHot encodings yield similar performance. This observation aligns with prior transformer-based studies, which also utilize OneHot encodings [9, 54, 59]. However, in cases requiring cross-attention (line 4 and 5), PE outperforms OneHot encodings, highlighting its advantages in cross-attentive contexts.
+
+Analysis of Permutation Learning Choices. We conduct an ablation study for permutation learning, as shown in Tab. 3. First, we find that batch-level sampling is essential compared to applying contrastive learning individually within each manual book. This is primarily because contrastive learning benefits from a larger pool of negative samples. Second, while we employ the Sinkhorn [41] algorithm on the similarity matrix to enforce that rows and columns sum to one, empirical results indicate limited performance improvement. Last, by providing the model with the ground truth assembly order, we establish an upper performance bound for our assembly model.
+
+# 4.5. Visualization and Analysis
+
+Demonstration of Soft Guidance. We manually add different positional encodings to the feature of chair's back, as shown in Fig. 5. With PE(5), the model focuses on the final position, aligning well with the correct step diagram.
+
+![](images/d1012fae2f211719a7a5e12dad7e6eaf22b6d3345f52a182560e40f55cdb5236.jpg)  
+Figure 4. Qualitative comparison of various 3D part assembly methods. Four examples are shown: chair (a) and table (b) from the PartNet dataset, and chair (c) and table (d) from the IKEA-Manual dataset.
+
+![](images/92abb7530521214acb3eceaf903a17bfdbbd508c2fa627ef8ea52fdbd860142b.jpg)  
+Figure 5. Visualization of the cross attention scores between step diagrams (top) and a part (left, chair's back) with PE at different positions. The final assembly results are displayed on the right.
+
+However, when PE(2) is applied, drawing the model's attention to the second step diagram, the model struggles to accurately predict the chair's back pose. Notably, even with PE(0), the model manages to shift its attention toward the correct final step, demonstrating the fault tolerance and robustness of soft guidance: the model can still identify the correct match even when given an incorrect sequence.
+
+Qualitative Comparison. As shown in Fig. 4, our proposed Manual-PA produces the most faithful part pose prediction for the shapes illustrated in the step diagrams. For "Manual-PA w/o Order", some parts do not align well with the assembly instructions, indicating the importance of providing precise correspondences between the step diagram and the parts for accurate assembly. In the case of Image
+
+PA, the input consists only of the final image, making it challenging to locate the poses of parts that are occluded, such as the supporting rods beneath the table top in (b). 3DHPA lacks visual input and operates in a generative mode, relying solely on shape priors. Consequently, it generates shapes that are reasonable, as seen in (a), but there are noticeable discrepancies between its output and the ground truth shapes. For zero-shot results on the IKEA-Manual dataset, both 3DHPA and Image-PA exhibit degraded performance, while Manual-PA still produces faithful assembly results, demonstrating its superior generalization ability.
+
+# 5. Conclusions and Future Work
+
+We introduce a novel 3D part assembly framework that leverages diagrammatic manuals for guidance. Our proposed approach, Manual-PA, learns the sequential order of part assembly and uses it as a form of soft guidance for 3D part assembly. Manual-PA achieves superior performance on the PartNet dataset compared to existing methods and demonstrates strong generalizability to real-world IKEA furniture, as validated on the IKEA-Manual dataset.
+
+Future work could aim to bridge the gap between synthetic and real-world IKEA manuals. Although we have addressed adaptability in detecting a varying number of new parts introduced in each step and robust handling of differing perspectives, other visual elements such as arrows, detailed close-ups, and part labels remain challenging. Moreover, manuals often include sub-module assemblies, resulting in a tree-like, non-linear assembly structure.
+
+# References
+
+[1] Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. Learning representations and generative models for 3d point clouds. In ICML, pages 40-49. PMLR, 2018. 6  
+[2] Yizhak Ben-Shabat, Xin Yu, Fatemeh Saleh, Dylan Campbell, Cristian Rodriguez-Opazo, Hongdong Li, and Stephen Gould. The ika asm dataset: Understanding people assembling furniture through actions, objects and pose. In CVPR, pages 847-859, 2021. 1  
+[3] Yizhak Ben-Shabat, Jonathan Paul, Eviatar Segev, Oren Shrout, and Stephen Gould. *Ikea ego 3d dataset: Understanding furniture assembly actions from ego-view 3d point clouds*. In WACV, pages 4355-4364, 2024. 1  
+[4] Fabio M Carlucci, Antonio D'Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi. Domain generalization by solving jigsaw puzzles. In CVPR, pages 2229-2238, 2019. 2  
+[5] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In ICCV, 2021. 4  
+[6] Siddhartha Chaudhuri, Evangelos Kalogerakis, Leonidas Guibas, and Vladlen Koltun. Probabilistic reasoning for assembly-based 3d modeling. In SIGGRAPH, pages 1-10, 2011. 2  
+[7] Jianqiu Chen, Mingshan Sun, Tianpeng Bao, Rui Zhao, Liwei Wu, and Zhenyu He. Zeropose: Cad-model-based zero-shot pose estimation. arXiv preprint arXiv:2305.17934, 2023. 2  
+[8] Junfeng Cheng, Mingdong Wu, Ruiyuan Zhang, Guanqi Zhan, Chao Wu, and Hao Dong. Score-pa: Score-based 3d part assembly. BMVC, 2023. 1, 2, 5, 6  
+[9] Bi'an Du, Xiang Gao, Wei Hu, and Renjie Liao. Generative 3d part assembly via part-whole-hierarchy message passing. In CVPR, pages 20850-20859, 2024. 1, 2, 5, 6, 7  
+[10] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In CVPR, pages 605-613, 2017. 5  
+[11] Thomas Funkhouser, Michael Kazhdan, Philip Shilane, Patrick Min, William Kiefer, Ayellet Tal, Szymon Rusinkiewicz, and David Dobkin. Modeling by example. ACM TOG, 23(3):652-663, 2004. 2  
+[12] Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning an invariant mapping. In CVPR, pages 1735-1742. IEEE, 2006. 5  
+[13] Junwen Huang, Hao Yu, Kuan-Ting Yu, Nassir Navab, Slobodan Ilic, and Benjamin Busam. Matchu: Matching unseen objects for 6d pose estimation from rgb-d images. In CVPR, pages 10095-10105, 2024. 2  
+[14] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICML, pages 4904-4916. PMLR, 2021. 2  
+[15] Evangelos Kalogerakis, Siddhartha Chaudhuri, Daphne Koller, and Vladlen Koltun. A probabilistic model for
+
+component-based shape synthesis. ACM TOG, 31(4):1-11, 2012. 2  
+[16] Dahun Kim, Donghyeon Cho, Donggeun Yoo, and In So Kweon. Learning image representations by completing damaged jigsaw puzzles. In WACV, pages 793-802. IEEE, 2018. 2  
+[17] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-language transformer without convolution or region supervision. In ICML, pages 5583-5594. PMLR, 2021. 2  
+[18] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In ICCV, pages 4015-4026, 2023. 2  
+[19] Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83-97, 1955. 4  
+[20] Yann Labbe, Lucas Manuelli, Arsalan Mousavian, Stephen Tyree, Stan Birchfield, Jonathan Tremblay, Justin Carpentier, Mathieu Aubry, Dieter Fox, and Josef Sivic. Megapore: 6d pose estimation of novel objects via render & compare. In CoRL, 2022. 2  
+[21] Youngwoo Lee, Edward S Hu, and Joseph J Lim. Ikee furniture assembly environment for long-horizon complex manipulation tasks. In ICRA, pages 6343-6349. IEEE, 2021. 1  
+[22] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 34:9694-9705, 2021. 2  
+[23] Yichen Li, Kaichun Mo, Lin Shao, Minhyuk Sung, and Leonidas Guibas. Learning 3d part assembly from a single image. In ECCV, pages 664-682. Springer, 2020. 1, 2, 3, 5, 6  
+[24] Yulong Li, Andy Zeng, and Shuran Song. Rearrangement planning for general part assembly. In 7th Annual Conference on Robot Learning, 2023. 2, 5  
+[25] Yichen Li, Kaichun Mo, Yueqi Duan, He Wang, Jiequan Zhang, and Lin Shao. Category-level multi-part multi-joint 3d shape assembly. In CVPR, pages 3281-3291, 2024. 2, 6  
+[26] Jiehong Lin, Lihua Liu, Dekun Lu, and Kui Jia. Sam-6d: Segment anything model meets zero-shot 6d object pose estimation. In CVPR, pages 27906-27916, 2024. 2  
+[27] Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su. Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding. In CVPR, pages 909–918, 2019. 2, 5, 6  
+[28] Abhinav Narayan, Rajendra Nagar, and Shanmuganathan Raman. Rgl-net: A recurrent graph learning framework for progressive part assembly. In WACV, pages 78-87, 2022. 2, 5, 6  
+[29] Van Nguyen Nguyen, Thibault Groueix, Georgy Ponimatkin, Vincent Lepetit, and Tomas Hodan. Cnos: A strong baseline for cad-based novel object segmentation. In CVPR, pages 2134-2140, 2023. 2
+
+[30] Van Nguyen Nguyen, Thibault Groueix, Mathieu Salzmann, and Vincent Lepetit. Gigapose: Fast and robust novel object pose estimation via one correspondence. In CVPR, pages 9903-9913, 2024. 2  
+[31] Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles. In ECCV, pages 69-84. Springer, 2016. 2  
+[32] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 5  
+[33] Maxime Oquab, Timothee Darct, Theo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 4  
+[34] Evin Pinar Örnek, Yann Labbe, Bugra Tekin, Lingni Ma, Cem Keskin, Christian Forster, and Tomáš Hodač. Found- pose: Unseen object pose estimation with foundation features. ECCV, 2024. 2  
+[35] Kaiyue Pang, Yongxin Yang, Timothy M Hospedales, Tao Xiang, and Yi-Zhe Song. Solving mixed-modal jigsaw puzzle for fine-grained sketch-based image retrieval. In CVPR, pages 10347-10355, 2020. 2  
+[36] Sida Peng, Yuan Liu, Qixing Huang, Xiaowei Zhou, and Hujun Bao. Pvnet: Pixel-wise voting network for 6dof pose estimation. In CVPR, pages 4561-4570, 2019. 2  
+[37] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In CVPR, pages 652-660, 2017. 3  
+[38] Mahdi Rad and Vincent Lepetit. Bb8: A scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth. In ICCV, pages 3828-3836, 2017. 2  
+[39] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, pages 8748-8763. PMLR, 2021. 2  
+[40] Rodrigo Santa Cruz, Basura Fernando, Anoop Cherian, and Stephen Gould. Deeper: Visual permutation learning. In CVPR, pages 3949-3957, 2017. 2  
+[41] Richard Sinkhorn. Diagonal equivalence to matrices with prescribed row and column sums. The American Mathematical Monthly, 74(4):402-405, 1967. 7  
+[42] Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063, 2024. 4  
+[43] Minhyuk Sung, Hao Su, Vladimir G Kim, Siddhartha Chaudhuri, and Leonidas Guibas. Complementme: Weakly-supervised component suggestions for 3d modeling. ACM TOG, 36(6):1-12, 2017. 2  
+[44] Meng Tian, Marcelo H Ang, and Gim Hee Lee. Shape prior deformation for categorical 6d object pose and size estimation. In ECCV, pages 530-546. Springer, 2020. 2  
+[45] Yunsheng Tian, Jie Xu, Yichen Li, Jieliang Luo, Shinjiro Sueda, Hui Li, Karl DD Willis, and Wojciech Matusik. As
+
+semble them all: Physics-based planning for generalizable assembly by disassembly. ACM TOG, 41(6):1-11, 2022. 1  
+[46] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, 2017. 4  
+[47] Yuxuan Wan, Kaichen Zhou, Hao Dong, et al. Scanet: Correcting lego assembly errors with self-correct assembly network. arXiv preprint arXiv:2403.18195, 2024. 1  
+[48] He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, and Leonidas J Guibas. Normalized object coordinate space for category-level 6d object pose and size estimation. In CVPR, pages 2642-2651, 2019. 2  
+[49] Ruocheng Wang, Yunzhi Zhang, Jiayuan Mao, Chin-Yi Cheng, and Jiajun Wu. Translating a visual lego manual to a machine-executable plan. In ECCV, pages 677-694. Springer, 2022. 1, 2  
+[50] Ruocheng Wang, Yunzhi Zhang, Jiayuan Mao, Ran Zhang, Chin-Yi Cheng, and Jiajun Wu. *Ikea-manual: Seeing shape assembly step by step*. *NeurIPS*, 35:28428-28440, 2022. 2, 5, 6  
+[51] Bowen Wen, Wei Yang, Jan Kautz, and Stan Birchfield. Foundationpose: Unified 6d pose estimation and tracking of novel objects. In CVPR, pages 17868-17879, 2024. 2  
+[52] Rundi Wu, Yixin Zhuang, Kai Xu, Hao Zhang, and Baoquan Chen. Pq-net: A generative part seq2seq network for 3d shapes. In CVPR, pages 829-838, 2020. 2  
+[53] Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox. PoseCNN: A convolutional neural network for 6d object pose estimation in cluttered scenes. In Robotics: Science and Systems, 2017. 2  
+[54] Boshen Xu, Sipeng Zheng, and Qin Jin. Spaformer: Sequential 3d part assembly with transformers. arXiv preprint arXiv:2403.05874, 2024. 1, 2, 5, 6, 7  
+[55] Kangxue Yin, Zhiqin Chen, Siddhartha Chaudhuri, Matthew Fisher, Vladimir G Kim, and Hao Zhang. Coalesce: Component assembly by learning to synthesize connections. In 2020 International Conference on 3D Vision (3DV), pages 61-70. IEEE, 2020. 2  
+[56] Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas J Guibas, Hao Dong, et al. Generative 3d part assembly via dynamic graph learning. NeurIPS, 33: 6315-6326, 2020. 1, 2, 5, 6  
+[57] Jiahao Zhang, Anoop Cherian, Yanbin Liu, Yizhak Ben-Shabat, Cristian Rodriguez, and Stephen Gould. Aligning step-by-step instructional diagrams to video demonstrations. In CVPR, pages 2483-2492, 2023. 1  
+[58] Jiahao Zhang, Frederic Z Zhang, Cristian Rodriguez, Yizhak Ben-Shabat, Anoop Cherian, and Stephen Gould. Temporally grounding instructional diagrams in unconstrained videos. WACV, 2025. 1  
+[59] Rufeng Zhang, Tao Kong, Weihao Wang, Xuan Han, and Mingyu You. 3d part assembly generation with instance encoded transformer. IEEE Robotics and Automation Letters, 7(4):9051-9058, 2022. 1, 2, 5, 6, 7  
+[60] Ruiyuan Zhang, Jiaxiang Liu, Zexi Li, Hao Dong, Jie Fu, and Chao Wu. Scalable geometric fracture assembly via co-creation space among assemblers. In AAAI, pages 7269–7277, 2024. 1, 2, 5, 6
+
+[61] Xinghao Zhu, Devesh K Jha, Diego Romeres, Lingfeng Sun, Masayoshi Tomizuka, and Anoop Cherian. Multi-level reasoning for robotic assembly: From sequence inference to contact selection. In ICRA, pages 816-823. IEEE, 2024. 1

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