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CVPR/2025/Gazing Into Missteps_ Leveraging Eye-Gaze for Unsupervised Mistake Detection in Egocentric Videos of Skilled Human Activities/full.md

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+# Gazing Into Missteps: Leveraging Eye-Gaze for Unsupervised Mistake Detection in Egocentric Videos of Skilled Human Activities
+
+Michele Mazzamuto<sup>1*</sup>, Antonino Furnari<sup>1*</sup>, Yoichi Sato<sup>2</sup>, Giovanni Maria Farinella<sup>1</sup>  
+<sup>1</sup>University of Catania, Catania, Italy
+
+michele.mazzamuto@phd.unict.it, antonino.furnari@unict.it, giovanni.farinella@unict.it
+
+2The University of Tokyo, Tokyo, Japan
+
+ysato@iis.u-tokyo.ac.jp
+
+*Co-first authors.
+
+# Abstract
+
+We address the challenge of unsupervised mistake detection in egocentric video of skilled human activities through the analysis of gaze signals. While traditional methods rely on manually labeled mistakes, our approach does not require mistake annotations, hence overcoming the need of domain-specific labeled data. Based on the observation that eye movements closely follow object manipulation activities, we assess to what extent eye-gaze signals can support mistake detection, proposing to identify deviations in attention patterns measured through a gaze tracker with respect to those estimated by a gaze prediction model. Since predicting gaze in video is characterized by high uncertainty, we propose a novel gaze completion task, where eye fixations are predicted from visual observations and partial gaze trajectories, and contribute a novel gaze completion approach which explicitly models correlations between gaze information and local visual tokens. Inconsistencies between predicted and observed gaze trajectories act as an indicator to identify mistakes. Experiments highlight the effectiveness of the proposed approach in different settings, with relative gains up to  $+14\%$ ,  $+11\%$ , and  $+5\%$  in EPIC-Tent, HoloAssist and IndustReal respectively, remarkably matching results of supervised approaches without seeing any labels. We further show that gaze-based analysis is particularly useful in the presence of skilled actions, low action execution confidence, and actions requiring hand-eye coordination and object manipulation skills. Our method is ranked first on the HoloAssist Mistake Detection challenge.
+
+# 1. Introduction
+
+Smart glasses are gaining more and more popularity, with various existing products capable of supporting the user
+
+![](images/2b811f6157ff7387f73370561628fd60c541e1af3163c949e215ce63515cce7e.jpg)
+
+![](images/88423628da16cfbcc6d22c29c3842120f9e4ba5404ec38b5b7218d68dfefcd4d.jpg)
+
+![](images/53ea10e3d6e253068726746521659c59f28488c3f634f1db3c46b5ae83cd539b.jpg)
+
+![](images/0e82c833cfef49cc015ef873f109c530f6011105b66c6a6cb49a04bcff1fb6a1.jpg)
+
+![](images/229aad7cc873cea7fba49158a9d1a4199fcdfb2f6db55f30d7134f60469de5e4.jpg)  
+Figure 1. Top: gaze trajectories of a correct and wrong execution of the "place water container" action, together with gaze fixation maps averaged across many action instances. Note the higher variability exhibited by wrong executions. Bottom: (a) The proposed unsupervised mistake detection method assumes as input a video with a partial gaze trajectory on the initial part of the video. (b) A gaze completion model predicts a gaze trajectory for the remaining part of the video, conditioned on the input video and the partial trajectory. (c) A mistake is detected if the predicted trajectory is significantly different from the observed one, suggesting a deviation from the expected attention patterns.
+
+![](images/a81329599ad5c1ad15a4d2395934fdbdb37ac92e091f6c4464007b6be92022ff.jpg)  
+(a) Input
+
+![](images/6db858edc082baff826b4290066ffefa0ec900c48eecda6c1319dddc378df6c0.jpg)  
+(b) Gaze Completion
+
+![](images/be34b4f706f53ddbf18917352027ed5236a0914ca03365faf1756c2d47de061d.jpg)  
+(c) Compare with GT
+
+through Augmented Reality. In order to provide timely assistance, wearable devices should be able to identify moments in which the user makes mistakes or is confused and requires help [7]. If such instances are properly detected, the AI system can proactively offer contextual information or suggestions on how to best carry out the task at hand [42].
+
+Previous works tackled the problem of detecting mistakes from a fully supervised perspective, where mistake instances were labeled in egocentric video and machine learning algorithms were trained to discriminate between video segments of correct action executions and incorrect ones [34, 42]. Such a fully supervised approach has two main downsides: 1) It is domain-dependent, hence requiring an accurate characterization of what a mistake is, depending on the context (e.g., a mistake in a kitchen is different from
+
+a mistake in the assembly line); 2) It requires to collect and label a sufficient number of mistake instances, which may be difficult to observe and record, involve time consuming procedures, and require expert knowledge. Another class of methods [9, 33] aim to detect mistakes without relying on mistake annotations, but still requires domain-specific and costly temporal action annotations. Ideally, a wearable assistant should be able to infer when the behavioral patterns of the user deviate from the norm in order to determine if they need assistance in a scenario-independent setting, i.e., without making specific assumptions on how a mistake is defined and without requiring costly labels. To overcome the aforementioned limitations, we propose to detect mistakes in egocentric videos of human activity in an unsupervised way, learning from unlabeled video.
+
+Mistakes in task execution, in particular for tasks requiring hand-eye coordination and object manipulation skills, often involve abnormal attention patterns of the camera wearer [17, 18]. For instance, imagine a user operating a coffee machine without first adding water. As they press the brew button, they notice that no coffee is produced and start shifting their attention erratically between the cup, the water tank, and the LED indicator, deviating from the typical attention sequence of "button  $\rightarrow$  cup  $\rightarrow$  button." (See Figure 1(top)). This behavior is well-documented in psychology literature, which shows that gaze patterns are crucial for the execution of even the most repetitive daily activities (e.g., making tea) [20], and that they change in response to task complexity [30] and mistakes [29].
+
+Following these observations, we study how the analysis of eye-gaze fixations can support mistake detection in egocentric video of skilled human activities. We hence propose to learn a model of "normal" attention patterns in the form of a gaze predictor producing likely eye gaze trajectories from a video at inference time. Since gaze prediction can be governed by high uncertainty, depending on the user's goals, we propose a novel "gaze completion" task in which a model takes as input a video and a partial gaze trajectory (Figure 1(a)) and is tasked to predict a likely continuation of the partial trajectory (Figure 1(b)). Gaze completion is tackled with a novel approach based on a Gaze-Frame Correlation module which explicitly models the correlation between gaze information and each local visual token. We expect videos of correct action executions to represent normal user behavior, and hence to be characterized by predictable gaze patterns, while human behavior will deviate from normality, and gaze will be unpredictable, when a mistake is made by the user. We hence signal a mistake by comparing the predicted gaze with the ground truth eye gaze trajectory obtained through a gaze tracker (Figure 1(c)).
+
+Experiments show the effectiveness of the proposed approach, both alone, or in combination with other techniques, when compared to one-class anomaly detection
+
+methods [8, 39], and various unsupervised mistake detection baselines, with relative gains up to  $+14\%$ ,  $+11\%$ , and  $+5\%$  in the on EPIC-Tent [15], HoloAssist [42] and IndustReal [32] datasets respectively, remarkably matching the results of supervised methods without any labels in one-class settings. Our analysis also shows that gaze is most effective in the presence of complex actions, low-confidence executions, and actions requiring hand-eye coordination and object-manipulation skills.
+
+In summary, the contributions of this work are as follows: 1) We investigate for the first time the problem of unsupervised mistake detection from egocentric video of human activity and provide an initial benchmark based on three datasets. 2) We define the novel "gaze completion" task where models predict gaze trajectories from video and partial gaze inputs, and introduce an approach based on a Gaze-Frame Correlation module; 3) We propose an approach to unsupervised mistake detection leveraging gaze completion to identify instances of unpredictable gaze patterns. Experiments analyze under which conditions gaze-based analysis is most useful and show the effectiveness of the approach, in one-class and unsupervised settings.
+
+We will publicly release the code and model checkpoints to support future research.
+
+# 2. Related Work
+
+Egocentric gaze estimation Literature on gaze estimation from egocentric video is rich, with previous works investigating simultaneous gaze prediction and action recognition [5], describing gaze prediction approaches incorporating egocentric cues [21], modeling task-dependent attention transition [13], leveraging vanishing point, manipulation point, hand regions [41], introducing specific architectures [1, 19], and proposing datasets to study egocentric gaze estimation and its applications in a variety of scenarios [12, 14, 22, 32, 42]. We propose a novel gaze completion task and show its application to the problem of unsupervised mistake detection in egocentric video. Differently from previous works, we define and tackle the novel task of gaze completion, with the aim to reduce the uncertainty associated with gaze prediction.
+
+Use of gaze in egocentric vision While many previous works focused on gaze estimation from video, few works investigated the use of gaze, estimated through a dedicated gaze tracker, as an input to support downstream egocentric vision applications. Specifically, previous investigations focused on discovering object usage [3], detecting privacy-sensitive situations [38], finding attended objects [27], assisting large language models in classification tasks [16], enhancing visual tasks [44, 46], improving egocentric human motion prediction [45], and aiding natural language processing tasks [36]. We show the effectiveness of gaze in mistake detection. Our method compares gaze trajectory
+
+ries predicted from visual data with gaze estimated through a gaze tracker to identify mistakes when predictions deviate from the ground truth.
+
+Mistake Detection in Egocentric Videos Mistakes naturally occur in human activities. The ability to automatically detect them from egocentric video can be beneficial for an AR assistant to offer support. Identifying mistakes usually entails modeling procedural knowledge [4, 9, 34], skill assessment [10], action segmentation [11] or detecting forgotten actions [37]. Notably, previous works tackled the task in a supervised fashion, training models to classify an action segment as "correct" or "mistake" in manually annotated instances [34, 42]. While this approach is feasible in a closed-world scenario, it requires 1) a definition of what a mistake is, depending on the domain (e.g., kitchens vs the assembly line), 2) significant amounts of manually labeled data, which is expensive and requires expert knowledge. In this work, we tackle an unsupervised mistake detection task, in which models observe unlabeled video at training time and are tasked to detect mistakes from video at test time. Our unsupervised scheme is possible through the analysis of gaze attention patterns, which provide a supervisory signal to create a joint video-gaze model of normal behavior.
+
+Video Anomaly Detection Our research also relates to the problem of Video Anomaly Detection (VAD), which involves recognizing abnormal or anomalous events within videos [23, 39]. A line of video anomaly methods are based on one-class classification, in which models are trained on normal videos and aim to identify divergence from the norm at test time [6, 8, 24, 25, 39, 40, 43]. Notably, anomaly detection in egocentric vision remains under-explored [26]. Similar to video anomaly detection, we aim to detect mistakes by determining video segments which deviate from statistics observed at training time [8]. Differently from previous works in video anomaly detection, we ground our predictions in an egocentric gaze estimation model, which acts as a proxy for modeling normal human behavior, hence effectively achieving mistake prediction detection when anomalous behavior is observed. Moreover, we go beyond the one-class assumption and show that our method can also be used in unsupervised settings where unlabeled correct and mistake examples are included at training time.
+
+# 3. Proposed Approach
+
+# 3.1. Mistake Detection Problem Setup
+
+The mistake detection task consists in highlighting those parts of the video in which the user is making a mistake during the execution of a given activity. In our setup, at each time-step  $t$ , a model  $\Phi$  takes as input a video  $V$  observed up to time-step  $t$ ,  $V_{1:t}$ , and a 2D gaze trajectory  $T_{1:t}$  obtained with a gaze tracker, where the  $i$ -th element of the trajectory  $T_i^{(x,y)}$  is a 2D gaze fixation in frame  $V_i$ . Given this input,
+
+the model has to return a score  $s_t = \Phi(V_{1:t}, T_{1:t})$  indicating whether a mistake is happening at the current time  $t$ . In this context, high  $s_t$  scores indicate the occurrence of a mistake, while low  $s_t$  scores indicate a correct action. We can hence see the mistake detection problem as a classification task, in which timestep  $t$  is classified as a mistake if  $s_t > \theta$ , where  $\theta$  is a chosen threshold. We follow previous literature on anomaly detection [8, 39] and evaluate methods in a threshold-independent fashion by reporting the Receiver Operating Characteristics Area Under the Curve (ROC-AUC), where we consider "mistake" as the positive class<sup>1</sup>. For completeness, we also report the best  $F_1$  score achieved considering the different thresholds, as well as its related precision and recall values.
+
+# 3.2. Proposed Approach
+
+At each timestep  $t$ , we trim the input video  $V_{1:t}$  and gaze trajectory  $T_{1:t}$  to the last observed  $F$  frames, hence considering  $V_{t-F:t}$  and  $T_{t-F:t}$  as inputs to our mistake detection method. (Figure 1(a)). Our method relies on two main components: a gaze completion model (Figure 1(b)), and a scoring function (Figure 1(c)).
+
+Gaze Completion Model Figure 2 illustrates the proposed gaze completion model. The model takes as input the video  $V_{t - F:t}$  and the first half of the input gaze trajectory  $T_{t - F:t - F / 2}$  and predicts a gaze trajectory  $\hat{T}_{t - F / 2:t}$  aligned to the remaining part of the ground truth trajectory  $T_{t - F / 2:t}$ . The goal of this model is to predict where the user is looking in the video, conditioned on the initial trajectory. As we show in the experiments, the conditioning allows to reduce the uncertainty on gaze predictions and give a prior into the intention and characteristics of the user. For instance, the model can notice that the user is a novice from the partial input trajectory or get an understanding of the performed activity and adapt its prediction accordingly. We build on [19] and propose an encoder-decoder transformer-based architecture, including two approaches to condition gaze prediction on the input partial trajectory: channel fusion and correlation fusion.
+
+Model Overview The input gaze trajectory  $T_{t - F:t}$  is encoded into a stack of heatmaps  $Q$  obtained by centering a Gaussian distribution of standard deviation  $\sigma$  around the gaze points. The first half of the stack  $Q_{1:F/2}$ , corresponding to the input half trajectory  $T_{t - F:t - F/2}$ , is forwarded to the two trajectory fusion models (paths (1) and (2) in Figure 2), which inject information on the input trajectory at different semantic levels in the model. Input frames  $V_{t - F:t}$  are processed by a token embedding layer which maps them to visual tokens with a convolution as in [19]. This mod
+
+![](images/d0cd36aaf41b121e6bd512cfe318be61b829619341c7a909c433087a5aaacdff.jpg)  
+Figure 2. The model takes as input  $F$  RGB frames  $V_{t - F:t}$  and a partial 2D gaze trajectory  $T_{t - F:t - F / 2}$  on the first  $F / 2$  input frames, and outputs a predicted trajectory  $\hat{T}_{t - F / 2:t}$  from the input video, conditioned on the input trajectory. The input trajectory is encoded as a spatio-temporal heatmap  $Q$ . Trajectory and RGB inputs are fused using two strategies, channel fusion, which adds gaze heatmaps as a separate channel (1), and correlation fusion, which uses a dedicated gaze-visual correlation module (2). We follow the design of [19] and process our inputs with a transformer encoder-decoder architecture which outputs a predicted gaze heatmap  $\hat{Q}$ , supervised via a standard Kullback-Leibler divergence loss using the ground truth unobserved trajectory  $T_{t - F / 2:t}$ . The output trajectory  $\hat{T}_{t - F / 2:t}$  is recovered from  $\hat{Q}$  using a peak finding operation.
+
+ule is also responsible for mapping the input heatmaps to a single trajectory token. Input tokens are then processed by the transformer encoder, by the gaze-visual correlation module and finally by the transformer decoder to output a likely completion of the gaze in the form of heatmaps  $\hat{Q}$ , which are supervised with a standard Kullback-Leibler loss. Specifically,  $\hat{Q}$  contains  $F$  heatmaps related to the  $F$  input frames  $V_{t - F:t}$ . The final trajectory  $\hat{T}$  is obtained by finding the global maxima of the predicted gaze heatmaps. Channel Fusion The channel fusion module (Figure 2(1)) adds the heatmaps in  $Q_{1:F/2}$  to the first  $F/2$  frames of the input video,  $V_{t - F:t - F/2}$  as an additional channel. The values of this channel are set to zero for the remaining frames. This form of early fusion acts as a soft conditioning aiming to include information about the input gaze trajectory in the computation. Note that, in order to incorporate information about the relationships between haze and input frames, the model needs to learn how to compute suitable gaze representations from the additional input channel during training. Correlation Fusion This approach (Figure 2(2)) aims to fuse visual tokens with the gaze trajectory token computed by the token embedding layer. Inspired by global-local fusion originally proposed in [19], this is done in two stages. First, within the transformer encoder, where attention between all visual features and the gaze token is computed. In this stage, correlations between all visual tokens and across visual and gaze tokens are leveraged to obtain a strong representation. Second, within the dedicated gaze-visual correlation module. Here, attention is computed only between the gaze token and the visual tokens, thus learning a ded
+
+icated attention mechanism which explicitly enriches the representation of each visual token with gaze information. Note that this fusion mechanism operates both at the early (through the encoder) and mid (through the gaze-visual correlation module) levels, thus allowing to leverage low-level (co-occurrences of gaze and visual features) and more semantic (co-occurrences of gaze and semantic visual concepts) information.
+
+Scoring Function Our approach predicts the mistake confidence score  $s_t$  by comparing the predicted trajectory  $\hat{T}_{t - F / 2:t}$  with the ground truth one  $T_{t - F / 2:t}$  which is obtained by the gaze tracker of the wearable device. We explore four different ways to compare the two trajectories: Euclidean distance, Dynamic Time Warping (DTW), Heatmap, and Entropy.
+
+Euclidean Distance This method consists in accumulating the Euclidean distances computed between corresponding points in each trajectory as the score  $s_t$ :
+
+$$
+s _ {t} = \sum_ {i = F / 2 + 1} ^ {F} \| T _ {t - F + i} - \hat {T} _ {t - F + i} \|. \tag {1}
+$$
+
+Dynamic Time Warping This scoring function uses Dynamic Time Warping (DTW)[31] to measure the distance between trajectories:
+
+$$
+s _ {t} = \mathrm {D T W} (T, \hat {T}) \tag {2}
+$$
+
+Where  $\mathrm{DTW}$  returns the cost of aligning  $T$  to  $\hat{T}$  according to
+
+the DTW algorithm2.
+
+Heatmap Differently from previous functions, this approach explicitly considers the probability values predicted by the model at each location. Specifically, we evaluate the likelihood of a ground truth eye fixation  $T_{i}$  obtained by the device under the predicted heatmap  $\hat{Q}_i$ , which can be computed as:
+
+$$
+P \left(T _ {i} \mid \hat {Q}\right) = \hat {Q} _ {i} \left(T _ {i} ^ {x}, T _ {i} ^ {y}\right) \tag {3}
+$$
+
+where  $T_{i}^{x}$  and  $T_{i}^{y}$  are the coordinates of the trajectory point  $T_{i} = [T_{i}^{x}, T_{i}^{y}]$ . The score associated to the predicted trajectory  $\hat{T}$  is computed as the sum of the likelihoods of each trajectory point, considering the predicted heatmap  $\hat{Q}$ :
+
+$$
+s _ {t} = \sum_ {i = F / 2 + 1} ^ {F} P \left(T _ {t - F + i} | \hat {Q}\right). \tag {4}
+$$
+
+Entropy This is the only method which does not require ground truth gaze for computation. We consider this measure as a way to check whether mistakes are systematically characterized by uncertain gaze predictions. In this case, the score  $s_t$  is set as the mean entropy of all predicted heatmaps for a given trajectory  $\hat{T}$ . The entropy  $H$  of a single heatmap  $\hat{Q}$  is given by:
+
+$$
+s _ {t} = - \frac {1}{F / 2} \sum_ {i = F / 2 + 1} ^ {F} \sum_ {x, y} \hat {Q} _ {t - F + i} ^ {(x, y)} \log_ {2} \left(\hat {Q} _ {t - F + i} ^ {(x, y)}\right) \tag {5}
+$$
+
+$Q_{j}^{(x,y)}$  is the value at coordinates  $(x,y)$  of heatmap  $Q_{j}$ .
+
+# 4. Experiments and Results
+
+# 4.1. Datasets and Implementation Details
+
+We perform our experiments on three popular datasets.
+
+EPIC-Tent [14] includes 7 hours of egocentric video of 29 subjects wearing a head-mounded GoPro and an SMI eye tracker while assembling a camping tent. The dataset includes egocentric video, gaze and labels indicating video segments in which users make mistakes. Subjects also rated their level of confidence in action execution in each clip in the videos. EPIC-Tent contains 151,689 mistake frames and 384,558 frames of correct executions, hence with a 28:72 ratio between correct and mistake frames. Since no official train-test split is available, we randomly split videos in training, validation and test sets roughly following a 60:15:25 ratio, obtaining 86,099,27,613, and 37,977 mistake frames in the training, validation, and test sets respectively. Since the dataset contains a single video per subject, there is no subject overlap between the three sets.
+
+IndustReal [32] is designed for studying procedural tasks in industrial-like environments and consists of distinct training, validation, and test sets. The training set comprises
+
+78,902 frames, with  $95.68\%$  frames labeled as correct and  $4.32\%$  labeled as mistakes. The validation set includes 38,036 frames, with  $95.18\%$  correct and  $4.82\%$  mistaken frames. The test set contains 90,105 frames, with  $92.53\%$  correct and  $7.47\%$  mistaken frames.
+
+HoloAssist [42] focuses on a variety of scenarios in which users perform tasks with the assistance of an expert. The training set comprises 11,614,033 frames, with  $94\%$  frames labeled as correct and  $6\%$  labeled as mistakes. The test set contains 1,699,562 frames, with  $95\%$  correct and  $5\%$  mistake frames.
+
+Implementation Details We process input frames with a stride of 1 and set the batch size to 4 clips of 8 frames each. Weight decay is set to 0.07 to prevent overfitting. See the supplementary material for more details.
+
+# 4.2. Supervision Levels and Compared Approaches
+
+We compare the proposed approach to methods belonging to three different supervision levels: fully supervised, one-class classification, and unsupervised. All baselines described below are compared with a random baseline which assigns a random score to each input clip.
+
+Fully Supervised Methods are trained assuming the availability of mistake labels for all image frames. We consider this class of methods to provide an upper-bound to performance when assessing one-class and unsupervised methods. We consider two approaches in this class: a TimeSformer [2] action recognition model which classifies the input video clips without access to any temporal context from actions executed before or after the current one, and a C2F [35] temporal action segmentation model operating on DINOv2 [28] features, which naturally performs action segmentation taking into account the temporal context in which an action (or mistake) is executed.
+
+One-Class Classification Methods are trained only on videos of correct executions, following the standard setup of anomaly detection [8, 39]. In this context, we assume that the data is verified by an expert for correctness before being used for training. Note that this check does not require marking the temporal occurrence of mistakes, but only discarding any video which contains mistakes. For this class, we compare our method with respect to TrajREC [39] and MoCoDAD [8], two popular approaches for video anomaly detection based on the processing of human skeletal data. Since full human skeletons are not visible in egocentric videos, we replace skeletal data with hand joint keypoints<sup>3</sup>. We also adapt TrajREC and MoCoDAD to take a single gaze point instead of, or in addition to, the hand keypoints to assess the ability of such methods to leverage
+
+<table><tr><td></td><td>Scoring</td><td>Fusion</td><td>F1</td><td>Precision</td><td>Recall</td><td>AUC</td></tr><tr><td>1</td><td>Random</td><td>//</td><td>0.36</td><td>0.29</td><td>0.42</td><td>0.51</td></tr><tr><td>2</td><td>Entropy</td><td>//</td><td>0.41</td><td>0.27</td><td>0.62</td><td>0.51</td></tr><tr><td>3</td><td>Euclidean</td><td>//</td><td>0.42</td><td>0.29</td><td>0.60</td><td>0.55</td></tr><tr><td>4</td><td>DTW</td><td>//</td><td>0.44</td><td>0.31</td><td>0.68</td><td>0.56</td></tr><tr><td>5</td><td>Heatmap</td><td>//</td><td>0.45</td><td>0.32</td><td>0.70</td><td>0.57</td></tr><tr><td>6</td><td>Heatmap</td><td>CH</td><td>0.45</td><td>0.32</td><td>0.74</td><td>0.63</td></tr><tr><td>7</td><td>Heatmap</td><td>CORR</td><td>0.50</td><td>0.36</td><td>0.82</td><td>0.65</td></tr><tr><td>8</td><td>Heatmap</td><td>CH + CORR</td><td>0.51</td><td>0.36</td><td>0.85</td><td>0.69</td></tr></table>
+
+eye-gaze information<sup>4</sup>. We compare these models to an instantiation of the proposed approach in which the gaze completion model is trained only on correct executions, hence effectively replicating a one-class scheme. We also compare with respect to a baseline which replaces the proposed gaze completion module with a simple gaze prediction component based on GLC [19]. Following the one-class setup, we train the GLC method of [19] on correct executions only and compare the predicted and ground truth gaze using the considered scoring functions.
+
+Unsupervised Methods assume no knowledge of which examples are correct executions and which are mistakes. Hence, models are trained on a natural mix of correct and incorrect action executions. This is the least constrained case in which the collected data is not verified by an expert prior to training. We compare our model with TrajREC and MoCoDAD adapted as discussed above and with the gaze-prediction baseline GLC [19].
+
+# 4.3. Performance of Proposed Model and Ablations
+
+Table 1 reports the performance of the proposed approach in unsupervised settings, evaluating the considered scoring functions and gaze-video fusion strategies in the unsupervised mistake detection settings on EPIC-Tent. Rows 2-5 compare scoring functions when both fusion strategies are turned off and the model is not conditioned on previous trajectories. The entropy scoring function achieved an AUC of 0.51 and an F1 score of 0.41 (row 2), only marginally above the random baseline (F1 of 0.36), suggesting that high entropy in the predictions marginally correlates with the presence of a mistake. Alternative scoring functions improved the results, yielding an AUC of 0.55 and 0.56 when using Euclidean distance and DTW scoring functions, respectively (rows 3-4). The heatmap-based scoring function produced the best results, with an AUC of 0.57 and an F1 score of 0.45, significantly above random level (compare with the F1 score of 0.36 in row 1). The advantage of the heatmap scoring function is likely due to the better
+
+Table 1. Ablation of various scoring functions and fusion strategies on EPIC-Tent in the unsupervised setting. Best results per-block are underlined, while best global results are in bold. CH: channel fusion, CORR: correlation fusion.  
+
+<table><tr><td>Method</td><td>Sup. Level</td><td>F1</td><td>Precision</td><td>Recall</td><td>AUC</td></tr><tr><td>Random</td><td>//</td><td>0.36</td><td>0.29</td><td>0.42</td><td>0.51</td></tr><tr><td>TimeSformer [2]</td><td>Fully Supervised</td><td>0.49</td><td>0.35</td><td>0.80</td><td>0.67</td></tr><tr><td>C2F [35]</td><td>Fully Supervised</td><td>0.58</td><td>0.44</td><td>0.85</td><td>0.72</td></tr><tr><td>TrajREC (G) [39]</td><td>One-Class</td><td>0.40</td><td>0.26</td><td>0.88</td><td>0.51</td></tr><tr><td>MoCoDAD (G) [8]</td><td>One-Class</td><td>0.43</td><td>0.27</td><td>0.91</td><td>0.50</td></tr><tr><td>TrajREC (H) [39]</td><td>One-Class</td><td>0.44</td><td>0.31</td><td>0.76</td><td>0.55</td></tr><tr><td>MoCoDAD (H) [8]</td><td>One-Class</td><td>0.46</td><td>0.33</td><td>0.79</td><td>0.60</td></tr><tr><td>TrajREC (H+G) [39]</td><td>One-Class</td><td>0.42</td><td>0.29</td><td>0.75</td><td>0.53</td></tr><tr><td>MoCoDAD (H+G) [8]</td><td>One-Class</td><td>0.43</td><td>0.30</td><td>0.77</td><td>0.56</td></tr><tr><td>TrajREC (H+G)* [39]</td><td>One-Class</td><td>0.47</td><td>0.34</td><td>0.77</td><td>0.63</td></tr><tr><td>MoCoDAD (H+G)* [8]</td><td>One-Class</td><td>0.49</td><td>0.35</td><td>0.81</td><td>0.65</td></tr><tr><td>GLC [19]</td><td>One-Class</td><td>0.46</td><td>0.37</td><td>0.62</td><td>0.66</td></tr><tr><td>Ours</td><td>One-Class</td><td>0.52</td><td>0.37</td><td>0.85</td><td>0.69</td></tr><tr><td>Ours + MoCoDAD (H)*</td><td>One-Class</td><td>0.54</td><td>0.41</td><td>0.86</td><td>0.72</td></tr><tr><td>TrajREC (G) [39]</td><td>Unsupervised</td><td>0.27</td><td>0.16</td><td>0.94</td><td>0.50</td></tr><tr><td>MoCoDAD (G) [8]</td><td>Unsupervised</td><td>0.33</td><td>0.21</td><td>0.88</td><td>0.51</td></tr><tr><td>TrajREC (H) [39]</td><td>Unsupervised</td><td>0.40</td><td>0.27</td><td>0.79</td><td>0.58</td></tr><tr><td>MoCoDAD (H) [8]</td><td>Unsupervised</td><td>0.41</td><td>0.27</td><td>0.86</td><td>0.60</td></tr><tr><td>MoCoDAD (H+G)* [8]</td><td>Unsupervised</td><td>0.41</td><td>0.27</td><td>0.88</td><td>0.60</td></tr><tr><td>GLC [19]</td><td>Unsupervised</td><td>0.44</td><td>0.33</td><td>0.70</td><td>0.61</td></tr><tr><td>Ours</td><td>Unsupervised</td><td>0.51</td><td>0.36</td><td>0.85</td><td>0.69</td></tr><tr><td>Ours + MoCoDAD (H)*</td><td>Unsupervised</td><td>0.52</td><td>0.37</td><td>0.88</td><td>0.70</td></tr></table>
+
+* Late fusion
+
+Table 2. Mistake detection results on EPIC-Tent. Best results are in bold, second best results are underlined.
+
+exploitation of the probability values computed by the gaze prediction model, as compared to other scoring functions. Hence, we adopted the heatmap-based scoring as our primary method in following comparisons.
+
+Rows 6-7 compare approaches using one of the two fusion strategies. While both fusion strategies improve results (compare rows 6-7 with 5), the proposed correlation strategy (CORR) systematically outperforms channel fusion, obtaining an AUC score of 0.65 and an F1 score of 0.50 and doubling the recall of the random baseline (0.82 vs 0.42) with better precision (0.36 vs 0.29). Combining the two fusion strategies (row 8) leads to an AUC of 0.69 and an F1 score of 0.51 (+0.12 and +0.06 compared to the standard heatmap method - row 5). This configuration is the one referred to as "ours" in future comparisons. $^{5}$
+
+# 4.4. Comparison with the state of the art
+
+EPIC-Tent Table 2 compares the proposed mistake detection approach with competitors on EPIC-Tent, according to the three considered levels of supervision. C2F outperforms TimeSformer in all evaluated metrics, particularly in the F1 score (0.58 of C2F vs 0.49 of TimeSformer and 0.36 of the random baseline), suggesting that C2F is more adept at capturing temporal reasoning, which is crucial for identifying mistakes in dynamic activities. However, the reliance on fully labeled datasets poses limitations for both methods. One-class methods for anomaly detection adapted to take only gaze as input, namely, TrajREC (G) and MoCoDAD (G), show minor improvements over the random baseline in terms of AUC score (0.50 - 0.51 vs 0.51) and only small
+
+improvements in F1 score (0.40 - 0.43 vs 0.36). High recall values, paired with low precision, suggest that these methods tend to classify most clips as mistakes. Incorporating hand skeleton data instead of gaze, namely, TrajREC (H) and MoCoDAD (H), leads to slight improvements, as evidenced by F1 scores of 0.44 and 0.45, and AUC values of 0.55 and 0.60. Combining (H) and (G) models through late fusion (denoted with *) improves the results, with MoCoDAD  $(\mathrm{H} + \mathrm{G})^{*}$  achieving an F1 score of 0.49 and an AUC of 0.65, suggesting that the signals captured by gaze-based and hand-based analyses are complementary. The one-class approach based on GLC [19] gaze prediction shows improved results compared to previous one-class methods, being only slightly less effective than the late fused method, despite not analyzing any hand-based information. This highlights the value of leveraging gaze analysis for mistake detection.
+
+Finally, the proposed method based on gaze completion obtains the best results, yielding an AUC of 0.69, an F1-score of 0.52, a precision of 0.37, and a recall of 0.85, which amount to relative improvements of  $+5 / 13\%^6$  with respect to the best approach GLC and  $+35 / 44\%$  with respect to the random baseline. Late-fusing our approach with MoCoDAD (H) achieves enhanced results with an AUC of 0.72 and an F1 score of 0.54, improving over compared approaches, suggesting that gaze analysis can further benefit from integration with approaches based on different cues. It is worth noting that our best method remarkably achieves the same AUC score of 0.72 as the best supervised approach and a comparable  $F1$  score (0.54 vs 0.58) without access to labels during training. We compare unsupervised approaches in the bottom part of Table 2. Similarly to the one-class case, TrajREC (H) and MoCoDAD (H) slightly improve over the random baseline (e.g., 0.40 and 0.41 vs 0.36 of the random baseline in F1 score). GLC outperforms these former two methods obtaining an F1 score of 0.44 and an AUC score of 0.61, which are lower than the scores of 0.46 and 0.66 obtained in the one-class setting. In these settings, the proposed method achieves an F1 score of 0.51, which is comparable to the score obtained in one-class settings 0.52 with similar and recall values and the same AUC of 0.69, despite the unsupervised setting being more challenging. Late fusion with MoCoDAD (H) brings some additional improvements, with an F1 score of 0.52 and AUC of 0.70.
+
+Table 3 compares the proposed method with competitors on HoloAssist, which presents a more varied and expansive context than EPIC-Tent, making mistake detection more challenging. The random baseline achieves an F1 score of only 0.04. One-class methods like TrajREC and MoCoDAD improve over the baseline but tend to classify most clips as mistakes, with high recall values (0.96 and 0.94). Hand keypoint-based methods show improvements, with
+
+<table><tr><td>Method</td><td>Sup. Level</td><td>F1</td><td>Precision</td><td>Recall</td><td>AUC</td></tr><tr><td>Random</td><td>//</td><td>0.04</td><td>0.02</td><td>0.39</td><td>0.50</td></tr><tr><td>TimeSformer [2]</td><td>Fully Supervised</td><td>0.21</td><td>0.35</td><td>0.13</td><td>0.58</td></tr><tr><td>C2F [35]</td><td>Fully Supervised</td><td>0.38</td><td>0.37</td><td>0.40</td><td>0.65</td></tr><tr><td>TrajREC (G) [39]</td><td>One-Class</td><td>0.09</td><td>0.04</td><td>0.96</td><td>0.50</td></tr><tr><td>MoCoDAD (G) [8]</td><td>One-Class</td><td>0.11</td><td>0.06</td><td>0.94</td><td>0.51</td></tr><tr><td>TrajREC (H) [39]</td><td>One-Class</td><td>0.19</td><td>0.11</td><td>0.72</td><td>0.56</td></tr><tr><td>MoCoDAD (H) [8]</td><td>One-Class</td><td>0.17</td><td>0.10</td><td>0.71</td><td>0.55</td></tr><tr><td>TrajREC (H+G) [39]</td><td>One-Class</td><td>0.13</td><td>0.07</td><td>0.68</td><td>0.52</td></tr><tr><td>MoCoDAD (H+G) [8]</td><td>One-Class</td><td>0.14</td><td>0.08</td><td>0.62</td><td>0.52</td></tr><tr><td>TrajREC (H+G)* [39]</td><td>One-Class</td><td>0.20</td><td>0.12</td><td>0.71</td><td>0.56</td></tr><tr><td>MoCoDAD (H+G)* [8]</td><td>One-Class</td><td>0.21</td><td>0.12</td><td>0.75</td><td>0.57</td></tr><tr><td>GLC [19]</td><td>One-Class</td><td>0.19</td><td>0.11</td><td>0.56</td><td>0.60</td></tr><tr><td>Ours</td><td>One-Class</td><td>0.22</td><td>0.14</td><td>0.59</td><td>0.61</td></tr><tr><td>Ours + MoCoDAD (H)*</td><td>One-Class</td><td>0.26</td><td>0.16</td><td>0.73</td><td>0.63</td></tr><tr><td>TrajREC (G) [39]</td><td>Unsupervised</td><td>0.05</td><td>0.03</td><td>0.92</td><td>0.50</td></tr><tr><td>MoCoDAD (G) [8]</td><td>Unsupervised</td><td>0.07</td><td>0.04</td><td>0.92</td><td>0.50</td></tr><tr><td>TrajREC (H) [39]</td><td>Unsupervised</td><td>0.11</td><td>0.07</td><td>0.32</td><td>0.56</td></tr><tr><td>MoCoDAD (H) [8]</td><td>Unsupervised</td><td>0.14</td><td>0.10</td><td>0.25</td><td>0.55</td></tr><tr><td>MoCoDAD (H+G)* [8]</td><td>Unsupervised</td><td>0.15</td><td>0.11</td><td>0.25</td><td>0.56</td></tr><tr><td>GLC [19]</td><td>Unsupervised</td><td>0.10</td><td>0.06</td><td>0.34</td><td>0.54</td></tr><tr><td>Ours</td><td>Unsupervised</td><td>0.18</td><td>0.12</td><td>0.40</td><td>0.59</td></tr><tr><td>Ours + MoCoDAD (H)*</td><td>Unsupervised</td><td>0.21</td><td>0.15</td><td>0.40</td><td>0.60</td></tr></table>
+
+* Late fusion
+
+Table 3. Mistake detection result on HoloAssist.
+
+TrajREC (H) slightly outperforming MoCoDAD (H). GLC demonstrates better AUC and F1 scores, with more balanced precision and recall metrics. Our approach achieves an AUC of 0.61 and an F1 score of 0.22 in one-class settings, with the best results from late fusion with MoCoDAD, yielding an F1 score of 0.26 and an AUC of 0.63. In the unsupervised scenario, TrajREC (G) and MoCoDAD (G) show limited effectiveness, while the (H) approaches perform slightly better. Our method achieves an AUC of 0.59 and an F1 score of 0.18, showing robustness across evaluation settings and relative improvements over GLC, with gains of  $+9\%$  and  $+80\%$ , respectively. Combining with MoCoDAD (H) further enhances performance.
+
+Results on IndustReal, shown in Table 4, confirm the trends observed in HoloAssist. TrajREC and MoCoDAD bring small improvements over the random baseline in H, G, and  $\mathrm{H + G}$  configurations. Our method outperforms competitors with improvements over GLC of  $+5\%$  in AUC and  $+14\%$  in F1 in one-class settings, and  $+6\%$  in AUC in unsupervised settings, while late fusion with MoCoDAD (H) does not improve performance due to MoCoDAD's reduced effectiveness in this scenario.
+
+# 4.5. Contribution of gaze across scenarios
+
+In this section, we analyze the performance of our method with respect to scenarios in order to assess under which conditions gaze analysis is more or less predictive of mistakes. Action Complexity We investigated how action complexity correlates with gaze-predicted mistakes in procedural tasks. We asked 40 volunteers to rate the complexity of performing actions contained in HoloAssist without looking (1 = easy, 5 = difficult). We then compared the complexity of the action associated to a given video segment to the ability
+
+<table><tr><td>Method</td><td>Sup. Level</td><td>F1</td><td>Precision</td><td>Recall</td><td>AUC</td></tr><tr><td>Random</td><td>//</td><td>0.12</td><td>0.06</td><td>0.62</td><td>0.51</td></tr><tr><td>TimeSformer [2]</td><td>Fully Supervised</td><td>0.20</td><td>0.12</td><td>0.35</td><td>0.58</td></tr><tr><td>C2F [35]</td><td>Fully Supervised</td><td>0.31</td><td>0.29</td><td>0.31</td><td>0.67</td></tr><tr><td>TrajRE(G) [39]</td><td>One-Class</td><td>0.17</td><td>0.09</td><td>0.90</td><td>0.53</td></tr><tr><td>MoCoDAD(G) [8]</td><td>One-Class</td><td>0.18</td><td>0.10</td><td>0.91</td><td>0.55</td></tr><tr><td>TrajREC(H) [39]</td><td>One-Class</td><td>0.21</td><td>0.12</td><td>0.88</td><td>0.57</td></tr><tr><td>MoCoDAD(H) [8]</td><td>One-Class</td><td>0.22</td><td>0.13</td><td>0.81</td><td>0.60</td></tr><tr><td>TrajREC(H+G) [39]</td><td>One-Class</td><td>0.18</td><td>0.10</td><td>0.86</td><td>0.55</td></tr><tr><td>MoCoDAD(H+G) [8]</td><td>One-Class</td><td>0.19</td><td>0.11</td><td>0.79</td><td>0.58</td></tr><tr><td>TrajREC(H+G)* [39]</td><td>One-Class</td><td>0.21</td><td>0.12</td><td>0.88</td><td>0.58</td></tr><tr><td>MoCoDAD(H+G)* [8]</td><td>One-Class</td><td>0.22</td><td>0.13</td><td>0.82</td><td>0.61</td></tr><tr><td>GLC [19]</td><td>One-Class</td><td>0.21</td><td>0.15</td><td>0.33</td><td>0.60</td></tr><tr><td>Ours</td><td>One-Class</td><td>0.24</td><td>0.18</td><td>0.35</td><td>0.63</td></tr><tr><td>Ours + MoCoDAD (H)*</td><td>One-Class</td><td>0.26</td><td>0.17</td><td>0.60</td><td>0.65</td></tr><tr><td>TrajREC (G) [39]</td><td>Unsupervised</td><td>0.11</td><td>0.06</td><td>0.92</td><td>0.51</td></tr><tr><td>MoCoDAD (G) [8]</td><td>Unsupervised</td><td>0.11</td><td>0.06</td><td>0.92</td><td>0.51</td></tr><tr><td>TrajREC (H) [39]</td><td>Unsupervised</td><td>0.15</td><td>0.11</td><td>0.28</td><td>0.55</td></tr><tr><td>MoCoDAD (H) [8]</td><td>Unsupervised</td><td>0.16</td><td>0.12</td><td>0.29</td><td>0.57</td></tr><tr><td>MoCoDAD (H+G)* [8]</td><td>Unsupervised</td><td>0.17</td><td>0.12</td><td>0.30</td><td>0.57</td></tr><tr><td>GLC [19]</td><td>Unsupervised</td><td>0.21</td><td>0.15</td><td>0.33</td><td>0.58</td></tr><tr><td>Ours</td><td>Unsupervised</td><td>0.21</td><td>0.16</td><td>0.33</td><td>0.62</td></tr><tr><td>Ours + MoCoDAD (H)*</td><td>Unsupervised</td><td>0.20</td><td>0.15</td><td>0.32</td><td>0.61</td></tr></table>
+
+* Late fusion
+
+Table 4. Mistake detection result on IndustReal.
+
+of our model to make a correct prediction (which we term "success"). Results (see Figure 3a) showed a positive correlation between difficulty and prediction success, measured with a Point Biserial Correlation of 0.3843, with  $p < 0.05^7$ . This suggests that our method is particularly effective in the case of complex actions which cannot be carried out without looking, while less effective in the case of trivial tasks.
+
+Confidence Level On the EPIC-Tent dataset, we compared if the self-rated confidence score reported by camera wearers was correlated to the success of our method. Results (see Figure 3b) obtained a Point Biserial Correlation of -0.1137,  $p < 0.05$  indicating a small but significant negative correlation: our method is most effective when the self-rated confidence is higher. This suggests that gaze-based analysis is more effective in the case of novices, which reported lower confidence and probably rely more on visual observations when executing their tasks.
+
+Action Type We finally assess whether the type of the performed action affects the performance of our method. To this aim, we grouped actions contained in all three datasets in four categories (Hand-Eye Coordination, Object Manipulation, Task Preparation, Inspection/Verification) $^{8}$ . We hence computed the number of co-ocurrences between success or failure of our method and the different action classes. Results (see Figure 4) show a Cramer's V statistic of 0.27 (a moderate correlation of 0.27 in a  $0 - 1$  scale) with a p-value  $p < 0.05$ . Gaze-based analysis proves particularly useful in the case of actions requiring hand-eye coordination and object manipulation abilities, while less effective
+
+![](images/e5f3c971d3b69084ec2c9d6bc0d3444b19600ca5032cffafe9ccfb9fe2f4311f.jpg)  
+(a)
+
+![](images/f7b1186e8243bdc2e94bdbb2a47538d4f9aee1ab066e8da7c0914dd5a7ebdb2b.jpg)  
+(b)
+
+![](images/fbf016b1e60f7931fd9ecf02146d2faca3fda6bef54c8c9fb1eabdf7156d23a3.jpg)  
+Figure 3. Distributions of difficulty ratings (a) and execution confidence ratings (b) with respect to wrong and correct predictions.  
+Figure 4. Distributions of Correct/Wrong pred. by action type.
+
+for generic actions such as task preparation and inspection.[9]
+
+# 5. Conclusion
+
+We proposed to perform mistake detection in egocentric videos in an unsupervised way, leveraging gaze signals. We introduced a novel gaze completion task, where gaze trajectories are predicted based on observed video and partial gaze data, and an approach to tackle this task. Mistake detection is performed comparing predicted trajectories with ground truth, identifying instances where gaze becomes unpredictable as potential mistakes. Experimental validation on EPIC-Tent, HoloAssist, and IndustReal demonstrates the efficacy of our method, surpassing traditional one-class techniques and other unsupervised mistake detection methods. Our method is ranked first on the HoloAssist Mistake Detection challenge. Code will be publicly shared.
+
+# Acknowledgments
+
+This research has been supported by the project Future Artificial Intelligence Research (FAIR) - PNRR MUR Cod. PE0000013 - CUP: E63C22001940006.
+
+# References
+
+[1] Mohammad Al-Naser, Shoaib Ahmed Siddiqui, Hiroki Ohashi, Sheraz Ahmed, Nakamura Katsuyki, Sato Takuto, and Andreas Dengel. Ogaze: Gaze prediction in egocentric videos for attentional object selection. In 2019 Digital Image Computing: Techniques and Applications (DICTA), pages 1-8. IEEE, 2019. 2  
+[2] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding?, 2021. 5, 6, 7, 8  
+[3] Dima Damen, Teesid Leelasawassuk, Osian Haines, Andrew Calway, and W. Mayol-Cuevas. You-do, i-learn: Discovering task relevant objects and their modes of interaction from multi-user egocentric video. In *British Machine Vision Conference*, 2014. 2  
+[4] Guodong Ding, Fadime Sener, Shugao Ma, and Angela Yao. Every mistake counts in assembly. ArXiv, abs/2307.16453, 2023. 3  
+[5] Alireza Fathi, Yin Li, and James M Rehg. Learning to recognize daily actions using gaze. In Computer Vision-ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I 12, pages 314-327. Springer, 2012. 2  
+[6] Jianfeng Feng, Fa-Ting Hong, and Weishi Zheng. Mist: Multiple instance self-training framework for video anomaly detection. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14004-14013, 2021. 3  
+[7] Shijia Feng, Michael Wray, Brian Sullivan, Youngkyoon Jang, Casimir J. H. Ludwig, Iain Gilchrist, and Walterio W. Mayol-Cuevas. Are you struggling? dataset and baselines for struggle determination in assembly videos. ArXiv, abs/2402.11057, 2024. 1  
+[8] Alessandro Flaborea, Luca Collorone, Guido Maria D'Amely di Melendugno, Stefano D'Arrigo, Bardh Prenkaj, and Fabio Galasso. Multimodal motion conditioned diffusion model for skeleton-based video anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 10318-10329, 2023. 2, 3, 5, 6, 7, 8  
+[9] Alessandro Flaborea, Guido Maria D'Amely di Melendugno, Leonardo Plini, Luca Scofano, Edoardo De Matteis, Antonino Furnari, Giovanni Maria Farinella, and Fabio Galasso. Prego: online mistake detection in procedural egocentric videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18483-18492, 2024. 2, 3  
+[10] Yixin Gao, S Swaroop Vedula, Carol E Reiley, Narges Ah-midi, Balakrishnan Varadarajan, Henry C Lin, Lingling Tao, Luca Zappella, Benjamin Béjar, David D Yuh, et al. Jhu-isi gesture and skill assessment working set (jigsaws): A surgical activity dataset for human motion modeling. In MICCAI workshop: M2cai, 2014. 3  
+[11] Reza Ghoddosian, Isht Dwivedi, Nakul Agarwal, and Behzad Dariush. Weakly-supervised action segmentation and unseen error detection in anomalous instructional videos. In
+
+Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 10128-10138, 2023. 3  
+[12] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Christian Fuegen, Abraham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4d: Around the World in 3,000 Hours of Egocentric Video. In IEEE/CVF Computer Vision and Pattern Recognition (CVPR), 2022. 2  
+[13] Yifei Huang, Minjie Cai, Zhenqiang Li, and Yoichi Sato. Predicting gaze in egocentric video by learning task-dependent attention transition. ArXiv, abs/1803.09125, 2018. 2  
+[14] Youngkyoon Jang, Brian Sullivan, Casimir Ludwig, Iain D. Gilchrist, Dima Damen, and Walterio Mayol-Cuevas. Epicent: An egocentric video dataset for camping tent assembly. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pages 4461-4469, 2019. 2, 5  
+[15] Youngkyoon Jang, Brian Sullivan, Casimir Ludwig, Iain D. Gilchrist, Dima Damen, and Walterio Mayol-Cuevas. Epicent: An egocentric video dataset for camping tent assembly. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pages 4461-4469, 2019. 2  
+[16] Robert Konrad, Nitish Padmanaban, J. Gabriel Buckmaster, Kevin C. Boyle, and Gordon Wetzstein. Gazegpt: Augmenting human capabilities using gaze-contingent contextual ai for smart eyewear, 2024. 2  
+[17] Fatemeh Koochaki and Laleh Najafizadeh. Predicting intention through eye gaze patterns. 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), pages 1-4, 2018. 2  
+[18] Kyle Krafka, Aditya Khosla, Petr Kellnhofer, Harini Kannan, Suchendra M. Bhandarkar, Wojciech Matusik, and Antonio Torralba. Eye tracking for everyone. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2176-2184, 2016. 2  
+[19] Bolin Lai, Miao Liu, Fiona Ryan, and James Rehg. In the eye of transformer: Global-local correlation for egocentric
+
+gaze estimation. British Machine Vision Conference, 2022. 2, 3, 4, 6, 7, 8  
+[20] M. Land, Neil Mennie, and Jennifer Rusted. The roles of vision and eye movements in the control of activities of daily living. Perception, 28:1311-28, 1999. 2  
+[21] Yin Li, Alireza Fathi, and James M Rehg. Learning to predict gaze in egocentric video. In Proceedings of the IEEE international conference on computer vision, pages 3216-3223, 2013. 2  
+[22] Yin Li, Miao Liu, and James M. Rehg. In the eye of beholder: Joint learning of gaze and actions in first person video. In Proceedings of the European Conference on Computer Vision (ECCV), 2018. 2  
+[23] Zhian Liu, Yongwei Nie, Chengjiang Long, Qing Zhang, and Guiqing Li. A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction. In Proceedings of the IEEE International Conference on Computer Vision, 2021. 3  
+[24] Cewu Lu, Jianping Shi, and Jiaya Jia. Abnormal event detection at 150 fps in matlab. In 2013 IEEE International Conference on Computer Vision, pages 2720-2727, 2013. 3  
+[25] Hui Lv, Zhongqi Yue, Qianru Sun, Bin Luo, Zhen Cui, and Hanwang Zhang. Unbiased multiple instance learning for weakly supervised video anomaly detection. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8022-8031, 2023. 3  
+[26] Mana Masuda, Ryo Hachiuma, Ryo Fujii, and Hideo Saito. Unsupervised Anomaly Detection of the First Person in Gait from an Egocentric Camera, page 604-617. Springer-Verlag, Berlin, Heidelberg, 2020. 3  
+[27] Michele Mazzamuto, Francesco Ragusa, Antonino Furnari, and Giovanni Maria Farinella. Learning to detect attended objects in cultural sites with gaze signals and weak object supervision. J. Comput. Cult. Herit., 2024. 2  
+[28] Maxime Oquab, Timothee Darcet, 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. 5  
+[29] Candace E. Peacock, Ben Lafreniere, Ting Zhang, Stephanie Santosa, Hrvoje Benko, and Tanya R. Jonker. Gaze as an indicator of input recognition errors. Proc. ACM Hum. Comput. Interact., 6(ETRA), 2022. 2  
+[30] Jeff Pelz and Roxanne Canosa. Oculomotor behavior and perceptual strategies in complex tasks. Vision research, 41: 3587-96, 2001. 2  
+[31] Hiroaki Sakoe. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26:159-165, 1978. 4  
+[32] Tim J Schoonbeek, Tim Houben, Hans Onvlee, Fons van der Sommen, et al. Industrial: A dataset for procedure step recognition handling execution errors in egocentric videos in an industrial-like setting. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 4365-4374, 2024. 2, 5
+
+[33] Luigi Seminara, Giovanni Maria Farinella, and Antonino Furnari. Differentiable task graph learning: Procedural activity representation and online mistake detection from egocentric videos. In Advances in Neural Information Processing Systems, 2024. 2  
+[34] Fadime Sener, Dibyadip Chatterjee, Daniel Shelepov, Kun He, Dipika Singhania, Robert Wang, and Angela Yao. Assembly101: A large-scale multi-view video dataset for understanding procedural activities. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21096-21106, 2022. 1, 3  
+[35] Dipika Singhania, Rahul Rahaman, and Angela Yao. C2f- tcn: A framework for semi-and fully-supervised temporal action segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10):11484-11501, 2023. 5,6,7,8  
+[36] Ekta Sood, Simon Tannert, Philipp Mueller, and Andreas Bulling. Improving natural language processing tasks with human gaze-guided neural attention. In Advances in Neural Information Processing Systems, 2020. 2  
+[37] Bilge Soran, Ali Farhadi, and Linda Shapiro. Generating notifications for missing actions: Don't forget to turn the lights off! In 2015 IEEE International Conference on Computer Vision (ICCV), pages 4669-4677, 2015. 3  
+[38] Julian Steil, Marion Koelle, Wilko Heuten, Susanne Boll, and Andreas Bulling. Privaceye: privacy-preserving head-mounted eye tracking using egocentric scene image and eye movement features. In Proceedings of the 11th ACM symposium on eye tracking research & applications, pages 1-10, 2019. 2  
+[39] Alexandros Stergiou, Brent De Weerdt, and Nikos Deligiannis. Holistic representation learning for multitask trajectory anomaly detection. In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024. 2, 3, 5, 6, 7, 8  
+[40] Waqas Sultani, Chen Chen, and Mubarak Shah. Real-world anomaly detection in surveillance videos. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 3  
+[41] Hamed Rezazadegan Tavakoli, Esa Rahtu, Juho Kannala, and Ali Borji. Digging deeper into egocentric gaze prediction. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 273-282, 2019. 2  
+[42] Xin Wang, Taein Kwon, Mahdi Rad, Bowen Pan, Ishani Chakraborty, Sean Andrist, Dan Bohus, Ashley Feniello, Bugra Tekin, Felipe Vieira Frujeri, Neel Joshi, and Marc Pollefeys. Holoassist: an egocentric human interaction dataset for interactive ai assistants in the real world. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 20270-20281, 2023. 1, 2, 3, 5  
+[43] Dan Xu, Elisa Ricci, Yan Yan, Jingkuan Song, and N. Sebe. Learning deep representations of appearance and motion for anomalous event detection. In *British Machine Vision Conference*, 2015. 3  
+[44] Yushi Yao, Chang Ye, Junfeng He, and Gamaleldin F. El-sayed. Teacher-generated spatial-attention labels boost robustness and accuracy of contrastive models. In 2023
+
+IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 23282-23291, 2023. 2  
+[45] Yang Zheng, Yanchao Yang, Kaichun Mo, Jiaman Li, Tao Yu, Yebin Liu, Karen Liu, and Leonidas J. Guibas. Gimo: Gaze-informed human motion prediction in context. In European Conference on Computer Vision, 2022. 2  
+[46] Yuchen Zhou, Linkai Liu, and Chao Gou. Learning from observer gaze: zero-shot attention prediction oriented by human-object interaction recognition, 2024. 2

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+# Gazing at Rewards: Eye Movements as a Lens into Human and AI Decision-Making in Hybrid Visual Foraging
+
+Bo Wang $^{1,2,3}$
+
+Dingwei Tan $^{1,2,4}$
+
+Yen-Ling Kuo
+
+Zhaowei Sun<sup>3</sup>
+
+Jeremy M. Wolfe $^{6,7}$
+
+Tat-Jen Cham
+
+Mengmi Zhang $^{1,2}$
+
+Address correspondence to mengmi.zhang@ntu.edu.sg
+
+<sup>1</sup>College of Computing and Data Science, Nanyang Technological University, Singapore
+
+$^{2}$ Deep NeuroCognition Lab, I2R and CFAR, Agency for Science, Technology and Research, Singapore
+
+<sup>3</sup>Harbin Institute of Technology, Harbin, China <sup>4</sup>Beijing Institute of Technology, Beijing, China
+
+<sup>5</sup>University of Virginia, USA <sup>6</sup>Brigham and Women's Hospital, USA <sup>7</sup>Harvard Medical School, USA
+
+# Abstract
+
+Imagine searching a collection of coins for quarters (0.25), dimes (0.10), nickels (0.05), and pennies (0.01)—a hybrid foraging task where observers look for multiple instances of multiple target types. In such tasks, how do target values and their prevalence influence foraging and eye movement behaviors (e.g., should you prioritize rare quarters or common nickels)? To explore this, we conducted human psychophysics experiments, revealing that humans are proficient reward foragers. Their eye fixations are drawn to regions with higher average rewards, fixation durations are longer on more valuable targets, and their cumulative rewards exceed chance, approaching the upper bound of optimal foragers. To probe these decision-making processes of humans, we developed a transformer-based Visual Forager (VF) model trained via reinforcement learning. Our VF model takes a series of targets, their corresponding values, and the search image as inputs, processes the images using foveated vision, and produces a sequence of eye movements along with decisions on whether to collect each fixated item. Our model outperforms all baselines, achieves cumulative rewards comparable to those of humans, and approximates human foraging behavior in eye movements and foraging biases within time-limited environments. Furthermore, stress tests on out-of-distribution tasks with novel targets, unseen values, and varying set sizes demonstrate the VF model's effective generalization. Our work offers valuable insights into the relationship between eye movements and decision-making, with our model serving as a powerful tool for further exploration of this connection. All data, code, and models are available at https://github.com/ZhangLab-DeepNeuroCogLab/visual-forager.
+
+# 1. Introduction
+
+![](images/5b8174584f725b8b8d6c11f79ab2d0c843ffe5ab4cc7be7a9e903e3070177f01.jpg)
+
+Counting down timer
+
+![](images/72354118862f531b94e75094cf3c457ea23bcf251613dc4ebcf0a9e5ec993721.jpg)
+
+Accumulative reward
+
+![](images/ee48eb6a74d0c229330a0b6196f4fe2ea26da38ce5f2bd607013aac80f0f3e32.jpg)
+
+Quarter ($0.25)
+
+![](images/368aaa1803058b9589b86e94d53de9ff554f8a67e5e4e4c5312d82294bb7e50a.jpg)
+
+Dime ($0.1)
+
+![](images/66baef91b0bcddad0b65fd21d4bd3d765731a54839b5f3887e5628e8ff9a893e.jpg)  
+Figure 1. Illustrative example of eye movements and decision-making in a hybrid visual foraging task. The image depicts a real-world scenario where the goal is to search piles of coins for multiple instances of target coins with varying monetary values in order to maximize the accumulative monetary reward, within a time-limited environment. Yellow dots and arrows represent the locations and order of eye movements during the search. Red bounding boxes show the target coins that are collected. Note that humans do not always collect every item they fixate on, highlighting the selective nature of the foraging process.
+
+Hybrid visual foraging is a ubiquitous challenge in our daily life, such as grocery shopping for a list of items, simultaneously scanning for traffic lights, parking spaces, and restaurants while driving, or looking for a specific amount of change among piles of coins (Fig. 1). These tasks involve searching for multiple instances of various target types stored in memory, where target values and prevalence can vary, and the exact number of target instances is often unknown. This raises a critical question about how to prioritize target selections during the search process. Understanding these dynamics is essential for optimizing search efficiency and decision-making in complex environments.
+
+To tackle this question, eye movements could offer a
+
+unique window into the underlying perceptual, cognitive, and evaluative processes involved in decision-making, such as sensory evidence sampling and accumulation [78, 87, 106, 140, 146], decision timing and temporal expectation [6, 10, 110, 120, 126], response inhibitions [24, 48, 65, 85, 88], and decision certainty and confidence [9, 23, 63, 96, 110], offering high temporal and spatial resolution [46, 49, 75, 103, 119]. In hybrid visual foraging, while neuroscience and psychology works [80, 133, 137-139] have primarily examined the sequence of target selections within the same environment and the timing of search transitions across different environments especially when target values and prevalence vary, there is a notable lack of studies focusing on eye movements. Here, we design and conduct human psychophysics experiments to examine how foraging strategies and eye movements are influenced by the prevalence and value of targets.
+
+Alongside studies in psychology and neuroscience, many AI models have been developed to predict eye movements during decision-making tasks, including visual search [2, 36, 51, 60, 86, 121, 128, 145], object recognition and detection [8, 91, 99, 130], and visual question answering [18, 59, 61]. Notably, existing visual search models integrate both bottom-up saliency [2, 36, 60] and top-down feature modulations [51, 86, 145]. However, these models assume idealized scenarios where either a single target type is present or multiple target types have equal values. As a result, they often overlook the need to prioritize target selections based on varying target prevalences and values during the search process. In this work, we introduce a computational model called Visual Forager (VF), a transformer-based architecture trained with reinforcement learning, designed to perform hybrid visual foraging efficiently across varying combinations of target prevalence and values. Unlike prior visual search models [19, 27, 122, 142], which often rely on human data for supervised training, our VF approximates human foraging behaviors and biases, despite zero training on human data. We highlight our key contributions:
+
+1. Drawing from psychology, we introduce hybrid visual foraging tasks for AI models. The predicted eye movements offer a unique window into the decision-making process with high spatial and temporal resolution.  
+2. We propose an AI model, Visual Forager (VF), for hybrid visual foraging tasks. VF uses actor-critic networks with a vision transformer backbone and integrates feature-based and value-based modulations to guide decision-making processes, determining where to fixate next and whether to collect currently fixated items during foraging tasks.  
+3. To benchmark AI model performances, we design and conduct human eye-tracking experiments for hybrid visual foraging tasks. Despite no training on human data, our VF achieves cumulative rewards comparable to human
+
+participants and approximates their foraging behaviors, including eye movements and decision biases toward highly valued and prevalent targets.
+
+4. Humans can flexibly adapt their foraging strategies to maximize total rewards under varying target values and prevalence. Remarkably, our VF also performs efficient foraging, under out-of-distribution conditions it was never trained on. This capability is attributed to our newly introduced data augmentations applied to target values.
+
+# 2. Related Work
+
+Eye movements as a window into decision making. Many psychology and neuroscience works have been using eye movements as a unique and non-invasive means [38, 41, 42, 62, 69, 75, 76, 79, 95, 101, 104, 105, 116, 117] to reflect how efficiently we interpret decision instructions [71, 140], the timing and duration of decision formation [6, 10, 110, 120, 126], expected rewards [43, 112, 125, 131, 143], decision accuracy [110], and our confidence in the outcome [9, 23, 63, 96]. Although multiple studies analyze the decision making in hybrid visual foraging tasks [15, 17, 35, 100, 144], there is a lack of research specifically examining eye movements and their relationships to decision making. To close this gap, we design and conduct eye-tracking experiments to investigate eye movements and their interactions with foraging behaviors.
+
+Computational models for goal-directed visual search. Eye movement in visual search is typically guided by five primary factors [136]: bottom-up saliency [66, 124, 135], top-down feature guidance [30, 34, 84], scene properties [12, 57, 123], prior history [32, 70, 132], and item values [3, 4, 82]. A range of computational models have been proposed to model human eye movements in each of these aspects during visual search [2, 13, 16, 21, 22, 44, 47, 51, 60, 67, 68, 74, 77, 86, 94, 118, 127-129, 145]. Recent deep learning models, such as [31, 40, 50, 51, 93, 141, 145, 146], have demonstrated success in searching for a single target in complex, naturalistic scenes by predicting a sequence of eye fixations and their durations. However, these models fail to look for multiple instances of multiple target types based on varying values and prevalence. To close this gap, we introduce Visual Forager (VF), capable of searching for multiple instances across different target types. Moreover, while models like GazeFormer [93], OAT [40], and IRL [141], rely on human data for training and struggle to generalize to unseen targets or unseen combinations of values and prevalence in out-of-distribution scenarios, VF can flexibly adapt its foraging strategy under these conditions.
+
+Deep reinforcement learning (RL) for value-guided decision-making. Deep RL models [1, 37, 52, 90, 92, 102, 109] have been applied to a wide range of decision-making
+
+![](images/09e7c9b7b8958f8fa6a77d788fc2872dcdf2c5b4550f6babb9ddc25882d84c67.jpg)  
+Figure 2. Schematic of the hybrid visual foraging experiment. Each foraging trial starts with a 2-second center fixation (omitted here for simplicity), followed by the presentation of target images and their associated values (e.g., a plant valued at 4). To ensure human participants memorize the targets and their values, they must pass a recognition test by selecting all targets among distractors and correctly matching their values. If they make errors, they repeat the target and value presentation phases. After another 2-second center fixation presentation, an object array is displayed. Both human and AI agents are tasked with collecting as many targets as possible through mouse clicks to maximize their total rewards, where rewards correspond to the values of the target objects, and a penalty of -1 is incurred for clicking on distractors. The trial ends either after 30 seconds or when 20 clicks are made.
+
+tasks, including video game playing [81, 89, 113], robotic control [5, 55, 107], and resource management [20, 83, 147]. These models learn to make sequential decisions by interacting with environments and receiving feedback through immediate or long-term reward [7, 26, 29, 39, 72]. Despite advancements in these decision-making models, they have not been specifically designed to model eye movements in hybrid foraging tasks. Here, we introduce a transformer-based network to learn optimal foraging and eye movement policies. This is in contrast to [54, 99, 111], where a transformer-based architecture is only used to extract visual features prior to decision-making.
+
+# 3. Hybrid visual foraging
+
+# 3.1. Human psychophysics experiments
+
+We conducted an in-lab psychophysics experiment for hybrid visual foraging, schematically illustrated in Fig. 2. The items were randomly arranged on a  $16 \times 16$  grid, containing either 90, 105, or 120 items, with  $20\%$  to  $30\%$  of the squares filled with target instances. In each foraging trial, subjects searched for  $N \in \{1, 2, 4\}$  target objects, each with a varying number of target instances. Targets and distractors in the search arrays were randomly selected from a pool of 2,400 unique items in [14]. A total of 15 subjects were recruited, yielding 750 trials, containing 50514 eye fixations and 12851 mouse clicks. All the experiments are conducted with the subjects' informed consent and according to the protocols approved by the Institutional Review Board of our institution. See Appendix Sec. S1.1 for more details.
+
+# 3.2. Foraging environments for AI models
+
+Humans have accumulated years of visual experiences, making them efficient zero-shot visual searchers without requiring prior training for hybrid visual foraging [138]. In contrast, AI models require task-specific training. Hence, we introduced procedural generation of diverse foraging environments, subsequently used to train AI models. As demonstrated in the human psychophysics experiments described above, the procedural generation of a foraging trial depends on several key experimental parameters: the total number of items on the object arrays within a  $16 \times 16$ -sized grid, the number of target objects on the search arrays, the prevalence of target instances for each target object, the values assigned to the target objects, and the selection of target and distractor items from a pool of 2,400 unique items. It is evident that the decision-making processes of both humans and AI models may be influenced by any of these experimental parameters. We sub-sample various combinations of these experimental parameters for the procedural generation of foraging environments: the total number of items on the search array is fixed at 105. A fixed set of 4 items is randomly selected as targets with their values set at 2, 4, 8, and 16 and their prevalence randomly determined. See Appendix Sec. S1.2 for details.
+
+To benchmark AI model performance in hybrid foraging tasks, we introduce two in-domain hybrid foraging conditions that align with the distribution of the training environments that the AI models were optimized to solve. (1) In-domain Uneven Value, Equal Prevalence (UnValEqPre): the prevalence of all 4 targets is equal but their values vary. (2) In-domain Uneven Value, Unequal Prevalence (UnValUnPre): both target prevalence and values vary, with low-value targets being more common and high-value targets scarcer. To assess whether the AI models can generalize to out-of-distribution (OOD) hybrid visual search tasks, where experimental parameters differ from those encountered during training, we introduced five OOD conditions. (1) OOD - Even Value, UnEqual Prevalence (EqValUnPre): the 4 target types have the equal values with varying prevalence. (2) OOD - Unseen target objects (UTargets) The target images are unseen during testing. (3) OOD - Unseen value combinations (UVvalues) The target values exceeded the range used for training, with their values changing in either arithmetic or geometric series. (4) OOD - Unseen total item numbers (UItemNum): The total number of items on the search grid differs from the one used for training. (5) OOD - Unseen target object sizes (USetSize) The set size of target objects is manipulated. See Appendix Sec. S1.3 for details of each condition.
+
+Comparisons of foraging environments between humans and AIs. For a fair comparison between human and AI performance, we categorized the foraging trials from human psychophysics experiments into the same seven
+
+conditions above used for testing the AI models. We apply identical rewards and penalties to humans and all AI models for training and evaluation. Moreover, to interact with the foraging environments, both humans and AI models must decide where to move their eye fixations next and whether to click on the current item. In human psychophysics experiments, each trial ends either after 30 seconds or once 20 clicks are made. Unlike humans, AI models do not experience time delays in eye movements or motor responses for mouse clicks. To simulate these time constraints and allow for a fair comparison between the fixation and click sequences of AI models and humans, we impose constant time costs on the AI models: 776 milliseconds for one click and 336 milliseconds for one fixation. These time costs are calculated through linear regression based on human response data (Appendix Fig. S1 and Fig. S2).
+
+# 4. Our proposed Visual Forager (VF)
+
+We propose the Visual Forager (VF), a computational model for hybrid visual foraging (Fig. 3). At the current  $t$ th fixation location  $F_{t}$ , the model takes as input the search image  $I_{S}$  and  $N$  target images  $I_{T}^{1:N}$  with their corresponding target values  $V^{1:N}$ , processes them with foveated vision, and modulates the decision making with visual features of target images and their corresponding values. Our VF outputs the predicted  $t + 1$ th fixation location  $F_{t+1}$  and the mouse-click policies for the currently fixated item. VF comprises three modules: target feature modulation, target value modulation, and decision-making.
+
+# 4.1. Target feature modulation
+
+Our VF processes the  $N$  input target images  $I_T^{1:N}$  with a feed-forward convolution neural network (2D-CNN) to extract their feature maps  $\phi_T^{1:N}$  at the last convolution layer. We used the VGG16 network [114], pre-trained on ImageNet [28] as the backbone of the feature extractor and froze its weights during training of hybrid foraging tasks.
+
+Receptive field sizes in the visual cortex increase progressively across brain areas [45]. This scaling is modeled in current visual recognition systems through eccentricity-dependent pooling operations [51]. Unlike uniform sampling in standard max pooling, receptive field size in eccentricity-dependent pooling layers grows with increasing distance from the current fixation  $F_{t}$ . Following [51], we replace VGG16's standard max pooling layers  $l_{10}, l_{14}$ , and  $l_{18}$  with eccentricity-dependent pooling. We replicated the eccentricity-dependent pooling from [51], aligning it with neurophysiological recordings in macaque monkeys. See Sec. S1.4 for the detailed implementation of eccentricity-dependent pooling. This modified feature extractor produces feature maps  $\phi_{S,t}$  for the search image  $\mathcal{I}_S$  from the last convolutional layer, given the fixation
+
+location  $F_{t}$ . For simplification of mathematical notations, we omit the subscript  $t$  in  $\phi_{S,t}$  for the rest of the text, unless specified. We use pre-trained ImageNet weights for VGG16 and freeze them for semantic feature extraction. Since pooling layers lack learnable weights, freezing the pre-trained weights of eccentricity-dependent VGG16 does not affect feature extraction quality. Note that we do not use eccentricity-dependent VGG16 to process feature maps of input target images  $\mathcal{I}_T^{1:N}$  as these target images are often small and they can be processed in high resolution within the foveated region.
+
+Visual search or feature-based attention is often modulated by the visual features of the targets [51, 145, 146]. Similar to [145], we implement the attentional modulation by computing the similarity between the features  $\phi_T^i$  of the  $i$ th target image  $I_T^i$  and the features  $\phi_S$  of the search image  $I_S$ :  $M_F^i = \mathcal{M}(\phi_T^i, \phi_S)$ , where  $\mathcal{M}(\cdot)$  is the 2D convolution of stride 2 with  $\phi_T^i$  serving as a convolution kernel applied to  $\phi_S$ . We repeat the same attentional modulation for each target image, resulting in  $N$  similarity maps  $M_F \in \mathbb{R}^{H \times W \times N}$ , where  $H \times W = 16 \times 16$  denotes the grid size of the object arrays on  $I_S$ .
+
+# 4.2. Target value modulation
+
+Values have proven to be a strong modulator of guidance in visual search [3]. To incorporate target values in our VF, we introduce a value encoder  $\mathcal{E}_V(\cdot)$  consisting of two fully connected layers and a ReLU activation layer in between.  $\mathcal{E}_V$  takes  $N$  target values  $V^{1:N} \in \mathbb{R}^N$  as input and produces a  $D$ -dimensional value embedding  $\mathcal{E}_V(V^{1:N})$  that encodes the target values. To embed  $E_V$  into  $M_F$ , we also introduce a 2D-CNN encoder  $\mathcal{E}_F(\cdot)$  to extract feature maps  $\mathcal{E}_F(M_F)$  of size  $H \times W \times D$  from the similarity maps  $M_F$ . The encoder comprises convolution blocks with ReLU activations, using  $1 \times 1$  convolution kernels with stride 1 to maintain the spatial resolution of  $M_F$ . No pooling layers are used. These convolution blocks facilitate feature fusion across all  $N$  similarity maps within each location but not across  $H \times W$  locations.
+
+Similar to the way that positional embeddings are added to each feature vector of the image patches in the vision transformer (ViT) [33], we duplicate the value embedding over all  $H \times W$  locations and perform element-wise summation  $\oplus$  on  $\mathcal{E}_F(M_F)$ :
+
+$$
+M _ {V} = \mathcal {E} _ {F} \left(M _ {F}\right) \oplus \text {d u p l i c a t e} \left(\mathcal {E} _ {V} \left(V ^ {1: N}\right)\right) \tag {1}
+$$
+
+This results in the value-modulated feature maps  $M_V \in \mathbb{R}^{H \times W \times D}$ , allowing the model to adapt the target similarity features based on target values. Note that the value-modulated feature maps  $M_V$  is fixation-dependent, since  $\phi_S$  changes based on the current fixation  $F_t$ . Due to math simplifications, we omit the subscript  $t$  for  $M_V$ .
+
+![](images/8641355b4c196799bd87074afd6e9e8a46f85c0b3e64ff7caa3cbe33f329d3dd.jpg)  
+Figure 3. Architecture overview of our Visual Forager. VF consists of three modules elaborated in Sec. 4: visual feature modulation from target images with foveated vision mimicking eccentricity-dependent sampling in human vision (Sec. 4.1), modulation from various values of different targets (Sec. 4.2), and decision-making process with an actor-critic transformer architecture, outputting next fixation locations from predicted attention maps and the probability of clicking the currently fixated item (Sec. 4.3).
+
+# 4.3. Policy network for decision making
+
+Given  $M_V$  at the current fixation  $F_t$  as the state  $s$  of the environment, VF must decide when to fixate next and whether to collect the currently fixated item. We model this as a Markov Decision Process with finite states and action spaces. Fixation locations are discretized to the  $H \times W$  grid, corresponding to the search array size on  $I_S$ . The mouse click is a binary decision of whether to collect the fixated item. Here, we introduce the architecture of the decision-making module, also known as the policy network.
+
+We employ the vision transformer (ViT) [33]) as the backbone for our decision-making network with its weights randomly initialized. We reshape the value-modulated similarity feature maps  $M_V$  into a sequence of patches  $x_{p}\in \mathbb{R}^{P\times D}$ , where  $P = H\times W$ . To retain positional information for all the patches, we add the standard learnable 1D positional embeddings to all the patches.
+
+Similar to the role of the classification token in the original ViT, we introduce the extra click token to the patch sequence. A click action head is attached to the click token at the last layer of the transformer. The click action head is implemented with an average pooling layer over  $D$  dimensions of the click token embedding followed by a sigmoid function, outputting the probability  $P_{C}$  of the click. A click action  $a_{c}$  is sampled based on  $P_{C}$ .
+
+In parallel, we take the embeddings of all the patches from  $x_{p}$  in the last layer of the ViT, apply average pooling over  $D$  dimensions for every patch, and obtain the logit of fixation probability over total  $P$  locations. Next, we use the softmax operation to normalize the logit map and reshape it to a 2D probabilistic attention map  $P_{F} \in \mathbb{R}^{H \times W}$  indicating the most probable fixation location at  $t + 1$ . The next fixation action  $a_{f} = F_{t + 1}$  is sampled based on  $P_{F}$ .
+
+# 4.4. Training Details
+
+To reduce overfitting and enhance the generalization of our VF, during training, we implement a channel augmentation technique that shuffles the input order of target images and their corresponding value pairs. We train VF with reinforcement learning (RL). The goal of RL is to jointly learn the fixation policy  $\pi_f(\cdot |s)$  and the click policy  $\pi_c(\cdot |s)$ , aiming to maximize the expected cumulative reward. Here, our VF uses the stochastic policy, where  $\pi_f(a_f|s) = P_F$  and  $\pi_c(a_c|s) = P_C$ . With these two separate policies, we can combine them into a joint probability over a multi-discrete action space  $P_A = P_CP_F = \pi (a_c,a_f|s)$ .
+
+We specifically used the Proximal Policy Optimization (PPO) algorithm [109] with the Adam optimizer [64] for training  $P_A$ . PPO is a policy gradient method designed to enhance the stability and efficiency of policy updates. Preliminaries on RL and PPO, as well as PPO hyperparameters, can be found in Appendix Sec. S2.1.
+
+In practice, PPO often employs an actor-critic network [109]. The actor network selects actions based on the current policy, while the critic network evaluates these actions by learning the value function, assessing the quality of the chosen actions. We use the policy network outlined in Sec. 4.3 as our actor network. To estimate the current state's value (or state-value in RL), we add a special state-value token to the token sequence in the policy network. A critic head, implemented as a single fully connected layer, is attached to this token embedding at the final transformer layer to output the state-value  $V_{s}$ .
+
+Two-stage training with curricula. Hybrid visual foraging remains one of the challenges for decision-making due to the diversity of conditions in search scenes. Training VF for this task requires a large amount of interactions with the environments. To address this problem, similar to other
+
+curriculum learning works in RL [73, 97, 98], we introduce two-stage training with curricula where the transfer learning is applied to VF such that experience gained in an easy environment can be leveraged when starting to learn the next harder task.
+
+In the first training stage, we focus solely on training VF's fixation policy  $\pi_f(\cdot |s)$ , omitting the click policy  $\pi_c(\cdot |s)$ . VF receives a reward equal to the target value for accurate fixations and a penalty of -1 for fixations on distractors. This is consistent with the rewards and penalty setups for humans. Moreover, clicking on blank areas wastes time and reduces available clicks; thus, we also apply an intrinsic penalty of -0.01 to discourage suboptimal behaviors in VF. See Tab. S5 for the impact of this intrinsic reward via ablation studies. To further ease the task, we temporarily disable VF's eccentricity-dependent vision, allowing it full-resolution access to the search image.
+
+Without eccentricity-dependent vision,  $M_V$  becomes independent of the fixation  $F_t$  and remains static across all fixations, resulting in a fixed policy for a given state. Thus,  $M_V$  must be modified to enable exploration and prevent VF from repeatedly choosing the same location. Like many other visual search models [58, 115, 145], we introduce an infinite inhibition-of-return (IOR) mechanism, which zeros out all previously visited cells of  $M_V$  on the  $16 \times 16$  grid.
+
+We proceed with the original hybrid foraging setup in the second training stage. We transfer all the weights pre-trained in stage one and freeze them. Only the positional embeddings and the weights responsible for the click policy  $\pi_c(\cdot |s)$  are fine-tunable. The same scoring systems for humans are applied to train our VF. Our VF processes  $I_{S}$  with eccentricity-dependent vision.
+
+In contrast to the infinite IOR in the first stage, we introduce a memory decay mechanism that approximates finite IOR, balancing the exploration of new locations and revisits of known ones. Although finite IOR reflects limitations in memory capacity, it also enhances vision by enabling strategic revisits in complex environments. For example, after exhausting high-value targets, returning to prior locations may help identify the next best options, thereby improving decision-making outcomes. Our VF employs a fixed finite IOR, independent of specific experimental conditions. The suppression of attention values on previously visited locations in  $M_F$  decays over time. At the current  $F_t$ , the suppression on past locations  $\tilde{t} \in \{1, 2, \dots, t\}$  is given by:  $m_{\tilde{t},t} = \eta^{t - \tilde{t}}$ , where  $\eta = 0.8$ . This allows the inhibition of previously visited locations to gradually weaken, enabling VF to revisit those locations.
+
+# 4.5. Baselines and Evaluation Metrics
+
+We introduced six baseline models. (1) Chance: A sequence of eye fixations is generated through uniform random sampling on a 16x16 grid, with a mouse click
+
+(2) Visual Feature Modulation Only (FeatOnly): The method discards value modulations and predicts a sequence of eye fixations based solely on the items with the highest activation on the eccentricity-dependent similarity maps  $M_F$  at  $F_t$ . The sequence of clicks directly corresponds to the sequence of eye fixations. (3) Max Value First (MaxVal) Eccentricity-dependent feature similarity maps  $M_F$  are modulated by multiplying each of its maps with the value of the corresponding target object in  $V^{1:N}$ . A sequence of eye fixations, equivalent to mouse clicks, is predicted by selecting the items with the highest values from value-modulated  $M_V$ . (4) Average Value First (AvgVal) We compute the average of value-modulated  $M_V$  in MaxVal over all target objects. The items with the highest averaged values get fixated and collected by the model. (5) Deep-Q learning (DQN) Instead of modular designs in our VF with the transformer architecture, we use a classical 2D-CNN-based deep-Q network [92] and train it end-to-end. See Appendix Sec. S2.3 for implementation details. (6) Upper bound (UpperBound) It is an oracle model with perfect target localization and recognition abilities, capable of making globally optimal decisions by always selecting the target instances with the highest values in the search arrays and never clicking on a distractor.
+
+All the baseline models use infinite inhibition of return to track previous fixation locations and prevent revisiting them. This is accomplished by either masking the visited item locations in the action probability distribution for DQN or by removing the clicked or fixated items from the search arrays for the other baselines introduced above.
+
+We also include four more baseline models from the existing literature: IVSN [145] and its variant with value modulation and behavioral cloning, along with the pre-trained Gazeformer [93] and Gazeformer fine-tuned on our in-domain data. Since these models do not utilize foveated vision, direct comparisons with our VF model are not entirely equitable. Nevertheless, we include their results to provide a performance reference in Tab. S6.
+
+We propose two evaluation metrics. First, Normalized Score (Norm.Score) refers to the cumulative rewards as a function of the number of clicks within a foraging trial, normalized by the maximum score of UpperBound for that trial. The normalized score has an upper limit of 1, and a higher normalized score indicates that AI models or humans are more effective at making decisions to optimize cumulative rewards. Second, Click Bias Ratio (CBR) reflects the clicking biases of humans and AI models during foraging. We define the proportion of selections of target objects as Proportion Picked (PP) and the proportion of target objects left on the array as Proportion On-Screen (POS). CBR is then calculated as the normalized relative difference between PP and POS:  $CBR = \frac{PP - POS}{PP + POS}$ . CBR
+
+![](images/ad8df476b357d9b2b152911aba2419f2027e9e64d66819604426976f5514b8c0.jpg)  
+A Uneven Value, Equal Prevalence
+
+![](images/df8f83ad2d370a2873284c373195378b1b18ecaeaeb00b64898c16626d1f0d34.jpg)  
+BUneven Value, Unequal Prevalence
+
+![](images/48cad82728db7a7b3b61cef88fb532feea4f52c0d0a77116e14f846ab4d56a70.jpg)  
+C Even Value, Unequal Prevalence  
+Figure 4. Humans and AI models are reward-seeking agents. We report the normalized scores (Norm. Score) as a function of click numbers for humans (red), our VF model (blue), and other baseline models (varying gray). Chance is in black. Three experimental conditions of foraging trials are included with varying prevalence and values of target objects. See Sec. 4.5 for evaluation metrics and baselines, and Sec. 3.2 for experimental conditions.
+
+can change over the course of clicks within a trial. If neither humans nor AI models exhibit clicking preferences, PP will equal POS, resulting in a CBR of 0. If either agent prefers one target object over others, PP will be greater than POS, yielding a positive CBR. If an AI model aligns with human clicking bias patterns in a given experiment, their CBR values will share the same signs. We also used scanpath similarity metrics, including ScanMatch [25] and Fixation Edit Distance (FED) [93], to compare the spatial and temporal dynamics of fixation sequences between humans and AI models. See Tab. S4 for the results and discussion on scanpath similarity.
+
+# 5. Results
+
+# 5.1. Humans and AI models are reward-seeking
+
+Humans and AI models are proficient foragers. We present the Norm.Score of humans and AI models under three conditions where target prevalence, value, or both vary, as shown in Fig. 4. Human subjects achieved Norm. Scores of  $87.4\%$ ,  $84.1\%$ , and  $93.1\%$  across these conditions, significantly exceeding chance levels. Notably, even at the first mouse click, the Norm. Score was already well above chance. Similarly, all competitive baselines and our VF model consistently outperformed chance across all click numbers. These results suggest that both human subjects and AI models are effective at hybrid visual foraging, with their mouse click decisions strongly influenced by the values of target objects. However, humans never reached the upper bound performance, indicating they are not perfect global optimizers. This may be due to imperfect object recognition [86], limited memory capacity [134], and foveated vision with restricted receptive fields [45]. We also present human and model scanpath visualizations in Fig. S6. These examples further support our analysis.
+
+Our VF model outperform all the baseline models. The Norm. Scores of our VF model are  $72.6\%$ ,  $67.1\%$ , and  $81.6\%$  across the three conditions, surpassing all baseline models. This indicates that our VF model
+
+effectively learns to optimize decision-making in foraging tasks and adapts well to variations in value and prevalence. Among the baseline models,FeatOnly achieved the highest Norm. Score, although it still underperformed VF. This highlights the importance of visual feature modulation in foraging tasks, but also reveals that feature modulation alone is insufficient for optimal decision-making. While both AvgVal and MaxVal models incorporate value-based guidance into their decision strategies, their lower performance compared toFeatOnly suggests that simply relying on explicit values is ineffective. Rather, value and feature modulations must interact in a more sophisticated manner to achieve optimal performance.
+
+# 5.2. Eye movements are effected by target values
+
+Eye fixations tend to be drawn to regions associated with higher rewards. Eye movements can serve as a valuable lens for examining the decision-making process [53]. Here, we analyzed eye fixation locations and their correlation with rewards in the corresponding areas. In Fig. S4, we present the average rewards of all target objects within the fixated areas, defined as those falling within a radius of 1.5 degrees of visual angle around each fixation. Surprisingly, we found that both humans and VF tend to fixate on regions associated with average rewards of 3.31 and 8.08, significantly higher than the averages derived from random fixations. This indicates that target values guide fixations during decision-making for both humans and VF. Despite limited visual coverage due to foveation, both agents can effectively explore more rewarding areas. We also conducted the human fixation duration analysis. Remarkably, we found that humans tend to spend more time fixating on higher-value targets compared to those with lower values (Appendix Sec. S3.3).
+
+# 5.3. Behavioral alignment between humans and VF
+
+Both humans and AI models tend to overpick high-valued targets and underpick low-valued targets. In this analysis, we examine the items selected by humans
+
+![](images/8f424ccd70cafa66ceb569032f8bce96a9f4ae5fbc9fd83cf3f52924f592a861.jpg)  
+Figure 5. (A) Our VF model has consistent clicking biases with humans. Humans (red) and our VF models (blue) share the same signs of CBR for most targets under UnValEqPre (a) and UnValUnPre (b). Chance (gray) has no preferences over target objects; hence, a CBR of 0. (B) Our VF model approximates humans in saccade size distributions. Saccade size distributions for humans (red), our VF model (blue), and our VF model with eccentricity removed (light blue) are presented. Vertical dash lines in colors indicate their mean saccade sizes in visual angle degrees. (C) Our VF model can generalize to out-of-distribution hybrid foraging tasks. Spider plot shows Norm.Score for humans (red), our VF (blue) and FeatOnly baseline (black dotted) under 7 experimental conditions.
+
+![](images/beae4ce86ab883fbd145b61398926fca76eedd6880be89840f41e61d2adbefe0.jpg)
+
+![](images/21dcc6d292c10e10c16edd55d42de27e03fe18dcefff92d3060b8b3e648e27fd.jpg)
+
+and AI models from the search arrays and report their click bias ratios (CBR) averaged over all click numbers under the UnValEqPre and UnValUnPre conditions, as shown in Fig. 5A. Consistent with [138], we find that humans tend to overpick the highest-valued targets, as indicated by a positive CBR, while they underpick the lowest-valued targets, reflected by a negative CBR. Interestingly, our VF exhibits similar signs and magnitudes of CBR on these targets, suggesting that it displays analogous click biases based on target values as humans. However, both humans and VF do not exclusively select the highest-valued targets, as their Proportion Picked (PP) for less-valued targets is not exactly zero. This suggests that both humans and VF occasionally select lower-valued targets during foraging. Moreover, we also noted a slight discrepancy in the signs of CBR when humans and VF select the second-highest-valued items. This indicates that VF is less sensitive to medium rewards compared to humans (Fig. S5).
+
+VF approximates the saccade size distributions of humans, without training on human eye movements. Human saccade sizes are restricted by physiological limitations of the eye muscles and oculomotor controls [45]. We aggregate all saccade sizes from humans and our VF model under 3 conditions and plot their distributions in Fig. 5B. Despite lacking prior training on human eye movements, VF yields a mean saccade size of 4.06 degree, closely approximating the mean saccade size of 4.05 degree for humans. Additionally, to investigate the mechanisms that constrain saccade sizes in VF, we present the distribution of saccade sizes with eccentricity-dependent layers replaced with standard max-pooling layers in the visual feature extractor. In line with [51], the saccade sizes increase, suggesting that smaller saccade sizes are partially due to foveal processing.
+
+# 5.4. VF generalizes to OOD conditions
+
+To assess the generalization performance of our VF model in out-of-distribution (OOD) hybrid foraging tasks, we
+
+benchmark humans, VF, and the best baseline model, FeatOnly, across five OOD conditions (Sec. 3.2). From Fig. 5C, VF outperforms FeatOnly in all experimental conditions, indicating that it learns generic decision-making strategies and adapts well to unseen foraging scenarios. However, VF still lags behind humans, suggesting that humans can more flexibly adjust their foraging strategies to maximize rewards based on the environments.
+
+# 6. Discussion
+
+In hybrid visual foraging, humans are proficient foragers, directing attention to regions with significantly higher rewards than chance. When there is an imbalance in target prevalence or values, humans tend to over-exploit the most prevalent or high-valued target types. To explain the characteristics of human behavior in hybrid visual foraging, we propose a transformer-based Visual Forager (VF) trained in reinforcement learning. We designed experiments using 2D grid worlds which provide precise control over experimental variables, minimize confounding factors, and make the analysis of cognitive processes more focused. VF's cumulative rewards match human performance and surpass other baseline models. Despite zero training on any human data, VF closely approximates human foraging behaviors, including foraging biases and eye movements. Remarkably, VF demonstrates exceptional generalization abilities, flexibly adjusting its foraging strategies to experimental conditions it has never encountered. We also clarify that novel stimuli for humans do not equate to an OOD task for AI since humans accumulate years of visual experiences which enables them to generalize effectively. However, we emphasize that while humans exhibit remarkable generalization based on prior experience and flexible cognition, AI models are still inferior to humans from our results. Our work paves the way for several new research directions in psychology, neuroscience, and AI. We discuss these future works in Appendix Sec. S4.
+
+# Acknowledgements
+
+This research is supported by the National Research Foundation, Singapore under its NRFF award NRF-NRFF15-2023-0001 and its AI Singapore Programme (AISG Award No: AISG2-RP-2021-025). We also acknowledge Mengmi Zhang's Start Up Grant from Agency for Science, Technology, and Research (A*STAR), and Start Up Grant from Nanyang Technological University.
+
+# References
+
+[1] Abbas Abdelmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, and Martin Riedmiller. Maximum a posteriori policy optimisation. arXiv preprint arXiv:1806.06920, 2018. 2  
+[2] Hossein Adeli and Gregory Zelinsky. Deep-bcn: Deep networks meet biased competition to create a brain-inspired model of attention control. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 1932-1942, 2018. 2  
+[3] Brian A Anderson and Steven Yantis. Persistence of value-driven attentional capture. Journal of Experimental Psychology: Human Perception and Performance, 39(1):6, 2013. 2, 4  
+[4] Brian A Anderson, Patryk A Laurent, and Steven Yantis. Value-driven attentional capture. Proceedings of the National Academy of Sciences, 108(25):10367-10371, 2011. 2  
+[5] OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chogiej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, et al. Learning dexterous in-hand manipulation. The International Journal of Robotics Research, 39(1):3-20, 2020. 3  
+[6] Jit Wei A Ang and Gerrit W Maus. Boosted visual performance after eye blinks. Journal of Vision, 20(10): 2-2, 2020. 2  
+[7] Jose A Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, and Sepp Hochreiter. Rudder: Return decomposition for delayed rewards. Advances in Neural Information Processing Systems, 32, 2019. 3  
+[8] Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu. Multiple object recognition with visual attention. arXiv preprint arXiv:1412.7755, 2014. 2  
+[9] Tarryn Balsdon, Valentin Wyart, and Pascal Mamassian. Confidence controls perceptual evidence accumulation. Nature communications, 11(1):1753, 2020. 2  
+[10] Harold Bekkering, Jos J Adam, Herman Kingma, A Huson, and HTA Whiting. Reaction time latencies of eye and hand movements in single-and dual-task conditions. Experimental brain research, 97:471-476, 1994. 2  
+[11] Dimitri Bertsekas. Dynamic programming and optimal control: Volume I. Athena scientific, 2012. 2  
+[12] Irving Biederman, Robert J Mezzanotte, and Jan C Rabinowitz. Scene perception: Detecting and judging
+
+objects undergoing relational violations. Cognitive psychology, 14(2):143-177, 1982. 2  
+[13] Ali Borji, Dicky N Sihite, and Laurent Itti. What/where to look next? modeling top-down visual attention in complex interactive environments. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(5):523-538, 2013. 2  
+[14] Timothy F Brady, Talia Konkle, George A Alvarez, and Aude Oliva. Visual long-term memory has a massive storage capacity for object details. Proceedings of the National Academy of Sciences, 105(38):14325-14329, 2008. 3, 1  
+[15] Matthew S Cain, Edward Vul, Kait Clark, and Stephen R Mitroff. A bayesian optimal foraging model of human visual search. Psychological science, 23(9):1047-1054, 2012. 2  
+[16] Frederick Callaway, Antonio Rangel, and Thomas L Griffiths. Fixation patterns in simple choice reflect optimal information sampling. PLoS computational biology, 17(3): e1008863, 2021. 2  
+[17] Eric L Charnov. Optimal foraging, the marginal value theorem. Theoretical population biology, 9(2):129-136, 1976. 2  
+[18] Xianyu Chen, Ming Jiang, and Qi Zhao. Predicting human scanpaths in visual question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10876-10885, 2021. 2  
+[19] Xianyu Chen, Ming Jiang, and Qi Zhao. Beyond average: Individualized visual scanpath prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25420-25431, 2024. 2  
+[20] Ying Chen, Zhiyong Liu, Yongchao Zhang, Yuan Wu, Xin Chen, and Lian Zhao. Deep reinforcement learning-based dynamic resource management for mobile edge computing in industrial internet of things. IEEE Transactions on Industrial Informatics, 17(7):4925-4934, 2020. 3  
+[21] Sun-Joo Cho, Sarah Brown-Schmidt, and Woo-yeol Lee. Autoregressive generalized linear mixed effect models with crossed random effects: An application to intensive binary time series eye-tracking data. Psychometrika, 83:751-771, 2018. 2  
+[22] Tim Chuk, Antoni B Chan, Shinsuke Shimojo, and Janet H Hsiao. Eye movement analysis with switching hidden markov models. Behavior research methods, 52: 1026-1043, 2020. 2  
+[23] Olympia Colizoli, Jan Willem de Gee, Anne E Urai, and Tobias H Donner. Task-evoked pupil responses reflect internal belief states. Scientific reports, 8(1):13702, 2018. 2  
+[24] Lorenza Serena Colzato, Wery PM Van Den Wildenberg, Nelleke C van Wouwe, Merel M Pannebakker, and Bernhard Hommel. Dopamine and inhibitory action control: evidence from spontaneous eye blink rates. Experimental brain research, 196:467-474, 2009. 2  
+[25] Filipe Cristino, Sebastiaan Mathot, Jan Theeuwes, and Iain D Gilchrist. Scanmatch: A novel method for comparing fixation sequences. Behavior research methods, 42:692-700, 2010. 7
+
+[26] Falcon Dai and Matthew Walter. Maximum expected hitting cost of a markov decision process and informativeness of rewards. Advances in Neural Information Processing Systems, 32, 2019. 3  
+[27] Ryan Anthony Jalova de Belen, Tomasz Bednarz, and Arcot Sowmya. Scanpathnet: A recurrent mixture density network for scanpath prediction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5010-5020, 2022. 2  
+[28] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009. 4  
+[29] Rati Devidze, Parameswaran Kamalaruban, and Adish Singla. Exploration-guided reward shaping for reinforcement learning under sparse rewards. Advances in Neural Information Processing Systems, 35:5829-5842, 2022. 3  
+[30] James J DiCarlo, Davide Zoccolan, and Nicole C Rust. How does the brain solve visual object recognition? Neuron, 73(3):415-434, 2012. 2  
+[31] Zhiwei Ding, Xuezhe Ren, Erwan David, Melissa Vo, Gabriel Kreiman, and Mengmi Zhang. Efficient zero-shot visual search via target and context-aware transformer. arXiv preprint arXiv:2211.13470, 2022. 2  
+[32] Mieke Donk and Jan Theeuwes. Prioritizing selection of new elements: Bottom-up versus top-down control. Perception & psychophysics, 65:1231-1242, 2003. 2  
+[33] Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 4, 5  
+[34] Howard E Egeth, Robert A Virzi, and Hadley Garbart. Searching for conjunctively defined targets. Journal of Experimental Psychology: Human Perception and Performance, 10(1):32, 1984. 2  
+[35] Krista A Ehinger and Jeremy M Wolfe. When is it time to move to the next map? optimal foraging in guided visual search. Attention, Perception, & Psychophysics, 78: 2135-2151, 2016. 2  
+[36] Ralf Engbert, Hans A Trukenbrod, Simon Barthelmé, and Felix A Wichmann. Spatial statistics and attentional dynamics in scene viewing. Journal of vision, 15(1):14-14, 2015. 2  
+[37] Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al. Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures. In International conference on machine learning, pages 1407–1416. PMLR, 2018. 2  
+[38] I Evdokimidis, N Smyrnis, Theodoros Constantinidis, N Stefanis, D Avramopoulos, C Paximadis, C Theleritis, C Efstratiadis, G Kastrinakis, and C Stefanis. The antisaccade task in a sample of 2,006 young men: I. normal population characteristics. Experimental Brain Research, 147:45-52, 2002. 2  
+[39] Benjamin Eysenbach, Tianjun Zhang, Sergey Levine, and Russ R Salakhutdinov. Contrastive learning as
+
+goal-conditioned reinforcement learning. Advances in Neural Information Processing Systems, 35:35603-35620, 2022. 3  
+[40] Yini Fang, Jingling Yu, Haozheng Zhang, Ralf van der Lans, and Bertram Shi. Oat: Object-level attention transformer for gaze scanpath prediction. In European Conference on Computer Vision, pages 366-382. Springer, 2024. 2  
+[41] Burkhart Fischer, Monica Biscaldi, and Stefan Gezeck. On the development of voluntary and reflexive components in human saccade generation. Brain research, 754(1-2): 285-297, 1997. 2  
+[42] Klaus-Malte Flechtner, Bruno Steinacher, Robert Sauer, and Arthur Mackert. Smooth pursuit eye movements of patients with schizophrenia and affective disorder during clinical treatment. European archives of psychiatry and clinical neuroscience, 252:49-53, 2002. 2  
+[43] Jolande Fooken and Miriam Spering. Decoding go/no-go decisions from eye movements. Journal of vision, 19(2): 5-5, 2019. 2  
+[44] Tom Foulsham and Geoffrey Underwood. What can saliency models predict about eye movements? spatial and sequential aspects of fixations during encoding and recognition. Journal of vision, 8(2):6-6, 2008. 2  
+[45] Jeremy Freeman and Eero P Simoncelli. Metamers of the ventral stream. Nature neuroscience, 14(9):1195-1201, 2011. 4, 7, 8  
+[46] Mackenzie G Glaholt and Eyal M Reingold. Eye movement monitoring as a process tracing methodology in decision making research. Journal of Neuroscience, Psychology, and Economics, 4(2):125, 2011. 2  
+[47] Sebastian Gluth, Nadja Kern, Maria Kortmann, and Cécile L Vitali. Value-based attention but not divisive normalization influences decisions with multiple alternatives. Nature human behaviour, 4(6):634-645, 2020. 2  
+[48] David C Godlove and Jeffrey D Schall. Microsaccade production during saccade cancelation in a stop-signal task. Vision research, 118:5-16, 2016. 2  
+[49] Alexander Goettker and Karl R Gegenfurtner. A change in perspective: The interaction of saccadic and pursuit eye movements in oculomotor control and perception. Vision Research, 188:283-296, 2021. 2  
+[50] Jiaqi Gong, Shengting Cao, Soroush Korivand, and Nader Jalili. Reconstructing human gaze behavior from eeg using inverse reinforcement learning. Smart Health, 32:100480, 2024. 2  
+[51] Shashi Kant Gupta, Mengmi Zhang, Chia-Chien Wu, Jeremy Wolfe, and Gabriel Kreiman. Visual search asymmetry: Deep nets and humans share similar inherent biases. Advances in neural information processing systems, 34:6946-6959, 2021. 2, 4, 8, 1  
+[52] Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In International conference on machine learning, pages 1861-1870. PMLR, 2018. 2
+
+[53] Fred H Hamker. The reentry hypothesis: the putative interaction of the frontal eye field, ventrolateral prefrontal cortex, and areas v4, it for attention and eye movement. Cerebral cortex, 15(4):431-447, 2005. 7  
+[54] Nicklas Hansen, Hao Su, and Xiaolong Wang. Stabilizing deep q-learning with convnets and vision transformers under data augmentation. Advances in neural information processing systems, 34:3680-3693, 2021. 3  
+[55] Nicklas Hansen, Jyothir SV, Vlad Sobal, Yann LeCun, Xiaolong Wang, and Hao Su. Hierarchical world models as visual whole-body humanoid controllers. arXiv preprint arXiv:2405.18418, 2024. 3  
+[56] W Keith Hastings. Monte carlo sampling methods using markov chains and their applications. 1970. 2  
+[57] John M Henderson. Object identification in context: the visual processing of natural scenes. Canadian Journal of Psychology/Revue canadienne de psychologie, 46(3):319, 1992. 2  
+[58] Frank K Hu, Arthur G Samuel, and Agnes S Chan. Eliminating inhibition of return by changing salient nonspatial attributes in a complex environment. Journal of Experimental Psychology: General, 140(1):35, 2011. 6  
+[59] Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Kate Saenko. Learning to reason: End-to-end module networks for visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 804-813, 2017. 2  
+[60] Laurent Itti and Christof Koch. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision research, 40(10-12):1489-1506, 2000. 2  
+[61] Ming Jiang, Shi Chen, Jinhui Yang, and Qi Zhao. Fantastic answers and where to find them: Immersive question-directed visual attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2980-2989, 2020. 2  
+[62] Sung Jun Joo, Leor N Katz, and Alexander C Huk. Decision-related perturbations of decision-irrelevant eye movements. Proceedings of the National Academy of Sciences, 113(7):1925–1930, 2016. 2  
+[63] Katsuhisa Kawaguchi, Stephane Clery, Paria Pourriahi, Lenka Seillier, Ralf M Haefner, and Hendrikje Nienborg. Differentiating between models of perceptual decision making using pupil size inferred confidence. Journal of Neuroscience, 38(41):8874-8888, 2018. 2  
+[64] Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 5  
+[65] Krista Kornylo, Natalie Dill, Melissa Saenz, and Richard J Krauzlis. Canceling of pursuit and saccadic eye movements in humans and monkeys. Journal of Neurophysiology, 89 (6):2984-2999, 2003. 2  
+[66] Ilona Kovacs and Bela Julesz. A closed curve is much more than an incomplete one: effect of closure in figure-ground segmentation. Proceedings of the National Academy of Sciences, 90(16):7495-7497, 1993. 2  
+[67] Ian Krajbich and Antonio Rangel. Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions.
+
+Proceedings of the National Academy of Sciences, 108(33): 13852-13857, 2011. 2  
+[68] Ian Krajbich, Carrie Armel, and Antonio Rangel. Visual fixations and the computation and comparison of value in simple choice. Nature neuroscience, 13(10):1292-1298, 2010. 2  
+[69] Richard J Krauzlis. The control of voluntary eye movements: new perspectives. The Neuroscientist, 11(2): 124-137, 2005. 2  
+[70] Arni Kristjansson and Jon Driver. Priming in visual search: Separating the effects of target repetition, distractor repetition and role-reversal. Vision Research, 48(10): 1217-1232, 2008. 2  
+[71] Elizabeth A Krupinski. Current perspectives in medical image perception. Attention, Perception, & Psychophysics, 72(5):1205-1217, 2010. 2  
+[72] Adam Laud and Gerald DeJong. The influence of reward on the speed of reinforcement learning: An analysis of shaping. In Proceedings of the 20th International Conference on Machine Learning (ICML-03), pages 440–447, 2003. 3  
+[73] Seungjae Lee, Daesol Cho, Jonghae Park, and H Jin Kim. Cqm: Curriculum reinforcement learning with a quantized world model. Advances in Neural Information Processing Systems, 36:78824-78845, 2023. 6  
+[74] Tai Sing Lee and David Mumford. Hierarchical bayesian inference in the visual cortex. JOSA a, 20(7):1434-1448, 2003. 2  
+[75] R John Leigh and David S Zee. The neurology of eye movements. Oxford University Press, USA, 2015. 2  
+[76] Rebekka Lencer, Katja Trillenberg-Krecker, Eberhard Schwinger, and Volker Arolt. Schizophrenia spectrum disorders and eye tracking dysfunction in singleton and multiplex schizophrenia families. Schizophrenia Research, 60(1):33-45, 2003. 2  
+[77] John Liechty, Rik Pieters, and Michel Wedel. Global and local covert visual attention: Evidence from a bayesian hidden markov model. Psychometrika, 68:519-541, 2003. 2  
+[78] Zhongqiao Lin, Chechang Nie, Yuanfeng Zhang, Yang Chen, and Tianming Yang. Evidence accumulation for value computation in the prefrontal cortex during decision making. Proceedings of the National Academy of Sciences, 117(48):30728-30737, 2020. 2  
+[79] Stephen G Lisberger. Visual guidance of smooth pursuit eye movements. Annual review of vision science, 1(1):447-468, 2015. 2  
+[80] Yanjun Liu, Jeremy M Wolfe, and Jennifer Trueblood. The impact of risk and prevalence on foraging behavior in hybrid visual search. In Proceedings of the Annual Meeting of the Cognitive Science Society, 2023. 2  
+[81] Weiyu Ma, Qirui Mi, Xue Yan, Yuqiao Wu, Runji Lin, Haifeng Zhang, and Jun Wang. Large language models play starcraft ii: Benchmarks and a chain of summarization approach. arXiv preprint arXiv:2312.11865, 2023. 3  
+[82] Mary H MacLean and Barry Giesbrecht. Irrelevant reward and selection histories have different influences on
+
+task-relevant attentional selection. Attention, Perception, & Psychophysics, 77:1515-1528, 2015. 2  
+[83] Hongzi Mao, Mohammad Alizadeh, Ishai Menache, and Srikanth Kandula. Resource management with deep reinforcement learning. In Proceedings of the 15th ACM workshop on hot topics in networks, pages 50-56, 2016. 3  
+[84] John HR Maunsell and Stefan Treue. Feature-based attention in visual cortex. Trends in neurosciences, 29(6): 317-322, 2006. 2  
+[85] Eugene McSorley and Rachel McCloy. Saccadic eye movements as an index of perceptual decision-making. Experimental brain research, 198:513-520, 2009. 2  
+[86] Thomas Miconi, Laura Groomes, and Gabriel Kreiman. There's waldo! a normalization model of visual search predicts single-trial human fixations in an object search task. Cerebral cortex, 26(7):3064-3082, 2015. 2, 7  
+[87] Koorosh Mirpour and James W Bisley. The roles of the lateral intraparietal area and frontal eye field in guiding eye movements in free viewing search behavior. Journal of Neurophysiology, 2021. 2  
+[88] Marcus Missal and Stephen J Heinen. Stopping smooth pursuit. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1718):20160200, 2017. 2  
+[89] Volodymyr Mnih. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013. 3  
+[90] Volodymyr Mnih. Asynchronous methods for deep reinforcement learning. arXiv preprint arXiv:1602.01783, 2016. 2, 3  
+[91] Volodymyr Mnih, Nicolas Heess, Alex Graves, et al. Recurrent models of visual attention. Advances in neural information processing systems, 27, 2014. 2  
+[92] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. nature, 518(7540):529-533, 2015. 2,6,3  
+[93] Sounak Mondal, Zhibo Yang, Seoyoung Ahn, Dimitris Samaras, Gregory Zelinsky, and Minh Hoai. Gazeformer: Scalable, effective and fast prediction of goal-directed human attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1441-1450, 2023. 2, 6, 7  
+[94] Milica Mormann and J Edward Russo. Does attention increase the value of choice alternatives? Trends in cognitive sciences, 25(4):305-315, 2021. 2  
+[95] Douglas P Munoz and Brian C Coe. Saccade, search and orient—the neural control of saccadic eye movements, 2011. 2  
+[96] Peter R Murphy, Niklas Wilming, Diana C Hernandez-Bocanegra, Genis Prat-Ortega, and Tobias H Donner. Adaptive circuit dynamics across human cortex during evidence accumulation in changing environments. Nature neuroscience, 24(7):987–997, 2021. 2  
+[97] Sanmit Narvekar. Curriculum learning in reinforcement learning. In *IJCAI*, pages 5195-5196, 2017. 6
+
+[98] Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E Taylor, and Peter Stone. Curriculum learning for reinforcement learning domains: A framework and survey. Journal of Machine Learning Research, 21 (181):1-50, 2020. 6  
+[99] Adam Pardyl, Michal Wronka, Maciej Wolczyk, Kamil Adamczewski, Tomasz Trzciński, and Bartosz Zielinski. Adaglimpse: Active visual exploration with arbitrary glimpse position and scale. arXiv preprint arXiv:2404.03482, 2024. 2, 3  
+[100] MJ Plank and A James. Optimal foraging: Lévy pattern or process? Journal of the Royal Society Interface, 5(26): 1077-1086, 2008. 2  
+[101] Allen D Radant and Daniel W Hommer. A quantitative analysis of saccades and smooth pursuit during visual pursuit tracking: A comparison of schizophrenics with normals and substance abusing controls. Schizophrenia research, 6(3):225-235, 1992. 2  
+[102] Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36, 2024. 2  
+[103] Martin Rolfs. Microsaccades: small steps on a long way. Vision research, 49(20):2415-2441, 2009. 2  
+[104] Randal G Ross, Ann Olincy, Gary Zerbe, and Allen Radant. Which duration of postsaccadic slowing identifies anticipatory saccades during smooth pursuit eye movements? Psychophysiology, 38(2):325-333, 2001. 2  
+[105] Peter Roy-Byrne, Allen Radant, Dane Wingerson, and Deborah S Cowley. Human oculomotor function: reliability and diurnal variation. Biological Psychiatry, 38(2):92–97, 1995. 2  
+[106] Marian Sauter, Nina M Hanning, Heinrich R Liesefeld, and Hermann J Müller. Post-capture processes contribute to statistical learning of distractor locations in visual search. Cortex, 135:108-126, 2021. 2  
+[107] Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, et al. Habitat: A platform for embodied ai research. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9339-9347, 2019. 3  
+[108] John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438, 2015. 2, 3  
+[109] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. 2, 5  
+[110] Joshua A Seideman, Terrence R Stanford, and Emilio Salinas. Saccade metrics reflect decision-making dynamics during urgent choices. Nature communications, 9(1):2907, 2018. 2  
+[111] Younggyo Seo, Danijar Hafner, Hao Liu, Fangchen Liu, Stephen James, Kimin Lee, and Pieter Abbeel. Masked world models for visual control. In Conference on Robot Learning, pages 1332-1344. PMLR, 2023. 3
+
+[112] Reza Shadmehr, Thomas R Reppert, Erik M Summerside, Tehrim Yoon, and Alaa A Ahmed. Movement vigor as a reflection of subjective economic utility. Trends in neurosciences, 42(5):323-336, 2019. 2  
+[113] David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. Mastering the game of go with deep neural networks and tree search. nature, 529(7587): 484-489, 2016. 3  
+[114] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 4  
+[115] Tim J Smith and John M Henderson. Does oculomotor inhibition of return influence fixation probability during scene search? Attention, Perception, & Psychophysics, 73: 2384-2398, 2011. 6  
+[116] N Smyrnis, I Evdokimidis, N Stefanis, T Constantinidis, D Avramopoulos, C Theleritis, C Paximadis, C Efstratiadis, G Kastrinakis, and C Stefanis. The antisaccade task in a sample of 2,006 young males: II. effects of task parameters. Experimental brain research, 147:53-63, 2002. 2  
+[117] Marc A Sommer and Robert H Wurtz. What the brain stem tells the frontal cortex. i. oculomotor signals sent from superior colliculus to frontal eye field via mediodorsal thalamus. Journal of neurophysiology, 91(3):1381-1402, 2004. 2  
+[118] Mingyu Song, Xingyu Wang, Hang Zhang, and Jian Li. Proactive information sampling in value-based decision-making: Deciding when and where to saccade. Frontiers in human neuroscience, 13:35, 2019. 2  
+[119] Miriam Spering. Eye movements as a window into decision-making. Annual review of vision science, 8(1): 427-448, 2022. 2  
+[120] Terrence R Stanford and Emilio Salinas. Urgent decision making: resolving visuomotor interactions at high temporal resolution. Annual Review of Vision Science, 7(1):323-348, 2021. 2  
+[121] Peter Stüttgen, Peter Boatwright, and Robert T Monroe. A satisficing choice model. Marketing Science, 31(6): 878-899, 2012. 2  
+[122] Xiangjie Sui, Yuming Fang, Hanwei Zhu, Shiqi Wang, and Zhou Wang. Scandmm: A deep markov model of scanpath prediction for 360deg images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6989-6999, 2023. 2  
+[123] Bernard Marius 't Hart, Hannah Claudia Elfriede Fanny Schmidt, Ingo Klein-Harmeyer, and Wolfgang Einhäuser. Attention in natural scenes: contrast affects rapid visual processing and fixations alike. Philosophical Transactions of the Royal Society B: Biological Sciences, 368(1628): 20130067, 2013. 2  
+[124] Steven Taylor and David Badcock. Processing feature density in preattentive perception. Perception & psychophysics, 44:551-562, 1988. 2  
+[125] David Thura, Ignasi Cos, Jessica Trung, and Paul Cisek. Context-dependent urgency influences speed-accuracy
+
+trade-offs in decision-making and movement execution. Journal of Neuroscience, 34(49):16442-16454, 2014. 2  
+[126] Andrew J Toole and Nick Fogt. head and eye movements and gaze tracking in baseball batting. Optometry and Vision Science, 98(7):750-758, 2021. 2  
+[127] Antonio Torralba, Aude Oliva, Monica S Castelhano, and John M Henderson. Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. Psychological review, 113(4):766, 2006. 2  
+[128] R Blythe Towal, Milica Mormann, and Christof Koch. Simultaneous modeling of visual saliency and value computation improves predictions of economic choice. Proceedings of the National Academy of Sciences, 110(40): E3858-E3867, 2013. 2  
+[129] Ralf Van der Lans, Rik Pieters, and Michel Wedel. Eye-movement analysis of search effectiveness. Journal of the American Statistical Association, 103(482):452-461, 2008. 2  
+[130] Leonard Elia Van Dyck, Roland Kwitt, Sebastian Jochen Denzler, and Walter Roland Gruber. Comparing object recognition in humans and deep convolutional neural networks—an eye tracking study. Frontiers in Neuroscience, 15:750639, 2021. 2  
+[131] Joanne C Van Slooten, Sara Jahfari, and Jan Theeuwes. Spontaneous eye blink rate predicts individual differences in exploration and exploitation during reinforcement learning. Scientific reports, 9(1):17436, 2019. 2  
+[132] Derrick G Watson and Glyn W Humphreys. Visual marking: prioritizing selection for new objects by top-down attentional inhibition of old objects. Psychological review, 104(1):90, 1997. 2  
+[133] Jeremy Wolfe, Matthew Cain, Krista Ehinger, and Trafton Drew. Guided search 5.0: Meeting the challenge of hybrid search and multiple-target foraging. Journal of vision, 15 (12):1106-1106, 2015. 2  
+[134] Jeremy M Wolfe. Saved by a log: How do humans perform hybrid visual and memory search? Psychological Science, 23(7):698-703, 2012. 7  
+[135] Jeremy M Wolfe and Jennifer S DiMase. Do intersections serve as basic features in visual search? Perception, 32(6): 645-656, 2003. 2  
+[136] Jeremy M Wolfe and Todd S Horowitz. Five factors that guide attention in visual search. Nature human behaviour, 1(3):0058, 2017. 2  
+[137] Jeremy M Wolfe, Avigael M Aizenman, Sage EP Boettcher, and Matthew S Cain. Hybrid foraging search: Searching for multiple instances of multiple types of target. Vision research, 119:50-59, 2016. 2  
+[138] Jeremy M Wolfe, Matthew S Cain, and Abla Alaoui-Soce. Hybrid value foraging: How the value of targets shapes human foraging behavior. Attention, Perception, & Psychophysics, 80:609-621, 2018. 3, 8  
+[139] Jeremy M Wolfe, Matthew S Cain, and Avigael M Aizenman. Guidance and selection history in hybrid foraging visual search. Attention, Perception, & Psychophysics, 81:637-653, 2019. 2
+
+[140] Chia-Chien Wu and Jeremy M Wolfe. Eye movements in medical image perception: a selective review of past, present and future. Vision, 3(2):32, 2019. 2  
+[141] Zhibo Yang, Lihan Huang, Yupei Chen, Zijun Wei, Seoyoung Ahn, Gregory Zelinsky, Dimitris Samaras, and Minh Hoai. Predicting goal-directed human attention using inverse reinforcement learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 193-202, 2020. 2  
+[142] Zhibo Yang, Sounak Mondal, Seoyoung Ahn, Ruoyu Xue, Gregory Zelinsky, Minh Hoai, and Dimitris Samaras. Unifying top-down and bottom-up scanpath prediction using transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1683-1693, 2024. 2  
+[143] Tehrim Yoon, Robert B Geary, Alaa A Ahmed, and Reza Shadmehr. Control of movement vigor and decision making during foraging. Proceedings of the National Academy of Sciences, 115(44):E10476-E10485, 2018. 2  
+[144] Jinxia Zhang, Xue Gong, Daryl Fougnie, and Jeremy M Wolfe. How humans react to changing rewards during visual foraging. Attention, Perception, & Psychophysics, 79:2299-2309, 2017. 2  
+[145] Mengmi Zhang, Jiashi Feng, Keng Teck Ma, Joo Hwee Lim, Qi Zhao, and Gabriel Kreiman. Finding any waldo with zero-shot invariant and efficient visual search. Nature communications, 9(1):3730, 2018. 2, 4, 6  
+[146] Mengmi Zhang, Marcelo Armendariz, Will Xiao, Olivia Rose, Katarina Bendtz, Margaret Livingstone, Carlos Ponce, and Gabriel Kreiman. Look twice: A generalist computational model predicts return fixations across tasks and species. PLoS computational biology, 18(11): e1010654, 2022. 2, 4  
+[147] Guangyao Zhou, Wenhong Tian, Rajkumar Buyya, Ruini Xue, and Liang Song. Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions. Artificial Intelligence Review, 57(5):124, 2024. 3

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+# Gen3DEval: Using vLLMs for Automatic Evaluation of Generated 3D Objects
+
+Shalini Maiti*† Lourdes Agapito† Filippos Kokkinos*
+
+*Meta AI †University College London
+
+![](images/e9508144c53a9912ece31378b93b567af95cb20252cfe717a0491d49403fb1f4.jpg)
+
+![](images/51fa661d259e1ea33e277c73a8537ae167ed46f9c64dafc71975f96147e8c0f9.jpg)
+
+![](images/479d773269fb0aef9f56be380e6f0868bf436fdea4ae366b76a3946b3d39a40b.jpg)
+
+![](images/b9aa855c729878cf47f711194d8991f6d55ba4ddd920bad928dfbe8db471e305.jpg)  
+Figure 1. Gen3DEval: A holistic ranking metric to assess the quality of generated 3D objects on appearance, surface quality and text fidelity using a vision large language model (vLLM) which is trained to choose the better out of two objects on the three evaluation dimensions (appearance, text fidelity or surface quality).
+
+![](images/8af0a7d610915b3f37972424dc8b0f0be11ee04d4b5902bbf3f65b909bf2c3f2.jpg)
+
+![](images/e452b601d94267c952fcd6c307865cc9c8a8dc225272e31ab6f7780740ee1b1e.jpg)
+
+# Abstract
+
+Rapid advancements in text-to-3D generation require robust and scalable evaluation metrics that align closely with human judgment, a need unmet by current metrics such as PSNR and CLIP, which require ground-truth data or focus only on prompt fidelity. To address this, we introduce Gen3DEval, a novel evaluation framework that leverages vision large language models (vLLMs) specifically finetuned for 3D object quality assessment. Gen3DEval evaluates text fidelity, appearance, and surface quality by analyzing 3D surface normals, without requiring ground-truth comparisons, bridging the gap between automated metrics and user preferences. Compared to state-of-the-art task-agnostic models, Gen3DEval demonstrates superior performance in user-aligned evaluations, placing it as a comprehensive and accessible benchmark for future research on text-to-3D generation. The project page can be found here: https://shalini-maiti.github.io/gen3deval.github.io/.
+
+# 1. Introduction
+
+The domain of text-to-3D generation has advanced significantly in recent years, driven by the rise of scalable architectures like diffusion models [43], neural radiance fields (NeRF) [36], and Gaussian splatting [25]. However, the field lacks standardized, human-aligned evaluation metrics that can reliably assess these assets and the methods that produce them. Existing metrics—such as CLIP [42] scores evaluate only limited aspects of the output like text fidelity and similarity-based measures like Peak-Signal-To-Noise Ratio (PSNR), SSIM [55], Chamfer Distance, and Fréchet Inception Distance (FID) [20] depend on ground-truth data making them inadequate and impractical for text-to-3D generation, where diverse outputs may correspond to a single prompt. In such cases, a unique, universally applicable reference does not exist, as multiple plausible 3D outputs can vary widely in style, appearance, and fidelity to the text. Meanwhile, FID computes a distributional similarity, which poses other challenges. Currently, there is no standardized large-scale validation set to serve as the ground-truth distri
+
+bution for 3D assets, making FID computation difficult and inconsistent. Moreover, generating sufficient 3D assets to estimate this distribution requires significant computational resources, casting FID as an expensive and time-intensive metric. As a result, these metrics fall short of capturing the nuanced requirements of evaluating text-to-3D generation, where a scalable, human-aligned approach is essential.
+
+While prior work such as GPT4VEval [57] has leveraged GPT-4V [38] for assessing 3D asset quality, GPT-4V is a general-purpose model not specifically trained for 3D quality assessment, which limits its effectiveness in this domain. Furthermore, it can be costly to deploy at scale, and in our experiments, we found that GPT-4o (which is the successor to GPT-4V) performed significantly worse than our method in aligning with human judgments of 3D asset quality.
+
+To bridge this gap, we introduce Gen3DEval, a vision-based large language model (vLLM) framework specifically fine-tuned to evaluate text-to-3D generation outputs in alignment with human preferences. Unlike existing metrics [42, 55, 63], Gen3DEval assesses not only text fidelity but also appearance and surface quality by analyzing rendered multi-view images. Supporting up to eight images as input, Gen3DEval enables comprehensive assessment by leveraging multi-view renderings, such as RGB and normal maps, of generated 3D objects. Using multi-view images as input allows for compatibility across diverse 3D representations [25, 36, 60].
+
+Built upon the recent vLLM early fusion approaches [4, 5, 27, 29], our method processes these input renderings by first encoding each image through an image encoder, which translates them into visual tokens. These tokens are then integrated with text tokens and fed into a Llama3 model, allowing Gen3DEval to interpret both visual and textual features of the 3D objects holistically. To ensure robust performance, we curate data from human assessments and further enhance our training dataset with synthetically generated perturbations of artist-created 3D objects, incorporating artifacts like floaters, transparency errors, text fidelity inconsistencies, excessively smooth surfaces etc.
+
+A key component of our framework is Gen3DEval-Bench, a benchmark dataset designed to standardize text-to-3D evaluations across various quality dimensions. Comprising 80 diverse prompts, Gen3DEval-Bench facilitates consistent, human-aligned assessments of visual fidelity and aesthetic preferences. Our evaluation pipeline involves two main stages: first, it performs pairwise comparisons of 3D objects using multi-view renderings. Then it applies ELO rating metrics [13] to generate scores that closely align with human judgment. This process ensures robust and reliable evaluations across a broad range of 3D generative methods. To sum up, our contributions are:
+
+- A state-of-the-art holistic evaluation method for text-to-3D generation that ranks methods across appearance, sur
+
+face quality and text fidelity.
+
+- A vLLM fine-tuned on the Llama3 [2] model, using a synthetic dataset curated to reflect human preferences for evaluating generated 3D assets.  
+- A benchmark dataset, Gen3DEval-Bench, comprising 80 prompts for ranking existing and future text-to-3D generation methods in a standardized manner.
+
+# 2. Related Work
+
+Text-to-3D and Image-to-3D generation. In recent times, the landscape of text-to-3D generation has seen rapid growth with the advent of representations such as Neural Radiance Fields [36], occupancy fields [37], SDF [60] and Gaussian Splats [25], and the availability of large, publicly available datasets such as Objaverse [10, 11]. Some of the earlier methods in the space currently include [23, 28, 40, 41, 47, 48, 53, 56, 61, 64] that optimizes a randomly-initialized 3D model via gradient descent conditioned on sampled outputs of a text-to-image generation model. Another direction of work include methods such as [14, 21, 24, 31, 45, 46, 54] that use multi-view diffusion models to fine-tune text-to-image models to quickly generate highly consistent multiple views or videos simultaneously from a single input image. Notably, another family of methods [15, 22, 47] learns 3D priors from a large amount of data and a scalable architecture to directly output robust 3D outputs from text or image inputs. With such an impressive pace of growth in this research domain, it is imperative for the presence of robust evaluation metrics and benchmarks to ensure continued progress in this field, which is a gap that Gen3DEval attempts to bridge.
+
+3D Evaluation Metrics and benchmarks Classical 3D metrics like PSNR, Chamfer Distance, LPIPS [63] and SSIM [55] were developed to measure the quality of a generated 3D asset against a ground-truth asset. However, these are similarity metrics and measure the distance between generated and the ground-truth data. This is infeasible since a single text prompt can be mapped to many generated 3D outputs, with their quality or fidelity being independent of their similarity to any single generated asset. We propose that instead of measuring similarity, we need to inject 3D quality priors into the method itself for the purpose of evaluation, which is the foundation of Gen3DEval.
+
+Other metrics such as CLIP [42] scores have tried to measure alignment with text by using a standard benchmark of textual prompts and computing a corresponding score. However, they underwhelm with an increase in diversity of prompts. Another inadequacy is that only textual alignment is measured and not appearance or surface quality. Gen3DEval addresses both of these aspects. The work closest to ours in attempting to solve this problem is GPT-4V(ison) is a Human-Aligned Evaluator for Text-to-3D Generation [57]. However, it uses GPT4 [38] off the shelf,
+
+![](images/579ec440a56b8c43a8ac4fb915479b109fe3be64f5df935925b4708df36a5137.jpg)  
+Stage 1: Train the vLLM (Gen3DEval) in two stages: pre-training and supervised fine-tuning (SFT).  
+Figure 2. Gen3DEval framework: In stage 1, we train a vLLM to choose which object is better in terms of appearance, surface quality or text fidelity. This is further divided into 2 parts. In pre-training, we train the vision-to-language projector using image summary VQA. In the supervised fine-tuning (SFT) stage, we use comparison data to train for instruction following and preference evaluation. In stage 2, we compute a ranking metric for the set of methods by applying the trained vLLM from stage 1 pairwise on Gen3DEval-Bench prompts.
+
+![](images/863556af3f21163f09f38966cb71dc225e042725ff1338cbf6edf13c8f68a513.jpg)  
+Stage 2: Use the trained vLLM (Gen3DEval) to generate a ranking metric.
+
+which is a task-agnostic vLLM trained on half a trillion parameters whose API and checkpoints are not publicly available, making it costly to scale, whereas Gen3DEval has been specifically trained to evaluate text-to-3D objects, on 8.35 billion parameters and will be made available for public usage. Moreover, in our experiments, we found that Gen3DEval performed significantly better than GPT4-o in aligning with human judgments of 3D asset quality.
+
+In the absence of quantifiable metrics, user studies have been popularly employed as the gold standard to evaluate 3D generation methods. However, this is time-consuming, cost-effective and lacks a standard procedure. Certain benchmarks such as  $\mathrm{T}^3$  Bench [17] and Dreamfusion prompts [40] have been created to reduce the lack of standardization in this process. Gen3DEval takes a step further in this direction by curating a benchmark Gen3DEval-Bench with diverse prompts in terms of types objects, length and compositionality.
+
+Large Multi-modal Models The past couple of years has seen great strides made in the development of Large languages models (LLMs) like Llama [52], GPT-4 [38], Claude [3], Gemini [49], and consequently, led to development of vLLMs such as LLaVA [29], BLIP [27], FUYU [5] and more [2, 3, 38]. They are powerful multimodal models that display strong image and language reasoning. However, since these are general purpose models, they do not perform well on evaluating generated 3D ob
+
+jects. [57] showcases capabilities of GPT4 [38] to be able to align with human preference for the assessment of 3D objects. Gen3DEval takes this effort further by fine-tuning a Llama3 [2] model using a curated synthetic dataset for the specific purpose of introducing 3D aesthetic preference into the vision-language space and transforming that into a ready-to-use evaluation ranking metric.
+
+# 3. Method
+
+The proposed method, Gen3DEval is a vision-based large language model (vLLM) that interprets and assesses the quality of 3D generated objects. We train Gen3DEval using a carefully curated Visual-Question-Answering (VQA) dataset, as detailed in Section 4.1. This training enables Gen3DEval to learn associations between visual cues in multi-view images and quality indicators such as text fidelity, surface detail, and appearance. We use up to eight multi-view RGB images—renderings that include RGB and normal maps—to capture comprehensive object details from multiple angles; see Figure 2 for an overview.
+
+# 3.1. Model Details
+
+The Gen3DEval training process builds upon the LLaVA architecture [29] and is organized into two sequential phases: pre-training and supervised fine-tuning. In the initial phase, each image is processed by an image encoder to produce visual embeddings  $I_{emb}$ . These embeddings are transformed
+
+into language-compatible tokens  $H_{i}$  via a linear projection matrix  $W_{\theta}$ , enabling them to integrate seamlessly with the language-based representations. At the same time, a language tokenizer converts the natural language QuestionAnswer (QA) pairs into text tokens  $H_{t}$ . The model  $F_{\phi}$  then receives both the image and text tokens as input, learning to predict the next token  $H_{y}$  by maximizing the likelihood of the correct token.
+
+For the image encoder, we initially consider CLIP [42], which is commonly used across various vision-language models [29, 40, 44]. However, since the input images are rendered views of 3D objects, we also evaluate two additional encoders: DinoV2 [39] and Fit3D [62]. DinoV2 generates visually consistent embeddings, while Fit3D is specifically designed to encode 2D images into features consistent with the underlying 3D scenes, making it particularly suited to our task. A comparison of these feature encoders is provided in Table 1 and discussed in Section 4.2.
+
+During the pre-training phase, the weights of both the LLM and the image encoder are frozen, and only the weights of the linear projection matrix  $W_{\theta}$  are updated. This selective tuning establishes alignment between the visual embeddings and language tokens, forming a foundation for integrated visual-language comprehension.
+
+In the fine-tuning phase, we unfreeze both the projection matrix and the LLM, allowing them to be fine-tuned jointly, while keeping the image encoder's weights frozen. This stage further specializes the model for 3D quality assessment, enhancing its sensitivity to features such as surface texture, text fidelity, and overall visual coherence across various prompts and multi-view renderings.
+
+# 3.2. Multi-view Input
+
+To effectively evaluate the quality of generated 3D objects, Gen3DEval leverages multi-view input, using up to 8 rendered images uniformly panning each object. This approach is essential for capturing the complete appearance and surface consistency of 3D objects, as single-view images may overlook aspects like hidden surfaces and occlusions that become visible when observed from multiple viewpoints.
+
+In pre-training, the input images range from a set of 1 to 4 RGB images or rendered surface normals panning the object in a  $360^{\circ}$  round-table manner alongside a short summary in a QA pair. In fine-tuning, we input two sets of images, for object 1 and object 2. Each set consists upto 4 multi-view images each, therefore training a VQA sample consists upto 8 images and QA capturing the preference for the preferred object. The number of tokens per image is 576 and approx. 250 for the text of the question.
+
+# 4. Training Details
+
+The following section provides an in-depth look at the Gen3DEval dataset, outlining its composition, structure,
+
+and the methodologies used to create a diverse and robust training set. We describe the dataset's sources and organization, the approach to rendering multi-view images, and the use of human judgment and synthetic perturbations to enhance model alignment with 3D quality standards. Each component of the dataset is designed to support the pre-training and fine-tuning stages, ensuring comprehensive coverage of key attributes like appearance, surface consistency, and text fidelity.
+
+# 4.1. Dataset
+
+Gen3DEval's training dataset is designed to train and evaluate the model's ability to assess 3D object quality across various dimensions, including appearance, surface consistency, and text fidelity. It comprises three subsets a) 3D artist-created meshes, b) human preference data on generative method outputs and c) synthetic 3D comparison data.
+
+3D Meshes: Comprising 140,000 high-quality 3D meshes created by artists, this internal dataset spans diverse semantic categories and provides a robust foundation for generalizing to different types of 3D content. Each asset comes with an accompanying text caption generated with Llama3.2 [2]. We render each 3D asset from multiple viewpoints, creating three types of visual inputs: RGB images, alpha masks, and surface normals. These multi-view renderings allow Gen3DEval to capture comprehensive visual cues necessary for accurate 3D evaluation.
+
+Human Annotations: To account for the nuances and irregularities inherent in 3D generative methods, we conducted a large-scale human preference study, collecting over 5K comparative data points across 13 different 3D generative methods [6, 8, 15, 16, 18, 30, 32-34, 46, 47, 51, 54]. Annotators viewed  $360^{\circ}$  videos of two 3D assets side-by-side and selected the preferred asset based on appearance and alignment with the corresponding generation prompt.
+
+This preference data was incorporated into the Gen3DEval training dataset, enhancing the model's alignment with human aesthetics and text fidelity expectations. We conducted an in-depth analysis of these annotations to identify common artifacts in modern text-to-3D generation methods, including (1) disconnected components, (2) Janus artifacts, (3) opacity inconsistencies, (4) floating elements, (5) overly smooth or irregular surfaces, (6) texture seams. Based on these insights, we replicated these artifacts in the 3D mesh data to scale up our dataset and further improve Gen3DEval's performance. Examples of these artifacts are illustrated in the appendix.
+
+Synthetic Data: To expand the training dataset, we applied controlled perturbations using Blender, NeRF, and Gaussian splatting techniques, simulating common artifacts and misalignments found in text-to-3D generative outputs. For 3D meshes, we introduced perturbations such as Laplacian smoothing [19], beveling, random surface extrusions,
+
+![](images/81a1990578a191cd13030036222c7c74b45f35ab4e7b6e53444a80317e43d459.jpg)  
+Figure 3. Training Dataset We use single and multi-view RGB and surface normals renderings of a 3D object generated from a prompt. We take these objects and perturb them to simulate common appearance, surface and text-related artefacts in generative 3D methods.
+
+and texture map alterations like blurring and seam introduction. Additionally, we fitted NeRF and Gaussian splats to the renderings of the artist-created 3D assets, providing a broader foundation for synthetic training data.
+
+To further enrich this dataset, we introduced additional perturbations to the NeRF and Gaussian splatting by manually adding transparency artifacts, floating elements, and disconnected components, mimicking frequent issues observed in text-to-3D generative methods.
+
+To scale the text fidelity comparison dataset, we used the multi-view video diffusion model from IM-3D [33] and trellis [58] to generate single and multiple views of 3D objects with varied captions, focusing on changes to appearance attributes and composition of the objects. Textual perturbations were created with Llama3.2 [2] by prompting the model to modify the original captions, introducing subtle variations. This approach allowed us to generate a large, diverse synthetic dataset tailored for text fidelity comparison. To ensure high relevance and quality, we applied CLIP [42] to filter out examples with low image-text similarity, resulting in a refined synthetic dataset for evaluating text fidelity. Figure 3 showcases some samples of our SFT dataset.
+
+# 4.2. Image Encoders
+
+We evaluated 3 image encoders: CLIP [42], DinoV2 [39], Fit3D [62] and the combinations of CLIP with DinoV2 and CLIP with Fit3D; reshaping them to match the bigger of the two and adding the values. For CLIP embeddings, the effective resolution is  $336 \times 336$  pixels and for both Dinov2 and Fit3D, which internally fine-tune the Dinov2 base model to introduce 3D awareness to image features, the model uses a ViT [12] backbone of patch size 14, effective resolution of  $224 \times 224$  and embedding dimension 768. The results of these ablation experiments are reported in Table 1.
+
+We observe that Gen3DEval performs equally well on synthetic surface assessment in all the configurations with lowest accuracy score of 0.89 in the case of Gen3DEval
+
+w/ Fit3D whereas on user-annotated out-of-domain (OOD) benchmark, CLIP clearly outperforms the rest. On the synthetic appearance benchmark, described in Section 5.2, Gen3DEval w/ Fit3D reports the lowest accuracy of 0.8, with the rest between .85-.89. On the other hand, on the human evaluation appearance dataset (in-domain methods) described in Section 5.1, Gen3DEval w/ CLIP as well as a combination of Fit3D and CLIP report the best accuracy score of 0.9, followed by CLIP and DinoV2 (0.86), then Fit3D (0.81) and finally DinoV2 (0.77). In terms of generalization with OOD benchmarks, Gen3DEval with CLIP outperforms the rest by a large margin.
+
+On text fidelity benchmarks, in keeping with our earlier observation, the performance on OOD text fidelity benchmark is much better for Gen3DEval with CLIP (0.86) alone compared with the rest, with the nearest neighbour in CLIP and DinoV2 (0.74). Finally, on the synthetic text fidelity benchmark, Gen3DEval w/ DinoV2 alone underwhelms reporting 0.75. While standalone numbers for Fit3D is better, CLIP reports high scores by itself as well as in conjunction with Fit3D and DinoV2.
+
+Overall, we noticed that Gen3DEval with CLIP embedding consistently performs well across all evaluation dimensions. As a result of this, Gen3DEval uses CLIP encoder to extract image embeddings.
+
+# 4.3. Stage 1: Pre-training
+
+The objective of this training stage is to train the projection matrix to learn correlations between the image encoding space and the language description space. To train the projection matrix, we use the renderings of the 141,000 3D artist-created meshes and their accompanying text prompts. We sample 40K single view image, 40K two-view images, 40K four-view images, and 10K four-view rendered surface normal images and their corresponding captions. We also combine 11.4K of the four-view images mentioned above and combine them to form an image grid. In the case of
+
+surface normals, we process the captions to remove any aspect of appearance mentioned in them which is irrelevant to rendered normals. All multi-view images are sampled uniformly from a  $360^{\circ}$  azimuth with a fixed elevation angle.
+
+The training process involved a batch size of 16, learning rate of  $1\mathrm{e}^{-3}$ , a cosine learning rate scheduler with a warm-up ratio of 0.03, using the ADAM optimizer. We optimized the model using maximum likelihood for the next token prediction. Pre-training was conducted on 8 A100 GPUs over a period of 1 day, encompassing 8K iterations.
+
+# 4.4. Stage 2: Supervised Fine-tuning
+
+The objective of the supervised fine-tuning (SFT) stage is to jointly train the instruction-following large language model (LLM) and the pre-trained projection matrix for the task of selecting the best 3D object out of two based on text fidelity, 3D appearance and surface quality. For fine-tuning, we utilize the human-annotated data and the synthetically generated comparison data. The SFT dataset distribution is displayed in the appendix.
+
+The fine-tuning process involved a batch size of 4, a learning rate of  $2\mathrm{e}^{-6}$  for the projector, and a learning rate of  $1\mathrm{e}^{-5}$  for the vLLM, with a cosine learning rate scheduler and a warm-up ratio of 0.03, using the ADAM optimizer. We optimized the model by using maximum likelihood of next token prediction. This stage was trained on 16 A100 GPUs for 18 hours, for 4K iterations.
+
+# 5. Experiments
+
+We evaluated the performance of Gen3DEval using three distinct datasets. Their details are explained in the following subsections. We also performed ablation studies to explore the impact of different image encoders on the performance of Gen3DEval. This involved varying the types of image encoders and delineating their respective contributions to the overall performance of the model in Section 4.2.
+
+# 5.1. Human Evaluation Dataset
+
+The purpose of the human evaluation dataset is to assess the alignment of Gen3DEval with human preference on text fidelity, appearance and surface quality. We curated this dataset in the same way as we curated the human preference data for the supervised fine-tuning stage of training in Section 4.1 under Human Annotations. Post curation and processing, we split the total data by holding out  $10\%$  of the total prompts (404) for the creation of this dataset and using the rest  $(90\%)$  for the supervised fine-tuning stage of training. This dataset has 506 VQA comparison data samples for a total of 40 prompts, annotated on the basis of appearance. We also added 3 evaluation datasets, one each for appearance, surface and text fidelity generated from annotating pairwise evaluation and removing any ambiguities from methods as well as prompts that were not used as part
+
+of the training data. We use these to calculate out-of-domain (OOD) generalization performance of our Gen3DEval.
+
+# 5.2. Synthetic Dataset
+
+The second dataset is a synthetic evaluation dataset that includes objects generated by 3D artists along with their synthetically perturbed counterparts, enabling controlled experimentation in a low-noise environment. We created and processed this dataset in the same way as we processed the synthetic dataset for appearance, surface and textual perturbations detailed in Section 4.1 under Synthetic Data. Given that the VQA training is so diverse, we further ensured no overlap with training captions and objects by filtering the evaluation dataset using a sentence similarity threshold using sentence transformer embeddings [50]. We also curated a portion of the synthetic evaluation dataset by applying surface perturbations to artist-drawn meshes and rendering the surface normals of these pairs of objects. All the filtering mechanisms were similarly applied to this dataset.
+
+# 5.3. Benchmark Details
+
+We create a diverse set of 80 prompts, Gen3DEval-Bench, which considers the diversity of objects, textures, and levels of composition. We determine the size of this benchmark with the consideration that text-to-3D generation is a time- and computation-intensive process, aiming to make the benchmark easily accessible. It is split between 40 animate (humanoids, animals) objects and 40 inanimate objects, as well as into 43 single object and 37 composite object prompts, i.e., combining multiple objects. The average number of words per prompt is 12.863. Refer to the supplementary for comparison with other prompt benchmarks.
+
+# 5.4. Metric Computation
+
+To compute the metric, Gen3DEval compares two methods at a time using the following procedure. First, it samples four images from a  $360^{\circ}$  RGB or surface normal video circling the object, at equal intervals covering a  $360^{\circ}$  view of the 3D asset per prompt per method for a pair of methods. For each prompt in Gen3DEval-Bench and each pair of methods, Gen3DEval is applied to a pair of assets at a time. It takes 8 input images (4 per object) alongwith the relevant QA prompt and parses the natural language output using [2] to determine which 3D asset is better. Subsequently, it applies the ELO rating system to the parsed outputs and extracts an overall ranking metric. The generation prompt is only provided in the case of evaluation on text fidelity and not for appearance and surface. We treat them as separate tasks which enables us to compare any two generated assets, irrespective of the generated prompts. It also allows us to evaluate image-to-3D methods more effectively.
+
+![](images/80a145baa19f955c22487283a90590c97cb3ea484c0847c00ae867b423830708.jpg)  
+Figure 4. Qualitative Comparison of methods on samples of the evaluation dataset across text fidelity, appearance and surface evaluation.
+
+# 6. Results
+
+# 6.1. Gen3DEval and other evaluator methods
+
+We compare Gen3DEval against classical baseline metrics such as CLIP as well state-of-the-art vLLMs on evaluation datasets described in Section 5.1 and Section 5.2. We show that the model outperforms all the current methods on assessing appearance preference by a large margin on synthetic, user preference and out-of-domain evaluation data, demonstrating a strong correlation with human preference in the context of text-to-3D asset generation. In terms of text fidelity, Gen3DEval outperforms CLIP [42], which is the most popular metric for text fidelity evaluation. Gen3DEval also narrowly outperforms ImageReward [59] and PickScore [26] on the out-of-domain benchmark which has been curated to remove any ambiguous samples. We also outperform our baselines of surface comparisons data using only synthetically perturbed surface normals.
+
+Moreover, Gen3DEval is the first method that unifies text-to-3D generation metrics by incorporating appearance as well as text fidelity metrics in a holistic manner as evidenced in Table 1. We also provide a qualitative comparison of Gen3DEval with other methods on a few samples from the evaluation dataset in Figure 4.
+
+We also report results for ablation studies for choice of image encoders used for the instruction tuning stage in Table 1. We note from the ablations that CLIP embeddings have a more consistent performance across all dimensions for the purpose of our metric.
+
+Finally, we note that while user studies or user preference data is the current gold standard, it can be noisy and uncorrelated. For instance, when it comes to text fidelity comparison, sometimes, the preference is influenced by appearance. Moreover, with short or simple generation prompts, it is difficult to pick one over the other. In case of appearance comparison, sometimes, the background or scale can influence our choice.
+
+# 6.2. Generative 3D Methods on Gen3DEval-Bench
+
+Table 2 notes the results of Gen3DEval on Gen3DEval-Bench for a collection of 10 generative 3D methods, namely, Trellis [58], AssetGen [47], Fantasia3D [9], TripoSR [51], Magic123 [41], Magic3d [28], Vfusion-3d [15], Dreamfusion [40], LatentNerf [35] and Flex3D [16]. We rank them in the order of their performance for appearance, surface quality and text fidelity. The overall ranking is an average of the scores of appearance and text fidelity.
+
+# 6.3. Limitations
+
+Gen3DEval's assessment of objects with janus can be slightly erratic. There is room for improvement for out-of-domain performance for surface evaluation, because of limited availability of diverse, annotated surface comparison data. We also note that for image-to-3D generation methods, the performance of methods are inherently influenced by a text-to-image generation pipeline. Therefore, composing a strong and consistent image benchmark would be a logical next step. While Gen3DEval can be used for the pur
+
+<table><tr><td></td><td colspan="3">Appearance</td><td colspan="2">Surface</td><td colspan="2">T-Fidelity</td></tr><tr><td></td><td>Human</td><td>Synthetic</td><td>OOD</td><td>Synthetic</td><td>OOD</td><td>Synthetic</td><td>OOD</td></tr><tr><td>Classical</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Avg. CLIP Score [42]</td><td>0.3</td><td>0.4</td><td>0.17</td><td>0.3</td><td>0.45</td><td>0.78</td><td>0.8</td></tr><tr><td>Avg. Image Reward Score [59]</td><td>0.73</td><td>0.6</td><td>0.66</td><td>0.7</td><td>0.54</td><td>0.65</td><td>0.85</td></tr><tr><td>Avg. PickScore [26]</td><td>0.37</td><td>0.25</td><td>0.34</td><td>0.26</td><td>0.21</td><td>0.81</td><td>0.85</td></tr><tr><td>Vision Large Language models</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Phi-3.5-Vision [1]</td><td>0.53</td><td>0.47</td><td>0.54</td><td>0.49</td><td>0.5</td><td>0.64</td><td>0.65</td></tr><tr><td>LLaVA-Qwen-7B [29]</td><td>0.54</td><td>0.46</td><td>0.54</td><td>0.51</td><td>0.46</td><td>0.68</td><td>0.58</td></tr><tr><td>LLaVA-Llama3-8b [29]</td><td>0.5</td><td>0.5</td><td>0.47</td><td>0.47</td><td>0.48</td><td>0.49</td><td>0.5</td></tr><tr><td>Llama3.2-Vision-11B* [2]</td><td>0.06</td><td>0.04</td><td>0.05</td><td>0.1</td><td>0.07</td><td>0.04</td><td>0.5</td></tr><tr><td>BLIP* [27]</td><td>0.05</td><td>0.28</td><td>0.2</td><td>0.07</td><td>0.09</td><td>0.37</td><td>0.13</td></tr><tr><td>GPT-4o* [38]</td><td>0.59</td><td>0.48</td><td>0.69</td><td>0.54</td><td>0.54</td><td>0.61</td><td>0.55</td></tr><tr><td>PaliGemma* [7]</td><td>0.02</td><td>0.02</td><td>0.21</td><td>0.25</td><td>0.25</td><td>0.17</td><td>0.1</td></tr><tr><td>Gen3DEval (CLIP)</td><td>0.9</td><td>0.85</td><td>0.89</td><td>0.99</td><td>0.67</td><td>0.98</td><td>0.86</td></tr><tr><td>Gen3DEval (CLIP + Fit3D)</td><td>0.9</td><td>0.88</td><td>0.78</td><td>0.97</td><td>0.57</td><td>1</td><td>0.53</td></tr><tr><td>Gen3DEval (CLIP + DinoV2)</td><td>0.86</td><td>0.89</td><td>0.78</td><td>0.99</td><td>0.51</td><td>0.98</td><td>0.74</td></tr><tr><td>w/ Fit3D</td><td>0.81</td><td>0.8</td><td>0.55</td><td>0.89</td><td>0.44</td><td>0.93</td><td>0.44</td></tr><tr><td>w/ DinoV2</td><td>0.77</td><td>0.87</td><td>0.54</td><td>0.97</td><td>0.61</td><td>0.75</td><td>0.58</td></tr></table>
+
+Table 1. We report accuracy for curated synthetic and out-of-domain human preference evaluation datasets for appearance, surface and fidelity to text. Additionally, for appearance, we compare methods on in-domain (unseen prompts from methods used for training data). We compare our method against classical metric methods as well as other vLLMs. For text fidelity, we do not provide prompts or pass empty strings for the classical methods. Methods with * next to their names do not currently support multi-image input and were passed either 4x2 grids composed of eight images or 8 input images in sequence in case of GPT-4o.  
+
+<table><tr><td>Methods</td><td>Appear.</td><td>Surf.</td><td>T-Fidelity</td><td>Overall</td></tr><tr><td>Trellis* [58]</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>AssetGen [47]</td><td>2</td><td>7</td><td>2</td><td>2</td></tr><tr><td>Flex3d* [16]</td><td>4</td><td>N/A</td><td>3</td><td>3</td></tr><tr><td>Latentnerf [35]</td><td>3</td><td>N/A</td><td>6</td><td>4</td></tr><tr><td>Magic123 [41]</td><td>5</td><td>4</td><td>4</td><td>5</td></tr><tr><td>Vfusion-3d* [15]</td><td>6</td><td>8</td><td>5</td><td>6</td></tr><tr><td>Magic3d [28]</td><td>7</td><td>5</td><td>7</td><td>7</td></tr><tr><td>Dreamfusion [40]</td><td>8</td><td>2</td><td>8</td><td>8</td></tr><tr><td>Fantasia3D [9]</td><td>N/A</td><td>3</td><td>N/A</td><td>N/A</td></tr><tr><td>TripoSR [51]</td><td>N/A</td><td>6</td><td>N/A</td><td>N/A</td></tr></table>
+
+Table 2. Gen3DEval applied to 3D generation methods on Gen3DEval-Bench. Methods are ranked (Best, Worst) on text fidelity, appearance and surface quality score (if available). Only appearance and text fidelity are used for the overall score. Imago-to-3D methods are denoted with * .
+
+pose of evaluating any given pair of generated 3D objects, for the purpose of being used as a standard evaluation metric, comprehensive application across numerous methods is relevant to its performance; examples in the appendix.
+
+# 7. Conclusion
+
+In this paper, we have laid out the lack of an existing metric in the text-to-3D domain that caters to all its necessary pa
+
+rameters and established the relevance for developing such a metric. It is a difficult problem to solve because of the many ways in which 3D generation is supported. Diverging from the trend of using similarity metrics, which is impractical in the case of text-to-3D generation, we propose introducing 3D aesthetic preference into the vision-language space and transforming that into a ready-to-use evaluation ranking metric using vLLMs. We demonstrate that Gen3DEval, a vLLM containing 8.35 billion parameters and trained using a synthetically curated 3D-object dataset coupled with user preference data establishes itself as a comprehensive, accessible and competitive evaluation method displaying strong performance on relevant metric dimensions, i.e., appearance, texture and text fidelity, in alignment with user 3D object preference. We hope that Gen3DEval will provide a standard benchmark and metric for the comparison of existing and future methods.
+
+# Acknowledgements
+
+We thank Antoine Toisoul for help in curating the synthetic data for our experiments. Shalini Maiti has been supported by a sponsored research award from Meta.
+
+# References
+
+[1] Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah, Ammar Ahmad Awan, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, et al. Phi-3 technical report: A highly capable language model locally on your phone. arXiv preprint arXiv:2404.14219, 2024. 8  
+[2] Meta AI. The llama 3 herd of models, 2024. 2, 3, 4, 5, 6, 8  
+[3] Anthropic. 3  
+[4] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou, and Tianhang Zhu. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023. 2  
+[5] Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sagnak Tasirlar. Introducing our multimodal models, 2023. 2, 3  
+[6] Raphael Bensadoun, Yanir Kleiman, Idan Azuri, Omri Harosh, Andrea Vedaldi, Natalia Neverova, and Oran Gafni. Meta 3d texturegen: Fast and consistent texture generation for 3d objects. arXiv preprint arXiv:2407.02430, 2024. 4  
+[7] Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, et al. Paligemma: A versatile 3b vlm for transfer. arXiv preprint arXiv:2407.07726, 2024. 8  
+[8] Jake Bruce, Michael D Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, et al. Genie: Generative interactive environments. In *Forty-first International Conference on Machine Learning*, 2024. 4  
+[9] Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 22246–22256, 2023. 7, 8  
+[10] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objverse-xl: A universe of  $10\mathrm{m} + 3\mathrm{d}$  objects. arXiv preprint arXiv:2307.05663, 2023. 2  
+[11] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A universe of annotated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13142-13153, 2023. 2  
+[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner,
+
+Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words. arXiv preprint arXiv:2010.11929, 7, 2020.5  
+[13] Arpad E Elo and Sam Sloan. The rating of chessplayers: Past and present. (No Title), 1978. 2  
+[14] Ruiqi Gao*, Aleksander Holynski*, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, and Ben Poole*. Cat3d: Create anything in 3d with multi-view diffusion models. Advances in Neural Information Processing Systems, 2024. 2  
+[15] Junlin Han, Filippos Kokkinos, and Philip Torr. Vfusion3d: Learning scalable 3d generative models from video diffusion models, 2024. 2, 4, 7, 8  
+[16] Junlin Han, Jianyuan Wang, Andrea Vedaldi, Philip Torr, and Filippos Kokkinos. Flex3d: Feed-forward 3d generation with flexible reconstruction model and input view curation. arXiv preprint arXiv:2410.00890, 2024. 4, 7, 8  
+[17] Yuze He, Yushi Bai, Matthieu Lin, Wang Zhao, Yubin Hu, Jenny Sheng, Ran Yi, Juanzi Li, and Yong-Jin Liu. T3bench: Benchmarking current progress in text-to-3d generation, 2023. 3  
+[18] Zexin He and Tengfei Wang. Openlrm: Open-source large reconstruction models. https://github.com/3DTopia/OpenLRM, 2023.4  
+[19] Leonard R Herrmann. Laplacian-isoparametric grid generation scheme. Journal of the Engineering Mechanics Division, 102(5):749-756, 1976. 4  
+[20] 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. Advances in neural information processing systems, 30, 2017. 1  
+[21] Lukas Hollein, Aljaž Božić, Norman Müller, David Novotny, Hung-Yu Tseng, Christian Richardt, Michael Zollhöfer, and Matthias Nießner. Viewdiff: 3d-consistent image generation with text-to-image models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 2  
+[22] Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400, 2023. 2  
+[23] Yukun Huang, Jianan Wang, Yukai Shi, Boshi Tang, Xianbiao Qi, and Lei Zhang. Dreamtime: An improved optimization strategy for diffusion-guided 3d generation. arXiv preprint arXiv:2306.12422, 2023. 2  
+[24] Wonbong Jang and Lourdes Agapito. Nvist: In the wild new view synthesis from a single image with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10181-10193, 2024. 2  
+[25] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42 (4), 2023. 1, 2  
+[26] Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy. Pick-a-pic: an open dataset
+
+of user preferences for text-to-image generation. In Proceedings of the 37th International Conference on Neural Information Processing Systems, Red Hook, NY, USA, 2023. Curran Associates Inc. 7, 8  
+[27] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In ICML, 2022. 2, 3, 8  
+[28] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 300–309, 2023. 2, 7, 8  
+[29] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning, 2023. 2, 3, 4, 8  
+[30] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. arXiv preprint arXiv:2311.07885, 2023. 4  
+[31] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Generating multiview-consistent images from a single-view image. arXiv preprint arXiv:2309.03453, 2023. 2  
+[32] Yuxin Liu, Minshan Xie, Hanyuan Liu, and Tien-Tsin Wong. Text-guided texturing by synchronized multi-view diffusion. arXiv preprint arXiv:2311.12891, 2023. 4  
+[33] Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, and Filippos Kokkinos. Im-3d: Iterative multiview diffusion and reconstruction for high-quality 3d generation. arXiv preprint arXiv:2402.08682, 2024.5  
+[34] Meshy. Meshy text-to-3D v3.0, 2024. 4  
+[35] Gal Metzer, Elad Richardson, Or Patashnik, Raja Giryes, and Daniel Cohen-Or. Latent-nerf for shape-guided generation of 3d shapes and textures. arXiv preprint arXiv:2211.07600, 2022. 7, 8  
+[36] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part I, page 405-421, Berlin, Heidelberg, 2020. Springer-Verlag. 1, 2  
+[37] Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), pages 3501 - 3512, Piscataway, NJ, 2020. IEEE. 2  
+[38] OpenAI. Gpt-4 technical report. 2023. 2, 3, 8  
+[39] Maxime Oquab, Timothee Darcet, Théo Moutakanni, Huy Q. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russ Howes, Po-Yao (Bernie) Huang, Shang-Wen Li, Ishan Misra,
+
+Michael G. Rabbat, Vasu Sharma, Gabriel Synnaeve, Huijiao Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. Dinov2: Learning robust visual features without supervision. *ArXiv*, abs/2304.07193, 2023. 4, 5  
+[40] Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv, 2022. 2, 3, 4, 7, 8  
+[41] Guocheng Qian, Jinjie Mai, Abdullah Hamdi, Jian Ren, Aliaksandr Siarohin, Bing Li, Hsin-Ying Lee, Ivan Skorokhodov, Peter Wonka, Sergey Tulyakov, and Bernard Ghanem. Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors. In The Twelfth International Conference on Learning Representations (ICLR), 2024. 2, 7, 8  
+[42] 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, 2021. 1, 2, 4, 5, 7, 8  
+[43] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models, 2021. 1  
+[44] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Lit, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Raphael Gontijo-Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi. Photorealistic text-to-image diffusion models with deep language understanding. In Proceedings of the 36th International Conference on Neural Information Processing Systems, Red Hook, NY, USA, 2024. Curran Associates Inc. 4  
+[45] Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, and Hao Su. Zero123++: a single image to consistent multi-view diffusion base model, 2023. 2  
+[46] Yichun Shi, Peng Wang, Jianglong Ye, Long Mai, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv:2308.16512, 2023. 2, 4  
+[47] Yawar Siddiqui, Tom Monnier, Filippos Kokkinos, Mahendra Kariya, Yanir Kleiman, Emilien Garreau, Oran Gafni, Natalia Neverova, Andrea Vedaldi, Roman Shapovalov, and David Novotny. Meta 3d assetgen: Text-to-mesh generation with high-quality geometry, texture, and pbr materials. arXiv, 2024. 2, 4, 7, 8  
+[48] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. arXiv preprint arXiv:2309.16653, 2023. 2  
+[49] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 3  
+[50] Nandan Thakur, Nils Reimers, Johannes Daxenberger, and Iryna Gurevych. Augmented SBERT: Data augmentation
+
+method for improving bi-encoders for pairwise sentence scoring tasks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 296–310, Online, 2021. Association for Computational Linguistics. 6  
+[51] Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang, Adam Letts, Yangguang Li, Ding Liang, Christian Laforte, Varun Jampani, and Yan-Pei Cao. Triposr: Fast 3d object reconstruction from a single image. arXiv preprint arXiv:2403.02151, 2024. 4, 7, 8  
+[52] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models, 2023. 3  
+[53] Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A. Yeh, and Greg Shakhnarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. arXiv preprint arXiv:2212.00774, 2022. 2  
+[54] Peng Wang and Yichun Shi. Imagedream: Image-prompt multi-view diffusion for 3d generation. arXiv preprint arXiv:2312.02201, 2023. 2, 4  
+[55] Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4): 600-612, 2004. 1, 2  
+[56] Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: high-fidelity and diverse text-to-3d generation with variational score distillation. In Proceedings of the 37th International Conference on Neural Information Processing Systems, Red Hook, NY, USA, 2024. Curran Associates Inc. 2  
+[57] Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. Gpt-4v (ision) is a human-aligned evaluator for text-to-3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22227–22238, 2024. 2, 3  
+[58] Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. arXiv preprint arXiv:2412.01506, 2024. 5, 7, 8  
+[59] Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: learning and evaluating human preferences for text-to-image generation. In Proceedings of the 37th International Conference on Neural Information Processing Systems, Red Hook, NY, USA, 2023. Curran Associates Inc. 7, 8  
+[60] Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, and Yaron Lipman. Multiview neural surface reconstruction by disentangling geometry and appearance. Advances in Neural Information Processing Systems, 33, 2020. 2  
+[61] Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang
+
+Wang. Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models. In CVPR, 2024. 2  
+[62] Yuanwen Yue, Anurag Das, Francis Engelmann, Siyu Tang, and Jan Eric Lenssen. Improving 2D Feature Representations by 3D-Aware Fine-Tuning. In European Conference on Computer Vision (ECCV), 2024. 4, 5  
+[63] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 2  
+[64] Junzhe Zhu, Peiye Zhuang, and Oluwasanmi Koyejo. Hifa: High-fidelity text-to-3d generation with advanced diffusion guidance. In International Conference on Learning Representations, 2023. 2

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CVPR/2025/GenAssets_ Generating in-the-wild 3D Assets in Latent Space/full.md

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+# GenAssets: Generating in-the-wild 3D Assets in Latent Space
+
+Ze Yang $^{1,2}$  Jingkang Wang $^{1,2}$  Haowei Zhang $^{1}$   
+Sivabalan Manivasagam $^{1,2}$  Yun Chen $^{1,2}$  Raquel Urtasun $^{1,2}$ $^{1}$ Waabi  $^{2}$ University of Toronto
+
+{zyang, jwang, hzhang, siva, ychen, urtasun}@waabi.ai
+
+![](images/1355d6fe82ad9a3bc3277061315e7c7506c63224b5a239c7fa00882d9ab819bb.jpg)  
+Figure 1. GenAssets takes in-the-wild camera image(s) and point cloud(s), and automatically reconstruct or generate  $360^{\circ}$  assets. Our 3D assets are diverse and high-quality with complete geometry and appearance, allowing for realistic and scalable sensor simulation.
+
+![](images/6aa2a7b5d6f85b57ffc3ff3b89dcc1808f158827eafd55814053273210b66a20.jpg)
+
+# Abstract
+
+High-quality 3D assets for traffic participants are critical for multi-sensor simulation, which is essential for the safe end-to-end development of autonomy. Building assets from in-the-wild data is key for diversity and realism, but existing neural-rendering based reconstruction methods are slow and generate assets that render well only from viewpoints close to the original observations, limiting their usefulness in simulation. Recent diffusion-based generative models build complete and diverse assets, but perform poorly on in-the-wild driving scenes, where observed actors are captured under sparse and limited fields of view, and are partially occluded. In this work, we propose a 3D latent diffusion model that learns on in-the-wild LiDAR and camera data captured by a sensor platform and generates high-quality 3D assets with complete geometry and appearance. Key to our method is a "reconstruct-then-generate" approach that first leverages occlusion-aware neural rendering trained over multiple scenes to build a high-quality latent space for objects, and then trains a diffusion model that operates on the latent space. We show our method outperforms existing reconstruction and generation based methods, unlocking diverse and scalable content creation for simulation. Please visit https://waabi.ai/genassets for more details.
+
+# 1. Introduction
+
+Simulation environments enable testing the performance of autonomy systems in long-tail and safety critical scenarios safely, efficiently, and scalably [41, 61, 77, 97]. To test the complete autonomy stack, the virtual environments should simulate the sensor data (e.g., LiDAR, camera) of the robot. Realistic and consistent sensor simulation across multiple modalities depends on the availability of high-quality 3D assets that accurately represent the geometry and appearance of traffic participants such as cars and motorcycles.
+
+Commoditized simulation environments rely on artists to manually build such 3D assets [14, 59, 62]. This process is slow, costly, and requires precise specification of attributes such as geometry, UV mapping, and materials. As a consequence, existing methods utilize very limited content, failing to capture the full diversity of objects in the real world.
+
+An alternative and much more appealing approach that has gained traction is to reconstruct assets from real world data captured by the sensor platform, which enables diversity and realism. Reconstruction-based neural rendering methods have demonstrated impressive results by optimizing 3D representations that can render and match input sensor data for self-driving scenes [11, 15, 29, 70, 74, 86, 93, 94, 97, 103]. These approaches render high-quality data
+
+when the viewpoint is close to the training views, but can suffer from severe artifacts at novel viewpoints due to incomplete observations. Additionally, each asset must be observed in the real world and reconstructed via optimization, making it computationally expensive and limiting diversity.
+
+Recent 3D generative models [6, 7, 17, 47, 57, 63] have shown promise in generating complete assets and rendering novel views far from source. These methods leverage a combination of generative adversarial networks (GANs), diffusion models, and neural rendering, and train on large corpuses of images to learn shape and appearance priors. NeRF-based diffusion models that optimize via score-distillation sampling (SDS) [34, 52], or denoise in 3D space [9, 20, 45, 48, 85], have demonstrated particularly high-quality complete shape and appearance generation, and also offer controllability through text/image conditioning. However, such models usually rely on synthetic datasets with ground-truth 3D models to render the dense 2D views necessary to learn a 3D representation prior. They also primarily work on "clean" images that fully capture the object of interest. This reliance on clean synthetic data limits their ability to handle in-the-wild real data from a moving platform, where the data is typically captured from sparse viewpoints, under partial occlusions, limited resolution, and with sensor noise (e.g., lighting artifacts, rolling shutter).
+
+In this work, we propose a latent diffusion 3D generative model that learns directly from sparse, in-the-wild data, enabling high-quality asset generation at scale. We tackle the challenges of asset completion and generative modelling via a two-stage "reconstruct-then-generate" methodology. In the first stage, we learn a low-dimensional object latent space that generates complete assets by training across multiple scenes via neural rendering. To handle partial occlusion, we jointly learn a scene representation for both the static scene and dynamic actors, enabling occlusion-aware rendering during learning. In the second stage, we train a diffusion model to operate on the 3D latent space, enabling realistic asset generation that can be conditioned on individual views, day or night, or the actor class, enabling asset variations that are applicable to new scenes. We evaluate our model on the public self-driving dataset PandaSet [87], demonstrating its capability to reconstruct and generate high-quality assets from sparse, in-the-wild data. Additionally, we showcase applications such as conditional generation, as well as realistic rendering of our assets for downstream simulation systems, highlighting our approach's flexibility, scalability, and practical utility.
+
+# 2. Related Work
+
+3D Reconstruction in the Wild: Reconstructing objects from in-the-wild data is essential for creating 3D digital twins and simulations, but poses significant challenges due to the complexity (e.g., occlusions, sparse viewpoints, par
+
+tial observations) and noise (e.g., localization and calibration inaccuracies [99], sensor noise). Object-based reconstructions fall into optimizing explicit meshes [46, 78, 95] or implicit representations [27, 43, 49, 80, 96, 98, 100] methods via differentiable rendering. However, they rely on accurate object segmentation masks and struggle to handle complex occlusions. To address this issue, recent works propose compositional neural fields [35, 50, 53, 74, 97] or 3D Gaussians [11, 29, 93] to decompose 3D world into static background and dynamic actors. These works often involve costly per-scene optimizations and cannot generalize well to novel views with large shifts nor produce complete (i.e., 360-degree) objects. To mitigate these limitations, recent works propose generalizable reconstruction models [10, 23, 44, 56, 83] that generate a 3D representation with a single feed-forward network trained on many scenes. Another line of work aims to incorporate data-driven priors from pre-trained models (e.g., stable diffusion [58]) via score-distillation sampling (SDS) [25, 52, 81, 84]. However, existing methods either require object-centric synthetic training data [23, 83, 84] (e.g., Objaverse [12]) and/or perform poorly on in-the-wild sensor data [10, 25, 81]. Our approach builds high-fidelity and complete assets by directly learning on real-world data.
+
+GAN-based 3D Generation: Generative Adversarial Networks [19] (GANs) have shown great potential for 3D asset generation [17, 90], where the generator transforms random noise into the target 3D representation, and the discriminator is jointly trained using 2D images rendered from the generated representation and the ground truth. This paradigm allows generation without requiring explicit 3D ground truth. Specifically,  $\pi$ -GAN [6] introduces a generative model for high-quality 3D-aware image synthesis based on neural radiance fields. GIRAFFE [47] further proposes to represent scenes as compositional generative neural feature fields, allowing for better object-background disentanglement and controllable scene generation. EG3D [7] and GET3D [17] further improve 3D generation efficiency and quality by integrating the triplane representation (for volume rendering or explicit mesh rendering) with a StyleGAN [28] architecture. Although they achieve robust and consistent 3D generation, they primarily focus on "clean" object-centric images with full coverage and no occlusions and have difficulties generalizing to in-the-wild driving data. Most recently, DiscoScene [90] and GINA-3D [63] learn 3D generative models from in-the-wild data. However, they often produce significant visual artifacts (e.g., floating particles, low-fidelity shapes and texture). In contrast, our method generates high-quality assets.
+
+Diffusion-based 3D Generation: Diffusion-based methods have achieved tremendous success in image generation
+
+[22, 51, 58, 67, 68], leveraging denoising diffusion probabilistic models [22] to transform white noise into high-quality outputs through iterative refinement. To bridge the gap between 2D and 3D generation, a common strategy involves lifting 2D images to 3D via score distillation sampling (SDS) [18, 25, 34, 52, 64, 71, 81, 84]. These methods rely on pre-trained or fine-tuned 2D diffusion models and optimize 3D representations with diffusion-prior guidance. The optimization is slow and the supervision can be multi-view inconsistent. To address this issue, recent works [32, 36, 37, 39, 64, 79] propose to synthesize multi-view consistent images via diffusion models and then perform 3D reconstruction with explicit meshes [89], neural radiance fields [32, 79] or 3D Gaussians [72, 92]. Another line of work performs diffusion directly in 3D, either by training 3D-aware diffusion model from posed 2D images without requiring explicit 3D supervision [1, 5, 9, 20, 45, 65, 69, 73, 91] or by operating directly on 3D data [38, 40, 75]. Inspired by latent diffusion models (LDM) [58], recent works diffuse within a latent space, where models like UNet [60] or VAEs [30, 76] map 3D data to a compressed latent representation for faster, higher-quality and more scalable synthesis of both objects [57, 85] and large-scale scenes [24, 42, 54, 55, 88, 101]. However, existing works primarily focus on synthetic objects [9, 42] or static scenes [24, 54, 55, 88] and cannot handle complex real-world scenarios. In contrast, we propose a latent diffusion 3D generative model with compositional radiance fields that learns directly from in-the-wild dynamic scenes, enabling high-quality 3D asset generation at scale.
+
+# 3. Method
+
+Our goal is to build a generative model that can create high-fidelity assets scalably for self-driving simulation. The model should enable unconditional generation of diverse assets for a variety of classes (e.g., cars, buses, trucks, pedestrians), and also support generating complete assets conditioned on new in-the-wild camera images and LiDAR point clouds for actors that may be occluded or far-away. Importantly, to avoid the quality degradation [9] that occurs when generative models trained on synthetic data are transferred to real data, our generative model should directly learn over a corpus of real-world driving scenes collected by a robot sensor platform. Towards this goal, we propose a two-stage "reconstruct-then-generate" framework. In the first stage, we jointly learn a set of latent codes through reconstruction-based neural rendering, where each latent code represents a foreground actor in our dataset. We devise a compositional scene representation that enables rendering of actors of driving scenes in an occlusion-aware manner, enabling us to decouple the foreground actors from the background (e.g., road, sky, vegetation). Fig. 2 provides an overview of this process. In the second stage, we train a diffusion model
+
+to learn generative priors in this asset latent space, enabling the generation of realistic and diverse neural assets. This enables both unconditional generation and conditional generation through score-based guidance. Fig. 3 shows the overview of our latent asset diffusion model. We first introduce our compositional scene representation (Sec. 3.1). We then describe how we compress the actor representations into a latent space to learn the set of latent codes via neural rendering (Sec. 3.2), and detail the diffusion process in the latent space and the training process (Sec. 3.3).
+
+# 3.1. Compositional Scene Representation
+
+We aim to learn a generative 3D asset model from real-world driving scenes. Unlike existing object-centric approaches [7, 9, 17, 18, 45, 48, 85], our sensor data contains many actors captured under different viewpoint, distances, and occlusions. To handle multiple instances and their occlusions, we leverage a compositional scene representation. Each scene is decomposed into a static background  $\mathcal{B}$  and a set of dynamic actors  $\{\mathcal{A}_i\}_{i=1}^K$ , with each actor enclosed within a 3D box and localized by a trajectory of  $SE(3)$  poses. We model the static background and dynamic actors with separate neural feature fields parameterized by triplane representations [7]. Specifically, for each dynamic actor's volume, explicit features are aligned along three orthogonal planes, each with resolution  $N_{\mathcal{A}} \times N_{\mathcal{A}} \times C$ , where  $N_{\mathcal{A}}$  is the spatial resolution, and  $C$  is the feature dimension. Similarly, triplane features of resolution  $N_{\mathcal{B}} \times N_{\mathcal{B}} \times C$  represent the background volume. For a spatial query coordinate  $\mathbf{x} \in \mathbb{R}^3$ , we project it onto each of the three feature planes  $\mathbf{t} = (\mathbf{t}^{xy}, \mathbf{t}^{xz}, \mathbf{t}^{yz})$  and bilinearly interpolate the corresponding feature vectors. The interpolated features are concatenated with view direction  $\mathbf{d} \in \mathbb{S}^2$  and processed by an MLP network  $f_{\mathrm{feat}}$  to yield geometry as a signed-distance function (SDF)  $s$  and a neural feature  $\mathbf{f}$ . This querying process is defined by:
+
+$$
+s, \mathbf {f} = f _ {\mathrm {f e a t}} (\{\operatorname {i n t e r p} (\mathbf {x} ^ {p}, \mathbf {t} ^ {p}) \} _ {p \in \{x y, x z, y z \}}, \mathbf {d}), \quad (1)
+$$
+
+where  $\mathbf{x}^p$  represents the 2D projection of  $\mathbf{x}$  onto feature plane  $p$ . To render the scene representation, we first transform the object-centric actor neural feature fields to world coordinates similar to [74, 97]. Then we compose the background and actor feature fields. The composited geometry and appearance features are then rendered into sensor observations through neural volume rendering.
+
+# 3.2. Learning Latent Asset Representations
+
+Encoding to a Latent Space: Per-scene reconstruction methods [74, 93, 97] train each scene separately, with its own set of actor and background triplanes. This prevents effective handling of ambiguities in occluded or unseen regions, resulting in actor representations that generalize poorly to unseen viewpoints. Naively training jointly over
+
+![](images/d6007d770690c8f517ec593fda1d3c06e079812fe73d3844f9b80cff4b5c756c.jpg)  
+Figure 2. Learning latent asset representation. We learn a low-dimensional object latent space that generates complete assets by training across multiple scenes via occlusion-aware neural rendering. The asset decoder is trained to map low-dimension latent codes into neural assets which are then composed with learnable per-scene background models to match real-world sensor observations.
+
+all the explicit triplanes for every actor in the dataset containing tens of thousands of objects requires vast amounts of GPU memory and still does not force the learned triplanes to have complete actor shape and appearance. To address these limitations, we propose learning a latent code  $\mathbf{c}_i\in \mathbb{R}^{n_A\times n_A\times c}$  for each actor representation and a class embedding  $\mathbf{e}_i\in \mathbb{R}^{n_A\times n_A\times c}$ , combined with a shared asset triplane decoder  $f_{\mathrm{dec}}$  that maps the latent code into the triplane representation:
+
+$$
+f _ {\mathrm {d e c}}: \mathbf {e} _ {i}, \mathbf {c} _ {i} \in \mathbb {R} ^ {n _ {A} \times n _ {A} \times c} \rightarrow \mathbf {t} _ {i} \in \mathbb {R} ^ {N _ {A} \times N _ {A} \times 3 C}. \tag {2}
+$$
+
+The class embedding  $\mathbf{e}_i$  has the same spatial resolution as the latent code and is shared among actors of the same class. We concatenate it with the actor latent code before passing it to the decoder. The decoder  $f_{\mathrm{dec}}$  upsamples the latent code by a factor  $f = N_{\mathcal{A}} / n_{\mathcal{A}}$ . The intuition is that different actors are observed from various viewpoints, so their feature planes  $\mathbf{t}_i$  capture different informative regions. By compressing them into a latent code bottleneck, we enable learning shape and appearance priors, allowing inference of invisible parts from sparse observations.
+
+**Rendering Sensor Observations:** Now that we have decoded the asset neural fields  $\{\mathbf{t}_i\}_{i=1}^N$  from the latent codes  $\{\mathbf{c}_i\}_{i=1}^N$ , the next step is to compose them with the background neural fields  $\mathbf{t_B}$  and render into the sensor data. In this work, we focus on camera images and LiDAR point clouds, as they are primary sensory modalities for SDVs.
+
+For camera rendering, we use a hybrid volume and neural rendering framework for both efficiency and realism. Given a camera ray  $\mathbf{r}(t) = \mathbf{o} + h\mathbf{d}$  sent from the camera center  $\mathbf{o}$  to the pixel in direction  $\mathbf{d}$ , we sample 3D points along the ray, querying geometry  $s_t$  and neural feature  $\mathbf{f}_t$  via Eq. (1). We then aggregate these samples to obtain the pixel feature through volume rendering:
+
+$$
+\mathbf {f} (\mathbf {r}) = \sum_ {t = 1} ^ {N _ {t}} w _ {t} \mathbf {f} _ {t}, \quad w _ {i} = \alpha_ {t} \prod_ {j = 1} ^ {t - 1} (1 - \alpha_ {j}), \tag {3}
+$$
+
+where  $\alpha_{t}$  is the opacity for the  $t$ -th point, derived from the SDF  $s_t$  following [74, 97]. We repeat this process to volume render all camera pixels, generating a 2D feature map  $\mathbf{F} \in \mathbb{R}^{H_f \times W_f \times C_f}$ . Then we leverage 2D CNN network  $f_{\mathrm{rgb}}$  [97] to convert this feature map into an image  $\mathbf{I}_{\mathrm{rgb}}$ :
+
+$$
+f _ {\mathrm {r g b}}: \mathbf {F} \in \mathbb {R} ^ {H _ {f} \times W _ {f} \times C _ {f}} \rightarrow \mathbf {I} _ {\mathrm {r g b}} \in \mathbb {R} ^ {H \times W \times 3}. \tag {4}
+$$
+
+The CNN network  $f_{\mathrm{rgb}}$  upsamples the rendered map from resolution  $H_{f} \times W_{f}$  to  $H \times W$ . This allows us to significantly reduce the number of neural feature field queries. This approach also enhances model capacity and improves image quality by capturing spatial relationships effectively.
+
+In addition to camera images, we use LiDAR rendering for additional geometry supervision. LiDAR sensors emit laser pulses and measure the time of flight to determine distances to reflective surfaces. This depth information provides valuable supervision for asset geometry. Using similar notation, we define  $\mathbf{r}(t) = \mathbf{o} + h\mathbf{d}$  as a ray cast from the LiDAR sensor center  $\mathbf{o}$  in the direction  $\mathbf{d}$ . We render LiDAR depth similarly to Eq. (3) by aggregating sample depths via volume rendering:  $D(\mathbf{r}) = \sum_{t=1}^{N_t} w_t h_t$ , where  $h_t$  is the depth of the  $t$ -th sampled point, and  $w_t$  is the sample weight computed as in Eq. (3).
+
+Learning: We jointly optimize the asset code  $\{\mathbf{c}_i\}_{i=1}^{N_c}$ , class embedding  $\{\mathbf{e}_i\}_{i=1}^{N_e}$ , background neural fields  $\mathbf{t}_B$ , asset decoder  $f_{\mathrm{dec}}$ , neural feature fields MLP  $f_{\mathrm{feat}}$ , and RGB CNN network  $f_{\mathrm{rgb}}$  by minimizing the differences between rendered and observed sensor data. To achieve high-fidelity reconstruction and rendering, we incorporate perceptual and patch-based adversarial objectives. We also regularize the latent space by applying a Kullback-Leibler (KL) penalty. Our full objective is:
+
+$$
+\mathcal {L} = \mathcal {L} _ {\mathrm {r g b}} + \lambda_ {\text {p e r p}} \mathcal {L} _ {\text {p e r p}} + \lambda_ {\text {a d v}} \mathcal {L} _ {\text {a d v}} + \lambda_ {\text {l i d}} \mathcal {L} _ {\text {l i d}} + \lambda_ {\mathrm {K L}} \mathcal {L} _ {\mathrm {K L}}, \tag {5}
+$$
+
+where  $L_{\mathrm{rgb}}$  and  $L_{\mathrm{perp}}$  represent the  $\ell_2$  photometric loss and the perceptual loss [102] between rendered and observed images,  $\mathcal{L}_{\mathrm{adv}}$  is the adversarial objective using patchGAN [26],  $\mathcal{L}_{\mathrm{lid}}$  is the  $\ell_1$  depth loss between rendered and observed LiDAR points, and  $\mathcal{L}_{\mathrm{KL}}$  is the injected KL penalty
+
+![](images/b7d4ce8951ce8b025cbe1ce6dc8230b5a4fed289b035b974b29e777dc2574251.jpg)  
+Figure 3. Left: Training asset diffusion model in latent space. Right: Sampling diffusion model for (un)conditional neural asset generation.
+
+![](images/e0fa0c0a58f2f98f78dc4b42b6a5420867851b5a1709a4384d428832d08d8ec4.jpg)
+
+guiding the latent space towards a standard normal distribution, similar to [30, 58]:  $\mathcal{L}_{\mathrm{KL}} = \frac{1}{2}\| \pmb{\mu}_i^2 + \pmb{\sigma}_i^2 - 1 - \log (\pmb{\sigma}_i^2)\|_1$ , where  $\pmb{\mu}_i$  and  $\pmb{\sigma}_i$  represent the mean and standard deviation components of latent code  $\mathbf{c}_i$ , i.e.,  $\mathbf{c}_i = \pmb{\mu}_i^2 + \pmb{\sigma}_i \odot \pmb{\epsilon}$  with  $\pmb{\epsilon} \sim \mathcal{N}(\mathbf{0}, \mathbf{I})$ . This regularization prevents overfitting and encourages the latent space to be compact, continuous, smooth, and low-variance, enabling easier learning of generative models. Similar to [16, 90], we introduce additional perceptual losses on object patches for enhanced object-level supervision, where the patches are obtained by projecting the 3D instance boxes onto the image plane. Please refer to supp. for more details.
+
+# 3.3. Latent Asset Diffusion Model
+
+Given our learned asset code library  $\{\mathbf{c}_i\}_{i = 1}^N$  from the dataset, we aim to learn generative priors using a diffusion model. Diffusion models [22, 66] are probabilistic frameworks that model the data distribution by progressively denoising a Gaussian noise variable. In our approach, the diffusion model operates directly in the latent space and begins with Gaussian noise, progressively denoising it to recover the underlying latent distribution. The forward or diffusion process is a discrete-time Markov chain that iteratively adds Gaussian noise to the latent code  $\mathbf{c}^{(0)}$ , producing progressively noisier codes following the transition probability  $q(\mathbf{c}^{(t)}|\mathbf{c}^{(t - 1)}) = \mathcal{N}(\sqrt{1 - \beta^{(t)}}\mathbf{c}^{(t - 1)},\beta^{(t)}\mathbf{I})$ , according to a noise schedule  $\beta^{(t)}$ . After  $T$  steps, the code approximates pure Gaussian noise. This forward process can be directly sampled at timestep  $t$  using closed-form expression:
+
+$$
+\mathbf {c} ^ {(t)} = \sqrt {\bar {\alpha} ^ {(t)}} \mathbf {c} ^ {(0)} + \sqrt {1 - \bar {\alpha} ^ {(t)}} \boldsymbol {\epsilon}, \tag {6}
+$$
+
+where  $\bar{\alpha}^{(t)} = \prod_{s=1}^{t} \alpha^{(s)}$  with  $\alpha^{(t)} = 1 - \beta^{(t)}$ , and  $\epsilon \sim \mathcal{N}(\mathbf{0}, \mathbf{I})$  is drawn from a Gaussian distribution. The goal of the diffusion model is to learn the reverse process, progressively denoising the noisy code to recover a clean version. We parameterize the denoising network  $f_{\mathrm{diff}}$  as a U-Net [60], which takes the noisy code  $\mathbf{c}^{(t)}$  and timestep  $t$  as inputs, predicting the noise estimate  $f_{\mathrm{diff}}(\mathbf{c}^{(t)}, t)$  and removing it to yield a denoised code. The denoising network is trained with a weighted  $\ell_2$  objective:
+
+$$
+\mathcal {L} _ {\text {d i f f}} = \mathbb {E} _ {\mathbf {c}, \boldsymbol {\epsilon}, t} \left[ \frac {1}{2} w ^ {(t)} \| f _ {\text {d i f f}} \left(\mathbf {c} ^ {(t)}, t\right) - \boldsymbol {\epsilon} \| _ {2} ^ {2} \right], \tag {7}
+$$
+
+where  $\epsilon \sim \mathcal{N}(\mathbf{0},\mathbf{I})$  is standard Gaussian noise,  $t\sim \mathcal{U}(1,T)$  is uniformly sampled from the interval  $[1,T]$ , and  $\mathbf{c}^{(t)}$  is derived from  $\mathbf{c}^{(0)}$  (Eq. (6)), where  $\mathbf{c}^{(0)}\sim \{\mathbf{c}_i\}_{i = 1}^N$  is sampled from the asset code library. The term  $w^{(t)}$  provides time-dependent weighting. Learning the diffusion process in the latent space, rather than in high-dimensional triplane space like prior work [2, 9, 45, 65, 85], offers key advantages for likelihood-based generative modeling by: (i) focusing on essential contents of the data and (ii) operating in a computationally efficient, compact space.
+
+Unconditional Asset Generation: Unconditional asset generation involves generating a latent code using the learned diffusion model  $f_{\mathrm{diff}}$  and then decoding it into neural assets using the learned asset decoder  $f_{\mathrm{dec}}$ . Sampling from the diffusion prior can be performed with various solvers (e.g., DDPM [22], DDIM [67]), similar to methods used in image generation. Taking DDIM [67] for example, we begin with a random Gaussian code  $\mathbf{c}^{(T)} \sim \mathcal{N}(\mathbf{0}, \mathbf{I})$  and iteratively denoise it over  $T$  steps until reaching  $t = 0$ :
+
+$$
+\mathbf {c} ^ {(t - 1)} = \sqrt {\alpha^ {(t - 1)}} \left(\frac {\mathbf {c} ^ {(t)} - \sqrt {\beta^ {(t)}} \tilde {\boldsymbol {\epsilon}}}{\sqrt {\alpha^ {(t)}}}\right) + \sqrt {\beta^ {(t - 1)}} \tilde {\boldsymbol {\epsilon}}, \tag {8}
+$$
+
+$$
+\text {s . t .} \tilde {\boldsymbol {\epsilon}} = f _ {\text {d i f f}} \left(\mathbf {c} ^ {(t)}, t\right), \tag {9}
+$$
+
+where  $\tilde{\epsilon}$  is the noise estimate at timestep  $t$  from the denoising network  $f_{\mathrm{diff}}$ . The final denoised code  $\mathbf{c}^{(0)}$  is a sample from the learned latent distribution. We then pass this code to the asset decoder to generate the asset neural fields,  $\mathbf{t} = f_{\mathrm{dec}}(\mathbf{c}^{(0)})$ , for scene composition and rendering.
+
+Conditional / Guided Assets Generation: For conditional generation, we aim to model the conditional distribution of the latent space  $p(\mathbf{c}|y)$ . This can be achieved with a conditional denoising network  $f_{\mathrm{diff}}(\mathbf{c}^{(t)}, t, y)$ , with condition signals such as actor class and time-of-day. As shown in Fig. 1, classifier-free diffusion guidance enables effective control over asset generation. Alternatively, for some inverse problems such as image-to-3D, conditional generation can be approximated from an unconditional model using classifier guidance [13, 68], leveraging gradients of the rendering loss w.r.t. known observations. This flexibility re
+
+moves the need for separate models tailored to different inverse problems. Given camera image and/or LiDAR point cloud of a new actor to reconstruct, during the sampling process (Eq. (8)) we compute the gradient of the rendering loss with respect to the current code:  $\mathbf{g} = \nabla_{\mathbf{c}^{(t)}}\left(\mathcal{L}_{\mathrm{rgb}} + \lambda_{\mathrm{lid}}\mathcal{L}_{\mathrm{lid}}\right)$  and then steer the sampling process by updating the noisy code similar to [9, 45]:
+
+$$
+\hat {\mathbf {c}} ^ {(t - 1)} \leftarrow \mathbf {c} ^ {(t - 1)} - \lambda \cdot \mathbf {g}, \tag {10}
+$$
+
+where  $\mathbf{c}^{(t - 1)}$  is computed following Eq. (8) and  $\hat{\mathbf{c}}^{(t - 1)}$  is the updated code;  $\lambda$  is a small guidance weight.
+
+# 4. Experiments
+
+In this section, we first describe our experimental setup. We then compare our model to state-of-the-art reconstruction methods across multiple novel view synthesis settings. Next, we compare to SoTA generative models for 3D generation. Finally, we demonstrate conditional generation from different classes, single-image, and appearance (day/night), and show that the generated assets enhance downstream detection performance. Please refer to the supp. for implementation details and ablation of our model components.
+
+# 4.1. Experimental Setup
+
+Dataset: We conduct experiments on the PandaSet [87] dataset, which includes 103 driving scenes captured in San Francisco, with each scene spanning 8 seconds (80 frames at  $10\mathrm{hz}$ ). The data collection platform features a  $360^{\circ}$  mechanical spinning LiDAR, a forward-facing LiDAR, along with six  $1920\times 1080$  cameras surrounding the vehicles. Object bounding boxes are provided with the dataset. As our focus is object reconstruction and generation, we extract object segmentation masks for metric evaluation. Specifically, we leverage 3D object boxes and LiDAR points to identify actor patches in the camera images, and then apply a visual foundation model [31] to generate instance masks for the actors. Our method does not require instance masks for learning. We employ them strictly to evaluate foreground-only metrics. Please see supp. for details.
+
+Baselines: We compare our method with several state-of-the-art reconstruction and generation approaches. For reconstruction, we benchmark against per-scene NeRF-based NeuRAD [74] and 3D Gaussian Splatting-based Street Gaussians [93], as well as generalizable reconstruction methods G3R [10] and PixelSplat [8]. During evaluation, these methods take as input all source views for the full scene, except for PixelSplat, which can only handle two views. For generation, we evaluate against the GAN-based EG3D [7] and DiscoScene [90], as well as diffusion-based SSDNeRF [9]. As EG3D and SSDNeRF are object-centric models, we train them on our object-centric benchmark with instance masks.
+
+# 4.2. Reconstruction Evaluation
+
+Evaluation Settings and Metrics: We evaluate our method across three challenging settings: (1) Sparse view synthesis: Using every 10th frame for training and the remaining frames for testing, with both training and testing frames captured from the front camera. (2) Novel camera synthesis: Training on frames from the front camera and evaluating on frames from the front-left camera. (3)  $360^{\circ}$  View synthesis: Rotating actors  $(0^{\circ} - 360^{\circ})$  to simulate various behaviors, evaluated on front camera views. We select 7 diverse scenes to assess reconstruction performance, with the remaining 96 scenes for learning generalizable priors. For sparse view synthesis and novel camera synthesis, we report PSNR, SSIM [82], and LPIPS [102]. For  $360^{\circ}$  view synthesis, since no ground truth images are available, we report FID metrics [21]. As PixelSplat [8] is designed to reconstruct with two input views, we only evaluate it on the sparse view synthesis setting.
+
+Sparse View Synthesis: We first compare our method against state-of-the-art approaches for sparse view synthesis in Tab. 1. Our method outperforms the baselines across all metrics. Unlike prior methods that uses  $90\%$  frames [11, 94],  $75\%$  frames [93] or  $50\%$  frames [10, 74, 97] for training, our setup is more challenging, relying on only  $10\%$  of frames for training. We found that baseline methods struggle to learn robust geometry, leading to severe artifacts in novel viewpoints. Qualitative results in Fig. 4 (top) show that our method renders camera images more accurately.
+
+Novel Camera Synthesis: We compare our method against SoTA for novel camera synthesis in Tab. 1. A visual comparison is provided in Fig. 4 (middle). All baseline methods effectively reconstruct observed camera data (front camera). However, for new sensor poses (front-left camera), previously unobserved regions become visible, requiring the model to "hallucinate" these areas. For instance, the front-right part of the SUV in Fig. 4 (middle) is invisible in the front camera but becomes visible in front-left camera views. Reconstruction methods fail on these regions. Our method, by learning generative priors, is capable of rendering complete and high-quality simulations in this setting.
+
+$360^{\circ}$  View Synthesis: Sensor simulation requires not only simulating novel viewpoints but also synthesizing entirely new scenarios. Consider a four-way intersection with a car crossing in front of you from a perpendicular lane. To explore a situation where that car suddenly turns sharply into your lane, we must render actors in significantly different poses than those seen in the original scene. To that end, we investigate a  $360^{\circ}$  view synthesis setting by rotating actors in the scene from  $0^{\circ}$  to  $360^{\circ}$ , simulating various
+
+![](images/0fe09ef11ca02842de980202ad0187a695aa53df72b458523891168a9da6aff0.jpg)  
+Figure 4. Top: Sparse view synthesis. GenAssets generalizes well on this extreme setting thanks to low-dimensional latent space learned across many scenes, while the SoTA reconstruction methods are less robust and produce noticeable visual artifacts (e.g., missing, blurry or distorted appearance). Middle: Novel camera synthesis. We train on frames from the front camera and evaluate on frames from the front-left camera. Our method generates assets that have better extrapolation results, while retaining high quality in the original view. Bottom:  $360^{\circ}$  view synthesis. Through our multi-scene training and latent space, our method enables higher-fidelity asset completion for different object orientations compared to SoTA reconstruction-based methods.
+
+<table><tr><td rowspan="2">Methods</td><td colspan="3">Sparse View Synthesis</td><td colspan="3">Novel Camera Synthesis</td><td>360° View Synthesis</td></tr><tr><td>PSNR↑</td><td>SSIM↑</td><td>LPIPS↓</td><td>PSNR↑</td><td>SSIM↑</td><td>LPIPS↓</td><td>FID↓</td></tr><tr><td>PixelSplat* [8]</td><td>17.67</td><td>0.704</td><td>0.336</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>G3R [10]</td><td>18.37</td><td>0.711</td><td>0.255</td><td>17.40</td><td>0.723</td><td>0.221</td><td>191.92</td></tr><tr><td>Street Gaussian [93]</td><td>20.01</td><td>0.763</td><td>0.156</td><td>17.81</td><td>0.724</td><td>0.226</td><td>162.37</td></tr><tr><td>NeuRAD [74]</td><td>21.07</td><td>0.825</td><td>0.129</td><td>17.49</td><td>0.723</td><td>0.214</td><td>159.38</td></tr><tr><td>Ours</td><td>21.34</td><td>0.825</td><td>0.113</td><td>18.36</td><td>0.805</td><td>0.147</td><td>100.28</td></tr></table>
+
+Table 1. Quantitative comparison with SoTA reconstruction approaches on PandaSet. We evaluate on sparse view synthesis (10% frames for training and remaining frames for testing), novel camera synthesis (training on front camera and testing on front-left camera), and  $360^{\circ}$  view synthesis (rotating actors from  $0^{\circ}$  to  $360^{\circ}$ ). GenAssets outperforms existing SoTA per-scene or generalizable reconstruction approaches across all settings. PixelSplat* [8] (2 input views) only for sparse view synthesis evaluation.
+
+orientations. Since ground-truth images are unavailable, we report FID [21] in Tab. 1. Qualitative results in Fig. 4 (bottom) show that our method effectively hallucinates unseen parts of the actors, whereas all baselines struggle to do so.
+
+# 4.3. Generation Evaluation
+
+Evaluation Metrics: To evaluate the generation quality, we report both Fréchet Inception Distance (FID) [21] and Kernel Inception Distance (KID) [3] scores. The metrics are calculated by comparing distributions of 10K generated images with all available images in the validation set, providing a quantitative assessment of similarity in terms of perceptual quality and realism. FID measures the distance between the mean and covariance of feature representations from real and generated image distributions. KID complements FID by measuring differences in feature embeddings
+
+through a kernel-based approach. All generated objects are rendered at a resolution of  $256 \times 256$ .
+
+Unconditional Generation: We compare our method against SoTA GAN-based method EG3D [7] and DiscoScene [90], as well as diffusion-based method SSDNeRF [9] in Tab. 2. All baselines struggle with occlusion, leading to suboptimal assets. In addition, EG3D and DiscoScene suffers from mode collapse, producing limited asset variety (mainly sedans). DiscoScene struggles to differentiate foreground and background and exhibits boundary artifacts, likely due to the absence of LiDAR or mask supervision. SSDNeRF yields blurry results, potentially due to challenges posed by our real-world sparse setting. Our model improves over these baselines across all metrics. Fig. 5 shows a visual comparison.
+
+![](images/0351ecbcc7f851e8d1a22eda20f12eaa410ba315b6536e27a47a1b96565979cf.jpg)  
+Figure 5. Qualitative comparison on unconditional generation. Our methods generate more diverse, complete and higher-quality 3D assets compared to SoTA 3D generative models.
+
+![](images/316ecfb449b4a3304e76b96382eaa40466e80128067c755e932a57ca3f4c572a.jpg)  
+Figure 6. Qualitative results on single-image to 3D.
+
+# 4.4. Applications
+
+Conditional Generation: The flexibility of our framework enables various conditional generation tasks. Specifically, we freeze the learned latent codes and train a conditional diffusion model  $f_{\mathrm{diff}}(\mathbf{c}^{(t)}, t, y)$  using classifier-free guidance. We explore conditioning on fine-grained actor classes and time-of-day (day/night), with results presented in Fig. 1. We can generate complete assets for a variety classes, with intra-class shape and appearance variation.
+
+Single Image to 3D: Our approach also enables generating 3D assets from single-view images using a rendering-guided denoising process (Eq. (10)), where the denoising gradient is directed to minimize rendering loss against the observed image. Fig. 6 shows qualitative results of single-image reconstruction for three nearby vehicles, alongside comparisons to SoTA large reconstruction model MeshLRM [83]. MeshLRM produces distorted shapes and synthetic cartoon-ish appearance for unobserved views. Our approach generates higher quality completion and is consistent with the input. Please refer to supp. for more comparisons with other SoTA large models.
+
+Data Augmentation with GenAssets: We now showcase that our generated assets boost downstream performance when training a BEVFormer [33] 3D object detector (Tab. 3). Specifically, we augment the training dataset by swapping out existing actors with generated ones for the same scene layouts. Please see supp. for more details.
+
+<table><tr><td>Methods</td><td>FID↓</td><td>KID↓</td></tr><tr><td>SSDNeRF [9]</td><td>191.30</td><td>156.13</td></tr><tr><td>DiscoScene [90]</td><td>138.48</td><td>116.27</td></tr><tr><td>EG3D [7]</td><td>80.56</td><td>38.97</td></tr><tr><td>Ours</td><td>59.50</td><td>28.32</td></tr></table>
+
+Table 2. Unconditional generation evaluation on PandaSet. KID scores are multiplied by  $10^{3}$ . GenAssets outperforms SoTA baselines on both metrics and generates higher-quality 3D assets.  
+
+<table><tr><td></td><td>mAP↑</td><td>AP@1m↑</td><td>AP@2m↑</td><td>AP@4m↑</td></tr><tr><td>Real</td><td>27.08</td><td>8.58</td><td>26.99</td><td>45.67</td></tr><tr><td>Real + Sim</td><td>29.32</td><td>9.78</td><td>29.18</td><td>49.00</td></tr></table>
+
+Table 3. Data augmentation with GenAssets helps 3D detection. We report distance-based APs [4] at  $1m$ ,  $2m$  and  $4m$ .
+
+# 5. Conclusion
+
+In this work, we tackled the challenge of generating high-quality and complete assets from in-the-wild LiDAR and camera data captured by a moving sensor platform. Towards this goal, we developed a "reconstructthen-generate" approach where we first learn to reconstruct foreground actors over multiple scenes with compositional scene neural rendering and encode them to a latent space. We then train a diffusion model to operate within this latent space to enable generation. We show our method generates high-quality, complete assets for actors such as vehicles and motorcycles, outperforming both per-scene reconstruction methods and generative models. We also show that our approach can be conditioned for controllable asset generation such as on sparse sensor data, actor class, and time of day, enabling diverse and scalable content creation for simulation. Future work involves generating dynamic lighting and animation, intrinsic decomposition, and adopting more efficient scene representation and rendering techniques.
+
+Acknowledgement: We sincerely thank the Waabi team for their invaluable assistance and support. We also thank the anonymous reviewers for their insightful suggestions.
+
+# References
+
+[1] Titas Anciukevicius, Zexiang Xu, Matthew Fisher, Paul Henderson, Hakan Bilen, Niloy J Mitra, and Paul Guerrero. Renderdiffusion: Image diffusion for 3d reconstruction, inpainting and generation. In CVPR, 2023. 3  
+[2] Miguel Angel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, et al. Gaudi: A neural architect for immersive 3d scene generation. NeurIPS, 2022. 5  
+[3] Mikołaj Binkowski, Danica J Sutherland, Michael Arbel, and Arthur Gretton. Demystifying mmd gans. arXiv, 2018. 7  
+[4] Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In CVPR, 2020. 8  
+[5] Ang Cao, Justin Johnson, Andrea Vedaldi, and David Novotny. Lightplane: Highly-scalable components for neural 3d fields. arXiv, 2024. 3  
+[6] Eric R Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In CVPR, 2021. 2  
+[7] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In CVPR, 2022. 2, 3, 6, 7, 8  
+[8] David Charatan, Sizhe Lester Li, Andrea Tagliasacchi, and Vincent Sitzmann. pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction. In CVPR, 2024. 6, 7  
+[9] Hansheng Chen, Jiatao Gu, Anpei Chen, Wei Tian, Zhuowen Tu, Lingjie Liu, and Hao Su. Single-stage diffusion nef: A unified approach to 3d generation and reconstruction. In ICCV, 2023. 2, 3, 5, 6, 7, 8  
+[10] Yun Chen, Jingkang Wang, Ze Yang, Sivabalan Manivasagam, and Raquel Urtasun. G3r: Gradient guided generalizable reconstruction. In ECCV, 2025. 2, 6, 7  
+[11] Ziyu Chen, Jiawei Yang, Jiahui Huang, Riccardo de Lutio, Janick Martinez Esturo, Boris Ivanovic, Or Litany, Zan Gojcic, Sanja Fidler, Marco Pavone, et al. Omnire: Omni urban scene reconstruction. arXiv, 2024. 1, 2, 6  
+[12] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A universe of annotated 3d objects. In CVPR, 2023. 2  
+[13] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. NeurIPS, 2021. 5  
+[14] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In CoRL, 2017. 1  
+[15] Tobias Fischer, Jonas Kulhanek, Samuel Rota Bulò, Lorenzo Porzi, Marc Pollefeys, and Peter Kontschieder. Dynamic 3d gaussian fields for urban areas. arXiv, 2024. 1
+
+[16] Raghudeep Gadde, Qianli Feng, and Aleix M Martinez. Detail me more: Improving gan's photo-realism of complex scenes. In ICCV, 2021. 5  
+[17] Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler. Get3d: A generative model of high quality 3d textured shapes learned from images. NeurIPS, 2022. 2, 3  
+[18] Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan T Barron, and Ben Poole. Cat3d: Create anything in 3d with multi-view diffusion models. arXiv, 2024. 3  
+[19] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. NeurIPS, 2014. 2  
+[20] Jiatao Gu, Alex Trevithick, Kai-En Lin, Joshua M Susskind, Christian Theobalt, Lingjie Liu, and Ravi Ramamoorthi. Nerfdiff: Single-image view synthesis with nef-guided distillation from 3d-aware diffusion. In ICML, 2023. 2, 3  
+[21] 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. 6, 7  
+[22] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NeurIPS, 2020. 3, 5  
+[23] Yicong Hong, Kai Zhang, Jiuming Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. LRM: Large reconstruction model for single image to 3d. arXiv, 2023. 2  
+[24] Qianjiang Hu, Zhimin Zhang, and Wei Hu. Rangeldm: Fast realistic lidar point cloud generation. In ECCV, 2025. 3  
+[25] Sungwon Hwang, Min-Jung Kim, Taewoong Kang, Jayeon Kang, and Jaegul Choo. VEGS: View extrapolation of urban scenes in 3d gaussian splattering using learned priors. arXiv, 2024. 2, 3  
+[26] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with conditional adversarial networks. In CVPR, 2017. 4  
+[27] Wonbong Jang and Lourdes Agapito. Codenerf: Disentangled neural radiance fields for object categories. In ICCV, 2021. 2  
+[28] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR, 2019. 2  
+[29] Mustafa Khan, Hamidreza Fazlali, Dhruv Sharma, Tongtong Cao, Dongfeng Bai, Yuan Ren, and Bingbing Liu. Autosplat: Constrained gaussian splattering for autonomous driving scene reconstruction. arXiv, 2024. 1, 2  
+[30] Diederik P Kingma. Auto-encoding variational bayes. arXiv, 2013. 3, 5  
+[31] 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, 2023. 6  
+[32] Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg
+
+Shakhnarovich, and Sai Bi. Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. arXiv, 2023. 3  
+[33] Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Yu Qiao, and Jifeng Dai. Bevformer: Learning bird's-eye-view representation from multi-camera images via spatiotemporal transformers. In ECCV, 2022. 8  
+[34] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In CVPR, 2023. 2, 3  
+[35] Jeffrey Yunfan Liu, Yun Chen, Ze Yang, Jingkang Wang, Sivabalan Manivasagam, and Raquel Urtasun. Neural scene rasterization for large scene rendering in real time. In ICCV, 2023. 2  
+[36] Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund Varma T, Zexiang Xu, and Hao Su. One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization. NeurIPS, 2024. 3  
+[37] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot one image to 3d object. In ICCV, 2023. 3  
+[38] Zhen Liu, Yao Feng, Michael J Black, Derek Nowrouzezahrai, Liam Paull, and Weiyang Liu. Meshdiffusion: Score-based generative 3d mesh modeling. arXiv, 2023. 3  
+[39] Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. In CVPR, 2024. 3  
+[40] Shitong Luo and Wei Hu. Diffusion probabilistic models for 3d point cloud generation. In CVPR, 2021. 3  
+[41] Sivabalan Manivasagam, Ioan Andrei Bársan, Jingkang Wang, Ze Yang, and Raquel Urtasun. Towards zero domain gap: A comprehensive study of realistic lidar simulation for autonomy testing. In ICCV, 2023. 1  
+[42] Quan Meng, Lei Li, Matthias Nießner, and Angela Dai. Lt3sd: Latent trees for 3d scene diffusion. arXiv, 2024. 3  
+[43] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 2021. 2  
+[44] Norman Müller, Andrea Simonelli, Lorenzo Porzi, Samuel Rota Bulò, Matthias Nießner, and Peter Kontschieder. Autorf: Learning 3d object radiance fields from single view observations. In CVPR, 2022. 2  
+[45] Norman Müller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bulo, Peter Kontschieder, and Matthias Nießner. Diffrf: Rendering-guided 3d radiance field diffusion. In CVPR, 2023. 2, 3, 5, 6  
+[46] Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müller, and Sanja Fidler. Extracting triangular 3d models, materials, and lighting from images. In CVPR, 2022. 2
+
+[47] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. In CVPR, 2021. 2  
+[48] Evangelos Ntavelis, Aliaksandr Siarohin, Kyle Olszewski, Chaoyang Wang, Luc V Gool, and Sergey Tulyakov. Autodecoding latent 3d diffusion models. NeurIPS, 2023. 2, 3  
+[49] Michael Oechsle, Songyou Peng, and Andreas Geiger. Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In ICCV, 2021. 2  
+[50] Julian Ost, Fahim Mannan, Nils Thuerey, Julian Knodt, and Felix Heide. Neural scene graphs for dynamic scenes. In CVPR, 2021. 2  
+[51] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. In ICLR, 2024. 3  
+[52] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv, 2022. 2, 3  
+[53] Ava Pun, Gary Sun, Jingkang Wang, Yun Chen, Ze Yang, Sivabalan Manivasagam, Wei-Chiu Ma, and Raquel Urtasun. Neural lighting simulation for urban scenes. In NeurIPS, 2023. 2  
+[54] Haoxi Ran, Vitor Guizilini, and Yue Wang. Towards realistic scene generation with lidar diffusion models. In CVPR, 2024. 3  
+[55] Xuanchi Ren, Jiahui Huang, Xiaohui Zeng, Ken Museth, Sanja Fidler, and Francis Williams. Xcube: Large-scale 3d generative modeling using sparse voxel hierarchies. In CVPR, 2024. 3  
+[56] Xuanchi Ren, Yifan Lu, Hanxue Liang, Zhangjie Wu, Huan Ling, Mike Chen, Sanja Fidler, Francis Williams, and Jiahui Huang. SCube: Instant large-scale scene reconstruction using voxsplats. arXiv, 2024. 2  
+[57] Barbara Roessle, Norman Müller, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Angela Dai, and Matthias Nießner. L3dg: Latent 3d gaussian diffusion. arXiv, 2024. 2, 3  
+[58] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022. 2, 3, 5  
+[59] Guodong Rong, Byung Hyun Shin, Hadi Tabatabaee, Qiang Lu, Steve Lemke, Martins Mozeiko, Eric Boise, Geehoon Uhm, Mark Gerow, and Shalin Mehta. Lgsvl simulator: A high fidelity simulator for autonomous driving. In ITSC, 2020. 1  
+[60] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, 2015. 3, 5  
+[61] Jay Sarva, Jingkang Wang, James Tu, Yuwen Xiong, Sivaban Manivasagam, and Raquel Urtasun. Adv3d: Generating safety-critical 3d objects through closed-loop simulation. In CoRL, 2023. 1  
+[62] Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. AirSim: High-fidelity visual and physical simul
+
+tion for autonomous vehicles. In Field and service robotics, 2018. 1  
+[63] Bokui Shen, Xinchen Yan, Charles R Qi, Mahyar Najibi, Boyang Deng, Leonidas Guibas, Yin Zhou, and Dragomir Anguelov. Gina-3d: Learning to generate implicit neural assets in the wild. In CVPR, 2023. 2  
+[64] Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv, 2023. 3  
+[65] J Ryan Shue, Eric Ryan Chan, Ryan Po, Zachary Ankner, Jiajun Wu, and Gordon Wetzstein. 3d neural field generation using triplane diffusion. In CVPR, 2023. 3, 5  
+[66] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In ICML, 2015. 5  
+[67] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv, 2020. 3, 5  
+[68] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv, 2020. 3, 5  
+[69] Stanislaw Szymanowicz, Christian Rupprecht, and Andrea Vedaldi. Viewset diffusion:(0-) image-conditioned 3d generative models from 2d data. In ICCV, 2023. 3  
+[70] Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul P Srinivasan, Jonathan T Barron, and Henrik Kretzschmar. Block-nerf: Scalable large scene neural view synthesis. In CVPR, 2022. 1  
+[71] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. arXiv, 2023. 3  
+[72] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. In ECCV, 2025. 3  
+[73] Ayush Tewari, Tianwei Yin, George Cazenavette, Semon Rezchikov, Josh Tenenbaum, Frédo Durand, Bill Freeman, and Vincent Sitzmann. Diffusion with forward models: Solving stochastic inverse problems without direct supervision. NeurIPS, 2023. 3  
+[74] Adam Tonderski, Carl Lindstrom, Georg Hess, William Ljungbergh, Lennart Svensson, and Christoffer Petersson. NeuRAD: Neural rendering for autonomous driving. In CVPR, 2024. 1, 2, 3, 4, 6, 7  
+[75] Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis, et al. Lion: Latent point diffusion models for 3d shape generation. NeurIPS, 2022. 3  
+[76] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. NeurIPS, 2017. 3  
+[77] Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, and Raquel Urtasun. Advsim: Generating safety-critical scenarios for self-driving vehicles. In CVPR, 2021. 1  
+[78] Jingkang Wang, Sivabalan Manivasagam, Yun Chen, Ze Yang, Ioan Andrei Bársan, Anqi Joyce Yang, Wei-Chiu Ma, and Raquel Urtasun. CADSim: Robust and scalable in-the-wild 3d reconstruction for controllable sensor simulation. In 6th Annual Conference on Robot Learning, 2022. 2
+
+[79] Peng Wang and Yichun Shi. Imagedream: Image-prompt multi-view diffusion for 3d generation. arXiv, 2023. 3  
+[80] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv, 2021. 2  
+[81] Qitai Wang, Lue Fan, Yuqi Wang, Yuntao Chen, and Zhaoxiang Zhang. FreeVS: Generative view synthesis on free driving trajectory. arXiv, 2024. 2, 3  
+[82] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. TIP, 2004. 6  
+[83] Xinyue Wei, Kai Zhang, Sai Bi, Hao Tan, Fujun Luan, Valentin Deschaintre, Kalyan Sunkavalli, Hao Su, and Zexiang Xu. MeshLRM: Large reconstruction model for high-quality mesh. arXiv, 2024. 2, 8  
+[84] Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P Srinivasan, Dor Verbin, Jonathan T Barron, Ben Poole, et al. Reconfusion: 3d reconstruction with diffusion priors. In CVPR, 2024. 2, 3  
+[85] Shuang Wu, Youtian Lin, Feihu Zhang, Yifei Zeng, Jingxi Xu, Philip Torr, Xun Cao, and Yao Yao. Direct3d: Scalable image-to-3d generation via 3d latent diffusion transformer. arXiv, 2024. 2, 3, 5  
+[86] Zirui Wu, Tianyu Liu, Liyi Luo, Zhide Zhong, Jianteng Chen, Hongmin Xiao, Chao Hou, Haozhe Lou, Yuantao Chen, Runyi Yang, et al. Mars: An instance-aware, modular and realistic simulator for autonomous driving. In CAAI International Conference on Artificial Intelligence, 2023. 1  
+[87] Pengchuan Xiao, Zhenlei Shao, Steven Hao, Zishuo Zhang, Xiaolin Chai, Judy Jiao, Zesong Li, Jian Wu, Kai Sun, Kun Jiang, et al. Pandaset: Advanced sensor suite dataset for autonomous driving. In ITSC, 2021. 2, 6  
+[88] Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang, and Raquel Urtasun. Learning compact representations for lidar completion and generation. In CVPR, 2023. 3  
+[89] 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, 2024. 3  
+[90] Yinghao Xu, Menglei Chai, Zifan Shi, Sida Peng, Ivan Skorokhodov, Aliaksandr Siarohin, Ceyuan Yang, Yujun Shen, Hsin-Ying Lee, Bolei Zhou, et al. Discoscope: Spatially disentangled generative radiance fields for controllable 3d-aware scene synthesis. In CVPR, 2023. 2, 5, 6, 7, 8  
+[91] Yinghao Xu, Hao Tan, Fujun Luan, Sai Bi, Peng Wang, Jiahao Li, Zifan Shi, Kalyan Sunkavalli, Gordon Wetzstein, Zexiang Xu, et al. Dmv3d: Denoising multi-view diffusion using 3d large reconstruction model. arXiv, 2023. 3  
+[92] Yinghao Xu, Zifan Shi, Wang Yifan, Hansheng Chen, Ceyuan Yang, Sida Peng, Yujun Shen, and Gordon Wetzstein. Grm: Large gaussian reconstruction model for efficient 3d reconstruction and generation. arXiv, 2024. 3  
+[93] Yunzhi Yan, Haotong Lin, Chenxu Zhou, Weijie Wang, Haiyang Sun, Kun Zhan, Xianpeng Lang, Xiaowei Zhou, and Sida Peng. Street gaussians for modeling dynamic urban scenes. In ECCV, 2024. 1, 2, 3, 6, 7
+
+[94] Jiawei Yang, Boris Ivanovic, Or Litany, Xinshuo Weng, Seung Wook Kim, Boyi Li, Tong Che, Danfei Xu, Sanja Fidler, Marco Pavone, et al. Emernerf: Emergent spatial-temporal scene decomposition via self-supervision. arXiv, 2023. 1, 6  
+[95] Ze Yang, Sivabalan Manivasagam, Ming Liang, Bin Yang, Wei-Chiu Ma, and Raquel Urtasun. Recovering and simulating pedestrians in the wild. In Conference on Robot Learning, 2021. 2  
+[96] Ze Yang, Shenlong Wang, Sivabalan Manivasagam, Zeng Huang, Wei-Chiu Ma, Xinchen Yan, Ersin Yumer, and Raquel Urtasun. S3: Neural shape, skeleton, and skinning fields for 3d human modeling. In CVPR, 2021. 2  
+[97] Ze Yang, Yun Chen, Jingkang Wang, Sivabalan Manivasagam, Wei-Chiu Ma, Anqi Joyce Yang, and Raquel Urtasun. Unisim: A neural closed-loop sensor simulator. In CVPR, 2023. 1, 2, 3, 4, 6  
+[98] Ze Yang, Sivabalan Manivasagam, Yun Chen, Jingkang Wang, Rui Hu, and Raquel Urtasun. Reconstructing objects in-the-wild for realistic sensor simulation. In ICRA, 2023. 2  
+[99] Ze Yang, George Chen, Haowei Zhang, Kevin Ta, Ioan Andrei Bársan, Daniel Murphy, Sivabalan Manivasagam, and Raquel Urtasun. Unical: Unified neural sensor calibration. In ECCV, 2025. 2  
+[100] Jason Zhang, Gengshan Yang, Shubham Tulsiani, and Deva Ramanan. NeRS: Neural reflectance surfaces for sparse-view 3d reconstruction in the wild. NeurIPS, 2021. 2  
+[101] Lunjun Zhang, Yuwen Xiong, Ze Yang, Sergio Casas, Rui Hu, and Raquel Urtasun. Copilot4d: Learning unsupervised world models for autonomous driving via discrete diffusion. arXiv, 2023. 3  
+[102] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 4, 6  
+[103] Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, and Ming-Hsuan Yang. Drivinggaussian: Composite gaussian splatting for surrounding dynamic autonomous driving scenes. In CVPR, 2024. 1

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CVPR/2025/GenDeg_ Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration/b4fb985e-0447-4144-8fd3-3575bae6c402_origin.pdf

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CVPR/2025/GenDeg_ Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration/full.md

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+# GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration
+
+Sudarshan Rajagopalan, Nithin Gopalakrishnan Nair, Jay N. Paranjape and Vishal M. Patel  
+Johns Hopkins University
+
+{sambasa2,ngopala2，jparanj1，vpatel36}@jhu.edu
+
+# Abstract
+
+Deep learning-based models for All-In-One image Restoration (AIOR) have achieved significant advancements in recent years. However, their practical applicability is limited by poor generalization to samples outside the training distribution. This limitation arises primarily from insufficient diversity in degradation variations and scenes within existing datasets, resulting in inadequate representations of real-world scenarios. Additionally, capturing large-scale real-world paired data for degradations such as haze, lowlight, and raindrops is often cumbersome and sometimes infeasible. In this paper, we leverage the generative capabilities of latent diffusion models to synthesize high-quality degraded images from their clean counterparts. Specifically, we introduce GenDeg, a degradation and intensity-aware conditional diffusion model, capable of producing diverse degradation patterns on clean images. Using GenDeg, we synthesize over 550k samples across six degradation types: haze, rain, snow, motion blur, low-light, and raindrops. These generated samples are integrated with existing datasets to form the GenDS dataset, comprising over 750k samples. Our experiments reveal that image restoration models trained on GenDS dataset exhibit significant improvements in out-of-distribution performance as compared to when trained solely on existing datasets. Furthermore, we provide comprehensive analyses on implications of diffusion model-based synthetic degradations for AIOR.
+
+# 1. Introduction
+
+Image restoration is a well-studied computer vision problem that aims to reverse the effects of image corruptions or artifacts. It is important for numerous applications, including autonomous driving, imaging and surveillance. Early approaches focused on handling specific degradations such as haze [16, 58], rain [19, 47, 52, 53],
+
+![](images/159e8888c5326d708fd744fd1094cb8b7067a886e4c3831a39804903a62aeea2.jpg)  
+Figure 1. Out-of-distribution performance of three image restoration models when trained solely using existing datasets and our proposed GenDS dataset. Significant improvements can be observed across all degradations. Metric values reduce outward.
+
+snow [11, 59], blur [34, 42] etc. More recent methods such as Restormer [57], MPRNet [56] and SwinIR [27] proposed architectures capable of addressing any single restoration task. However, these approaches are limited to addressing one type of degradation at a time, making them inefficient for scenarios involving multiple types of corruptions.
+
+All-In-One Restoration (AIOR) methods overcome this limitation by employing a single model capable of handling multiple types of degradations. Recent approaches include PromptIR [35], DA-CLIP [32], DiffUIR [62], DiffPlugin [29], InstructIR [13] and AutoDIR [18]. Most AIOR methods are trained using a single dataset for each restoration task such as RESIDE [23] for dehazing, Snow100k [30] for desnowing, Rain13K [56] for deraining and GoPro [34] dataset for motion deblurring. Although these approaches perform well on degradations from these dataset distributions, they often exhibit poor generalization when confronted with new scenes or out-of-distribution (OoD) degradation patterns, which is very common in real-world scenarios. Recent studies have discussed the problem of generalization in image restoration models in great depth [17, 22]. We hypothesize that the limited generalization of these models is mainly due to two reasons:
+
+1. Lack of large datasets with real degradations under diverse scenes. We consider the degradations haze, snow, rain, raindrop, motion blur and low-light. Fig. 2 shows the number of synthetic and real images for each degradation from its existing publicly available datasets, along with the number of unique scenes. To the best of our knowledge, we have included most of the existing datasets. Firstly, the figure shows that existing restoration datasets are significantly smaller than those used to train generalizable models for other low-level vision tasks, such as SAM [21] for segmentation and Depth-Anything [51] for depth estimation ( $>1.5\mathrm{M}$  samples). This limited dataset size hinders the generalization of models to diverse real-world scenarios. Secondly, degradations such as haze, raindrop, low-light and snow have very few real images compared to synthetic ones due to challenges in capturing real images under these conditions. For instance, haze is an atmospheric phenomenon which is difficult to simulate in real scenarios. Conversely, motion blur and rain have more real-world examples as they can be generated from existing videos [15, 26, 34, 42, 47]. Thirdly, degradations such as haze, raindrop and low-light have very limited scene diversity which can further limit the generalization of models. Finally, the number of samples across different degradations is highly imbalanced.
+
+2. Lack of variety in degradation patterns within datasets. Previously, we analyzed the distribution of samples for each degradation. Examining individual datasets, especially synthetic ones, reveals that they contain degradations generated using only a particular model. For instance, the images in the RESIDE [23] dataset are generated by the atmospheric haze model [7] with specific parameters. Consequently, training a network on such a dataset can tailor it to work only for the degradation patterns of that dataset, limiting its generalization capability for real-world dehazing.
+
+Thus, AIOR methods often overfit to training distributions, limiting generalization to real-world samples. To address this, we aim to develop robust AIOR models capable of generalizing to OoD restoration. We define OoD samples as those from test sets, whose corresponding training sets are not used. Within-distribution test sets refer to test splits of the datasets used for training (also referred to as existing training data). To support these definitions, we compute the Wasserstein Distance (WaD) between DA-CLIP [32]'s degradation feature distributions for OoD test sets and the existing training datasets for each degradation (see Table 1). The mean WaD between within-distribution test sets and their training sets is 0.084, which is much lower than the WaD for OoD test sets, validating our definition. Achieving good OoD performance requires large-scale training data with diverse degradations. Since collecting real-world data
+
+![](images/080b864616fae45555c5b0c8debd856228f74a136c81024e4b1066985c79689b.jpg)  
+Figure 2. Analysis of real and synthetic image restoration datasets for various degradations. Existing datasets are small and less diverse, especially for haze, low-light, and raindrop. Our diffusion-generated synthetic data substantially increases the number of samples as well as scene diversity.
+
+Table 1. Wasserstein distance between the degradation feature distributions of DA-CLIP [32] for existing training data and OoD test sets.  
+
+<table><tr><td>Distance</td><td>Haze</td><td>Rain</td><td>Snow</td><td>Motion</td><td>Low-light</td><td>Raindrop</td></tr><tr><td>Wasserstein</td><td>0.212</td><td>0.287</td><td>0.289</td><td>0.173</td><td>0.349</td><td>0.177</td></tr></table>
+
+for all the degradations is infeasible, we propose a novel degradation generation framework that leverages the generative capabilities of Latent Diffusion Models (LDMs) [40] to synthesize diverse degradations. Specifically, we introduce GenDeg, a Stable Diffusion-based model conditioned on text prompts, clean images and degradation intensity to produce images under various degradations.
+
+We train GenDeg by combining multiple existing datasets for each degradation type to ensure that it does not heavily rely on a specific degradation pattern or physical model. Thus, it can produce both synthetic and realistic degradations, thereby enriching the diversity of degradation patterns in the generated data. Furthermore, GenDeg offers fine-grained control over the intensity and spatial variations of generated degradations by conditioning on the mean  $(\mu)$  and standard deviation  $(\sigma)$  of the degradation map. Using GenDeg, we synthesize over 550k degraded images from roughly 120k clean images. We augment existing restoration datasets (used to train GenDeg) with our generated images to create a dataset, GenDS, with over 750k paired images under haze, rain, snow, motion blur, low-light and raindrop degradations. GenDS provides a significant boost in scene diversity and number of samples (see Fig. 2), resulting in substantial improvements in OoD performance of AIOR models (see Fig. 1). Fig. 3 illustrates that the diverse degradations in the GenDS dataset help bridge the domain gap between existing and OoD datasets (see Sec. 4.2 for
+
+![](images/fbebfd535215d8dbb6885a89275a25cd255005250687655f45ffb68f3afe9454.jpg)  
+DA-CLIP degradation features of hazy samples from training, GenDS and OoD datasets.  
+Figure 3. t-SNE visualization of degradation features obtained for hazy samples from existing training data, GenDS dataset and OoD test sets. The features were obtained using DA-CLIP [32].
+
+more details). Additionally, GenDS consists of the same clean images under different degradations that, to the best of our knowledge, is the first such dataset.
+
+Finally, we train five models on the GenDS dataset, namely NAFNet [10], PromptIR [35], a Swin Transformer-based model that we propose, DA-CLIP [32] and Diff-Plugin [29]. Our experiments demonstrate that these models achieve significant improvements in their generalization performance when trained on our large-scale dataset.
+
+In summary, our contributions are as follows:
+
+1. We propose a novel diffusion model-based degradation generation framework, GenDeg, which is capable of producing diverse degradations on any clean image.  
+2. Using GenDeg, we synthesize over 550k degraded images which when combined with existing datasets forms the comprehensive GenDS dataset comprising approximately 750k samples across highly diverse scenes. Furthermore, each image in GenDS has multiple degraded versions, making it, to the best of our knowledge, the first restoration dataset of its kind.  
+3. Finally, we train restoration models on the GenDS dataset and demonstrate that incorporating our synthetic data significantly improves the out-of-distribution restoration capabilities of these networks.
+
+# 2. Related Works
+
+In this section, we discuss relevant works on all-in-one image restoration and diffusion models for synthetic data. Related works on diffusion models are given in supplementary.
+
+# 2.1. All-in-one image restoration
+
+All-In-One restoration (AIOR) methods employ a single model to address multiple corruptions. Early approaches include All-in-one [25], which used neural architecture search, and Transweather [46], which unified multiple encoders for efficient multi-weather restoration. Airtnet and [12] used contrastive loss to learn degradation repre
+
+sentations while PromptIR [35] utilized learnable prompts. Recent approaches have leveraged diffusion models for AIOR. DA-CLIP [32] used degradation information from CLIP [37] to guide diffusion-based image restoration. DiffPluginin [29] adopts task plugins to guide a latent diffusion model for restoration. DiffUIR [62] proposed selective hourglass mapping and AutoDIR [18] developed an automatic approach using vision-language models for degradation detection and restoration. InstructIR [13] utilized text guidance as instructions for AIOR while AWRaCLe [38] used visual in-context learning. Despite these advancements, no prior work (to the best of our knowledge) has explored using diffusion models to generate degradations. Our approach enables the creation of large datasets with realistic degradations to train generalizable AIOR models.
+
+# 2.2. Diffusion models for synthetic data
+
+Diffusion models have shown great potential for synthetic data generation. Methods such as Dreambooth [41] and Distribution Matching Distillation [54] focus on improving the generative process using diffusion-generated data, rather than creating paired synthetic data for downstream tasks. Sim2real techniques such as EPE [39] translate simulation images into realistic ones but do not synthesize degraded images for restoration. Recent research has focused on utilizing latent diffusion models for synthetic data generation tailored to specific tasks. [3, 4, 43, 44, 55] used diffusion-generated images to improve classification and zero-shot classification performance. However, classification tasks do not require preservation of intricate details in the generated images. Other approaches [33, 45, 49, 61] use diffusion-generated data for semantic and aerial segmentation, demonstrating promising directions. Nonetheless, these approaches generate segmentation masks which lack detailed scene content. In contrast, we generate high quality degraded images for image restoration, where preserving scene consistency is crucial for good performance. Our approach effectively addresses these challenges, leading to significant improvements in the generalization of image restoration models trained on our synthetic data.
+
+# 3. Proposed Method
+
+In this section, we detail GenDeg, our diffusion-based method for generating large-scale synthetic data for image restoration. We also discuss the process of data generation, curation and training of restoration models.
+
+# 3.1. Diffusion-based degradation generation
+
+Our goal is to leverage the generative priors of pre-trained diffusion models to produce diverse degradations on clean images while preserving scene semantics. We consider the synthesis of six degradations, namely, haze, rain, snow, low-light, motion blur and raindrops. To achieve this, we re
+
+![](images/64b024d08f44bc88faf51798e44a99e76ef47fe99f6390882f8c1d88a01cda78.jpg)  
+(a)
+
+![](images/9c070bbce19fd1b23d88e5f54a2ebb04470db1d8beaf137d5504372d13fbbeb9.jpg)
+
+![](images/a9b9c2c369eb6dd105b5d6e1f914e25c633e672dfcfa6afdbcf789f463d9d36f.jpg)  
+(c)  
+Figure 4. (a) Illustrates the training stage of the GenDeg model where it is trained to condition on the clean image, text prompt and mean intensity  $(\mu)$  and variation  $(\sigma)$  of the degradation pattern. (b) Shows the inference stage where the model generates a degraded image based on these conditions; and (c) Depicts the architecture of the Swin-transformer-based restoration network.
+
+require a diffusion model that conditions on an input clean image (that needs to be degraded) and a prompt specifying the desired degradation. One popular approach that aligns with our objectives is the InstructPix2pix [5] model. It is a text-based image editing framework that leverages the latent diffusion model (LDM), Stable Diffusion [40], to generate edited images that are consistent with the input image. LDM operates in the latent space of a pre-trained variational auto-encoder [20] whose encoder and decoder are denoted by  $\mathcal{E}$  and  $\mathcal{D}$ , respectively. Given an image  $x_{\mathrm{in}}$ , image condition  $c_{\mathrm{img}}$  and text condition  $c_{\mathrm{text}}$ , the diffusion model  $(\epsilon_{\theta})$  minimizes the following objective during training
+
+$$
+L = \mathbb {E} _ {\mathcal {E} (x _ {\text {i n}}), \mathcal {E} (c _ {\text {i m g}}), c _ {\text {t e x t}}, \epsilon \sim \mathcal {N} (0, 1), t} \left[ \| \epsilon - \epsilon_ {\theta} \left(z _ {t}, t, \mathcal {E} \left(c _ {\text {i m g}}\right), c _ {\text {t e x t}}\right) \| _ {2} ^ {2} \right], \tag {1}
+$$
+
+where  $z_{t}$  is the noised version of  $\mathcal{E}(x_{\mathrm{in}})$  at timestep  $t$  of the forward diffusion process and  $\epsilon$  is the added noise.
+
+In our adaptation,  $x_{\mathrm{in}}$  is the degraded image from existing paired restoration datasets,  $c_{\mathrm{img}}$  is the corresponding clean image, and  $c_{\mathrm{text}}$  is a text-prompt describing the degradation to be produced. To train the diffusion model, we combine multiple synthetic and real image restoration datasets (see supplementary for details). This approach ensures that the diffusion model learns to produce diverse degradation patterns not specific to any single dataset.
+
+The text condition  $c_{\mathrm{text}}$  includes high-level scene information along with degradation specifics. We obtain scene descriptions by processing clean images through the BLIP-2 [24] captioning model. We then append degradation-specific phrases such as "in hazy conditions." to these descriptions, forming the final text prompt. Incorporating scene descriptions provides initial guidance to Stable Dif
+
+fusion during training, helping it generate an image related to  $c_{\mathrm{img}}$ . Examples of the text prompts are shown in Fig. 4.
+
+While this method produces degraded images effectively, we observed that using only the degradation type in the prompt causes the diffusion model to generate extreme degradation patterns during inference. For instance, the haze is excessively thick, or the rain is unrealistically heavy or minimal (see supplementary for examples). Such degradations could negatively impact the performance of a restoration model trained on this data, as the patterns differ significantly from typical real-world scenarios. To overcome this limitation, we introduce a conditioning on the level of the degradation, quantified by the mean intensity  $(\mu)$  and standard deviation  $(\sigma)$  of the degradation map  $c_{\mathrm{map}}$  defined as  $c_{\mathrm{map}} = |x_{\mathrm{in}} - c_{\mathrm{img}}|$ .  $\mu$  represents mean intensity of degradation in the degraded image while  $\sigma$  indicates its spatial distribution across the image. We fuse the conditioning information in the form of  $\mu$  and  $\sigma$  with the CLIP [37] embedding,  $e_{\mathrm{text}}$ , of the prompt,  $c_{\mathrm{text}}$ , as follows. First, we compute the range,  $[a,b]$  of  $\mu$  and  $\sigma$  for each degradation type from all their respective datasets. We divide this range into 128 bins and obtain a one-hot encoding for the bins corresponding to particular  $\mu$  and  $\sigma$  values calculated from  $c_{\mathrm{map}}$  during training. An additional bin is included for null-prompt conditioning [5], resulting in vectors of length 129.
+
+We then concatenate the one-hot vectors for  $\mu$  and  $\sigma$  to obtain  $c_{\mathrm{stats}} \in \mathbb{R}^{2 \times 129}$ .  $c_{\mathrm{stats}}$  is then projected to  $c_{\mathrm{stats}}^{\mathrm{proj}} \in \mathbb{R}^{2 \times 77}$ .  $c_{\mathrm{stats}}^{\mathrm{proj}}$  is then transposed to  $\mathbb{R}^{77 \times 2}$  and concatenated with  $e_{\mathrm{text}} \in \mathbb{R}^{77 \times 768}$  to obtain a vector of size  $\mathbb{R}^{77 \times 770}$ . We project this vector back to the CLIP text embedding dimension.
+
+sion and obtain  $e_{\mathrm{prompt}} \in \mathbb{R}^{77 \times 768}$  to be fed as conditioning to Stable Diffusion. All projection layers are learnable. This conditioning mechanism ensures that the diffusion model is aware of the degradation level to be added to the clean image, resulting in generated images,  $x_{\mathrm{gen}}$ , with diverse and realistic degradation patterns. The effect of varying  $\mu$  and  $\sigma$  is given in Sec. 4.2. Fig. 4 (a) summarizes the above steps.
+
+Finally, we tackle the challenge of aligning the generated degraded images  $(x_{\mathrm{gen}})$  precisely with the input clean images  $(c_{\mathrm{img}})$ . The VAE encoding and decoding process in LDMs causes the loss of fine details in the image [9, 18]. To mitigate this issue, we draw inspiration from AutoDIR [18], which introduced a Structure Correction Module (SCM) to reverse VAE-induced distortions. In our framework, the SCM, denoted by  $S$ , corrects  $x_{\mathrm{gen}}$  as follows:
+
+$$
+x _ {S} = x _ {\text {g e n}} + S ([ x _ {\text {g e n}}, c _ {\text {i m g}} ]) \tag {2}
+$$
+
+The goal of  $S$  is to reverse LDM and VAE artifacts while preserving the generated degradation. We train  $S$  after the degradation generator has been trained (keeping generator frozen) using a one-step reverse diffusion process:
+
+$$
+z _ {\text {g e n}} = \frac {\left(z _ {t} - \sqrt {1 - \bar {\alpha} _ {t}} \cdot \epsilon\right)}{\sqrt {\bar {\alpha} _ {t}}} \tag {3}
+$$
+
+Here  $\bar{\alpha}_t$  is the cumulative product of the noise schedule up to timestep  $t$ , and  $z_t$  is the noisy latent. We then obtain  $x_{\mathrm{gen}} = \mathcal{D}(z_{\mathrm{gen}})$ . The loss function for training  $S$  is given by
+
+$$
+L _ {S} = \sqrt {\bar {\alpha} _ {t - 1}} \cdot \sqrt {1 - \bar {\alpha} _ {t}} \cdot \| x _ {\text {i n}} - x _ {S} \| _ {2} ^ {2} \tag {4}
+$$
+
+The term  $\sqrt{\bar{\alpha}_{t-1}} \cdot \sqrt{1 - \bar{\alpha}_t}$  weights the performance of  $S$  for each timestep, recognizing that structure correction is easier near the initial timesteps ( $t \approx 0$ ) and quite challenging near the final timesteps ( $t \approx T$ ). This weighting reduces the influence of these extreme cases during training.
+
+We found that  $S$  works well for degradations that possess smooth characteristics such as haze, raindrops and motion blur. However, for degradations such as rain and snow,  $S$  blurs out the rain streaks and snowflakes in the generated image. Similarly, for low-light conditions, the SCM can produce blurry outputs due to the low pixel intensities. Thus, for rain, snow and low-light degradations, we omit the usage of  $S$ . Instead, we pass the clean image through the VAE encoder and decoder to obtain a slightly altered version  $\hat{c}_{\mathrm{img}}$  that is better aligned with the generated image than the original clean image  $(c_{\mathrm{img}})$ . Visual results showcasing the effect of  $S$  can be found in the supplementary.
+
+# 3.2. Dataset creation
+
+GenDeg enables synthesizing diverse degradations on any clean image. We generate degradations on unique clean images taken from the training datasets of GenDeg. Since we use a large number of training datasets, we obtain approximately 120k distinct scenes. For each clean image, we produce degradations that were not present in its original training set, resulting in five degradations per image. This
+
+strategy supplements existing restoration datasets with our synthetic data, enhancing the generalization capabilities of restoration models when trained on them.
+
+To generate a particular degradation, we randomly select a dataset associated with that degradation type. We sample  $\mu_{\mathrm{gen}}$  from the histogram of  $\mu$  values in the selected dataset, which is created by the same binning strategy used during training. Subsequently, we sample  $\sigma_{\mathrm{gen}}$  from a similar histogram of  $\sigma$  values obtained from images belonging to the sampled  $\mu_{\mathrm{gen}}$  bin. This process ensures that the value of  $\sigma_{\mathrm{gen}}$  is meaningfully correlated with the chosen  $\mu_{\mathrm{gen}}$ , resulting in realistic degradation patterns. To further enhance diversity, for every 1 in 20 images, we select a random value of  $\sigma_{\mathrm{gen}}$  (within acceptable limits) for a chosen  $\mu_{\mathrm{gen}}$ . The clean image is then degraded using the chosen  $\mu_{\mathrm{gen}}$  and  $\sigma_{\mathrm{gen}}$  values. After generation, we filter images based on the mean value of the generated degradation map to discard poor quality images (see Fig. 4(b)). The filtering thresholds are empirically determined for each degradation type (given in supplementary). In total, after filtering, we create approximately  $550\mathrm{k}$  degraded images which are combined with samples from existing datasets to obtain the GenDS dataset.
+
+# 3.3. Training image restoration models
+
+Transformer-based architectures have demonstrated enormous potential in learning generalizable image features. However, the usage of pre-trained transformers for image restoration remains limited. We hypothesize that transformer encoders pre-trained on large datasets such as ImageNet [14] can serve as effective feature encoders for improving generalization in restoration tasks. Hence, we choose a pre-trained Swin transformer encoder [50] for extracting generalizable features from degraded images. We specifically choose the Swin transformer [31] over the standard Vision Transformer (ViT) as it provides hierarchical features at multiple resolutions, which is crucial for preserving fine details in restored images. To reconstruct the restored image from the features extracted by the Swin transformer, we employ a lightweight convolutional decoder. This decoder aggregates information from different hierarchical levels of the encoder to produce a high-quality image. The overall architecture is depicted in Fig. 4 (c). The usage of  $3 \times 3$  convolutions in the decoder helps to overcome a major limitation of patch border artifacts [28] that occur when using transformer models for image restoration. The effect is more exacerbated when using vision transformers due to its large patch size. In addition to training the above Swin transformer-based architecture, we also train two other non-generative restoration networks: NAFNet [10] and PromptIR [35], on the GenDS dataset. To show that the GenDS dataset benefits generative image restoration, we also include two recent diffusion-based restoration models, namely, DA-CLIP [32] and Diff-Plugin [29].
+
+Table 2. Quantitative comparisons of NAFNet [10], PromptIR [35], Swin transformer, DA-CLIP [32] and Diff-Plugin [29] models using LPIPS and FID metrics (lower is better), trained with and without our GenDS dataset. Performance is evaluated on OoD test sets. The table also includes the performance of existing state-of-the-art (SOTA) approaches. Training with the GenDS dataset significantly enhances OoD performance. (R) indicates real images and (S) indicates synthetic images. Diff-Plugin# is the publicly available pre-trained model.  
+
+<table><tr><td>Method</td><td>REVIDE [60]</td><td>O-Haze [1]</td><td>RainDS [36]</td><td>LHP [15]</td><td>RSVD [8]</td><td>GoPro [34]</td><td>LOLv1 [48]</td><td>SICE [6]</td><td>RainDS [36]</td></tr><tr><td>Degradation Type</td><td>Haze (R)</td><td>Haze (R)</td><td>Rain (S)</td><td>Rain (R)</td><td>Snow (S)</td><td>Motion Blur (R)</td><td>Low-light (R)</td><td>Low-light (R)</td><td>Raindrop (R)</td></tr><tr><td>DiffUIR</td><td>0.268/58.5</td><td>0.334/147.2</td><td>0.088/31.2</td><td>0.187/26.5</td><td>0.176/26.1</td><td>0.144/25.2</td><td>0.148/65.1</td><td>0.442/102.9</td><td>-</td></tr><tr><td>Diff-Plugin#</td><td>0.281/72.9</td><td>0.377/164.7</td><td>0.194/45.0</td><td>0.178/30.2</td><td>0.207/22.5</td><td>0.217/32.8</td><td>0.195/70.5</td><td>0.233/69.2</td><td>-</td></tr><tr><td>InstructIR</td><td>0.313/65.4</td><td>0.341/154.5</td><td>0.117/29.2</td><td>0.139/21.2</td><td>-</td><td>0.146/21.1</td><td>0.132/57.3</td><td>0.234/65.2</td><td>-</td></tr><tr><td>AutoDIR</td><td>0.247/57.9</td><td>0.315/144.1</td><td>0.105/30.6</td><td>0.181/27.0</td><td>-</td><td>0.157/22.2</td><td>0.116/43.7</td><td>0.249/74.0</td><td>0.157/52.4</td></tr><tr><td>PromptIR</td><td>0.262/62.0</td><td>0.333/150.9</td><td>0.111/49.3</td><td>0.186/29.3</td><td>0.128/15.8</td><td>0.186/32.9</td><td>0.258/111.8</td><td>0.391/99.3</td><td>0.208/106.8</td></tr><tr><td>PromptIR GenDS</td><td>0.212/56.0</td><td>0.160/89.0</td><td>0.096/34.4</td><td>0.182/28.1</td><td>0.119/13.9</td><td>0.191/31.9</td><td>0.178/87.9</td><td>0.375/90.7</td><td>0.182/79.8</td></tr><tr><td>Swin</td><td>0.242/62.9</td><td>0.254/109.9</td><td>0.182/38.8</td><td>0.189/27.1</td><td>0.143/21.7</td><td>0.198/31.7</td><td>0.241/112.0</td><td>0.293/88.3</td><td>0.232/82.6</td></tr><tr><td>Swin GenDS</td><td>0.209/54.3</td><td>0.165/74.6</td><td>0.116/35.8</td><td>0.162/24.1</td><td>0.121/14.1</td><td>0.170/36.2</td><td>0.167/73.1</td><td>0.241/72.1</td><td>0.197/70.7</td></tr><tr><td>NAFNet</td><td>0.211/71.3</td><td>0.183/99.2</td><td>0.107/34.4</td><td>0.200/29.3</td><td>0.131/14.3</td><td>0.155/28.2</td><td>0.167/78.8</td><td>0.304/83.5</td><td>0.178/73.4</td></tr><tr><td>NAFNet GenDS</td><td>0.151/52.5</td><td>0.143/76.7</td><td>0.100/31.5</td><td>0.180/27.1</td><td>0.110/11.3</td><td>0.149/28.7</td><td>0.147/63.7</td><td>0.278/78.5</td><td>0.170/60.5</td></tr><tr><td>DA-CLIP</td><td>0.300/59.7</td><td>0.369/154.6</td><td>0.153/49.1</td><td>0.194/39.7</td><td>0.177/39.0</td><td>0.187/39.1</td><td>0.332/100.8</td><td>0.331/91.7</td><td>0.254/85.1</td></tr><tr><td>DA-CLIP GenDS</td><td>0.215/51.4</td><td>0.251/113.7</td><td>0.140/45.9</td><td>0.178/35.9</td><td>0.148/30.7</td><td>0.146/33.5</td><td>0.250/81.6</td><td>0.285/76.1</td><td>0.246/78.9</td></tr><tr><td>Diff-Plugin</td><td>0.273/63.9</td><td>0.320/130.3</td><td>0.197/49.3</td><td>0.214/36.4</td><td>0.207/24.3</td><td>0.174/27.1</td><td>0.276/103.3</td><td>0.256/79.5</td><td>0.287/93.0</td></tr><tr><td>Diff-Plugin GenDS</td><td>0.230/57.1</td><td>0.278/110.8</td><td>0.186/45.2</td><td>0.197/33.3</td><td>0.186/21.0</td><td>0.163/26.0</td><td>0.252/90.9</td><td>0.223/68.0</td><td>0.223/64.9</td></tr></table>
+
+# 4. Experiments
+
+In this section, we provide detailed results and analysis of our method. Implementation details and dataset details can be found in the supplementary.
+
+# 4.1. Results
+
+To assess the impact of the GenDS dataset, we first trained NAFNet [10], PromptIR [35], the Swin model (Sec. 3.3), DA-CLIP [32] and Diff-Plugin [29] solely on existing restoration datasets, without incorporating our synthetic data. We then evaluated their performance on both within-distribution and out-of-distribution (OoD) test sets. Subsequently, we retrained the same models using the GenDS dataset and evaluated the performance. Additionally, we compared their performance against state-of-the-art (SOTA) AIOR models, namely, DiffUIR [62], Diff-Plugin [29] (pre-trained model), InstructIR [13] and AutoDIR [18].
+
+Quantitative comparisons. Due to space constraints, we present quantitative comparisons using only the LPIPS and FID metrics (following [29]). Table 2 presents these scores for OoD test sets across all six degradations. We observe that PromptIR, NAFNet, the Swin model, DA-CLIP and Diff-Plugin exhibit significant improvements in OoD performance when trained on the GenDS dataset. Motion blur performance remains nearly the same even after training with the GenDS dataset. Since motion blur already contains sufficient real data with diverse scenes (see Fig. 2), introducing more synthetic data does not impact performance on real OoD samples. This highlights the importance of diverse high-quality data for generalizable AIOR.
+
+Furthermore, the results indicate that the synthetic data generated by GenDeg aids in bridging the domain gap with OoD samples. In certain instances, SOTA methods (upper portion of Table 2) outperform our models on specific OoD datasets (e.g., AutoDIR raindrop removal on RainDS). However, it is important to note that our models serve as simple baselines compared to the more complex SOTA architectures, and that SOTA methods do not consistently perform well for OoD datasets across degradations. Furthermore, SOTA architectures could further enhance their OoD performance by training with our GenDS dataset, as evidenced by the improvements observed for Diff-Plugin.
+
+Table 3 presents the mean within-distribution performance for each degradation. Interestingly, the performance remains almost identical even after training with our GenDS dataset. Moreover, the within-distribution performance shows substantial improvements for haze, lowlight and raindrop degradations. This improvement is likely due to the GenDS dataset effectively addressing the limited scene diversity present in existing datasets for these degradations. Detailed quantitative results (including PSNR and SSIM metrics) are available in the supplementary material.
+
+Qualitative comparisons. Fig. 5 shows qualitative results from one OoD test set per degradation for three top-performing models (PromptIR, NAFNet and the Swin model). Comparisons with SOTA are in the supplementary. Models trained with GenDS dataset consistently yield the best results. Notably, the enhanced images often contain richer colors than the ground truth (see first row), which can lower PSNR and SSIM scores. Thus, LPIPS and FID
+
+Table 3. Quantitative comparisons of mean LPIPS and FID scores (lower is better) across within-distribution datasets for each degradation. Comparisons are shown for PromptIR [35], NAFNet [10], Swin transformer, DA-CLIP [32] and Diff-Plugin [29] models trained with and without our GenDS dataset, along with SOTA models. Diff-Plugin# is the publicly available pre-trained model.  
+
+<table><tr><td>Method</td><td>Haze</td><td>Rain</td><td>Snow</td><td>Motion Blur</td><td>Raindrop</td><td>Low-light</td></tr><tr><td>DiffUIR</td><td>0.329/141.46</td><td>0.175/53.53</td><td>0.305/23.67</td><td>0.182/42.89</td><td>-</td><td>0.551/260.26</td></tr><tr><td>Diff-Plugin#</td><td>0.351/154.12</td><td>0.205/47.10</td><td>0.227/26.86</td><td>0.218/50.57</td><td>-</td><td>0.464/180.67</td></tr><tr><td>InstructIR</td><td>0.355/158.81</td><td>0.144/35.03</td><td>-</td><td>0.148/31.86</td><td>-</td><td>0.402/157.74</td></tr><tr><td>AutoDIR</td><td>0.306/136.27</td><td>0.139/38.12</td><td>-</td><td>0.161/33.34</td><td>0.195/68.09</td><td>0.420/155.14</td></tr><tr><td>PromptIR</td><td>0.309/141.05</td><td>0.097/32.61</td><td>0.100/18.34</td><td>0.163/35.79</td><td>0.189/84.48</td><td>0.421/189.87</td></tr><tr><td>PromptIR GenDS</td><td>0.210/112.54</td><td>0.080/27.95</td><td>0.091/16.19</td><td>0.171/34.93</td><td>0.188/74.65</td><td>0.354/168.59</td></tr><tr><td>NAFNet</td><td>0.190/118.22</td><td>0.074/21.84</td><td>0.067/8.20</td><td>0.136/28.72</td><td>0.085/39.91</td><td>0.349/172.36</td></tr><tr><td>NAFNet GenDS</td><td>0.171/104.43</td><td>0.077/22.13</td><td>0.069/8.56</td><td>0.136/29.31</td><td>0.069/29.92</td><td>0.316/148.65</td></tr><tr><td>Swin</td><td>0.244/121.92</td><td>0.092/24.50</td><td>0.080/10.80</td><td>0.194/40.69</td><td>0.097/47.09</td><td>0.420/187.65</td></tr><tr><td>Swin GenDS</td><td>0.182/105.242</td><td>0.090/24.48</td><td>0.083/11.44</td><td>0.189/42.17</td><td>0.092/39.63</td><td>0.368/166.13</td></tr><tr><td>DA-CLIP</td><td>0.344/147.15</td><td>0.114/33.32</td><td>0.128/43.61</td><td>0.198/50.03</td><td>0.101/40.82</td><td>0.538/208.47</td></tr><tr><td>DA-CLIP GenDS</td><td>0.267/109.11</td><td>0.102/30.50</td><td>0.072/26.48</td><td>0.140/37.82</td><td>0.094/36.03</td><td>0.434/182.16</td></tr><tr><td>Diff-Plugin</td><td>0.340/143.66</td><td>0.165/39.71</td><td>0.178/18.08</td><td>0.147/37.68</td><td>0.185/60.64</td><td>0.466/167.63</td></tr><tr><td>Diff-Plugin GenDS</td><td>0.302/128.01</td><td>0.164/39.22</td><td>0.176/17.70</td><td>0.142/35.99</td><td>0.133/42.52</td><td>0.456/156.18</td></tr></table>
+
+![](images/c261a0723fa926e6c6002c91b7881c85138be0356a9fa3fc26f59552a2632042.jpg)  
+Figure 5. Qualitative comparisons of image restoration models trained with and without our GenDS dataset. The suffix GD represents training with the GenDS dataset. Zoomed-in patches are provided for viewing fine details.
+
+scores are more reliable for testing the OoD performance. These results demonstrate that the synthetic data generated by GenDeg enhances the generalization of AIOR models.
+
+# 4.2. Analysis
+
+In this section, we use our generated synthetic data to conduct various insightful analyses.
+
+Synthetic Data Scaling. We analyze the impact of progressively adding synthetic data generated by GenDeg to existing real data on out-of-distribution (OoD) performance, utilizing the Swin transformer model. Fig. 6 illustrates the variation in mean OoD LPIPS and FID scores with increasing synthetic data. There is substantial OoD performance improvements with the addition of up to  $100\mathrm{k}$  syn
+
+![](images/24f65c4a03d6f5085b787ab4ae3304d35c5456c0011e6ecb8befaf76909f7ebe.jpg)  
+Figure 6. Effect of scaling number of GenDeg samples augmented with real data on OoD performance (LPIPS and FID).
+
+thetic samples, after which the performance improvement is reduced. Due to limited computational resources, we were unable to scale the synthetic dataset beyond 500k samples.
+
+Generated degradation diversity. GenDeg-generated data significantly aids in improving OoD performance of restoration models. This improvement is primarily due to the enhanced scene diversity (as shown in Fig. 2) in our dataset and the variety of degradation patterns produced by GenDeg. To illustrate the diversity in degradation patterns, we utilize degradation-aware CLIP (DA-CLIP [32]), a robust CLIP model trained to extract degradation-specific features from images. Fig. 3 presents a t-SNE visualization of the DA-CLIP degradation embeddings obtained from hazy samples in existing training datasets, our GenDS dataset, and the OoD test sets. The visualization reveals a clear gap between features of the training dataset and OoD test sets. This indicates that the degradation patterns in these datasets are different, hindering generalization. GenDS dataset bridges this gap by introducing numerous samples that resemble those in the OoD test sets, thereby enhancing generalization. Note that GenDeg was never trained on the OoD test sets. Furthermore, the t-SNE plot showcases the diversity of degradation patterns produced by our model, as evidenced by our samples spanning a wide area.
+
+Domain gap with real data. We examine the domain gap between existing datasets and GenDeg-generated data by training the Swin model exclusively on GenDeg synthesized data. Table 4 provides the mean LPIPS and FID scores for both within-distribution and OoD testing across all degradations. Training solely with GenDeg data lowers within-distribution performance compared to training on existing datasets due to the domain gap caused by factors such as alignment discrepancies between diffusion-generated samples and corresponding clean samples. However, utilizing both existing and GenDeg synthesized data, i.e., GenDS dataset, enhances performance as the restoration model benefits from diverse degradation patterns and scenes while maintaining performance on existing data.
+
+Table 4. LPIPS/FID scores for analyzing the performance difference between training on solely existing data, solely GenDeg data, and both existing and GenDeg data (GenDS dataset).
+
+<table><tr><td>Setting</td><td>Existing data</td><td>GenDeg data</td><td>GenDS data</td></tr><tr><td>Within-distribution</td><td>0.1879/72.10</td><td>0.2358/81.08</td><td>0.1669/64.84</td></tr><tr><td>OoD</td><td>0.211/59.36</td><td>0.207/61.71</td><td>0.1694/47.99</td></tr></table>
+
+Image,  $\sigma :0.12$ $\mu :0.1$ $\mu :0.2$ $\mu :0.27$
+
+![](images/26a7019e1b05229581f3ffd3a28df81af068a5298eb41620e180525356cee879.jpg)
+
+Image,  $\mu :0.15$ $\sigma :0.15$ $\sigma :0.17$ $\sigma :0.2$
+
+![](images/917a9824473871896f8e4bf9923f077fd2ea282d7af03eb9563dbbb581736be3.jpg)  
+Figure 7. Effect of varying  $\mu$  and  $\sigma$  in our GenDeg framework for the degradation of haze.
+
+![](images/6e37e4bff60b475e86e5d6b5d6845ef8e231aca56d03a792bd8c395bee6a933d.jpg)
+
+![](images/04719e1ea5371bb060f237e29e1cf5769df683e1988d3a03d867de9a89b5578e.jpg)
+
+The model trained only on existing datasets performs worse on OoD data than within-distribution. Training solely on GenDeg data improves OoD performance demonstrating that the diversity in scenes and degradation patterns enhances generalization. Nevertheless, the best performance is achieved when training on the GenDS dataset.
+
+Effect of  $\mu$  and  $\sigma$ . GenDeg enables control over the intensity and variations in the generated degradations, thereby enhancing diversity. In Fig. 7, we illustrate the effect of varying  $\mu$  and  $\sigma$  on two images for the synthesized degradation of haze. When  $\mu$  is increased, the intensity of haze in the image expectedly increases.  $\sigma$  controls the variation of haze in the image. For  $\sigma = 0.15$ , the non-homogenous haze (similar to NH-Haze [2]) is spread throughout the image. As  $\sigma$  increases, the spread of haze becomes more localized with higher intensity as seen from the figure.
+
+# 5. Conclusions
+
+In this paper, we addressed the important problem of generalization in All-In-One Restoration (AIOR) models. Toward this aim, we introduced GenDeg, a novel diffusion model-based framework for synthesizing diverse degradations on clean images, offering fine-grained control over degradation characteristics. Utilizing GenDeg, we generated over 550k degraded samples encompassing a wide range of scenes and degradations. Training AIOR models with both existing and GenDeg data yielded significant improvements in out-of-distribution performance. Our work suggests a promising research direction for addressing generalization challenges in AIOR, aiding in the development of more robust restoration models.
+
+# Acknowledgments
+
+This work is supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/ Interior Business Center (DOI/IBC) contract number 140D0423C0076. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DOI/IBC, or the U.S. Government.
+
+# References
+
+[1] Codruta O Ancuti, Cosmin Ancuti, Radu Timofte, and Christophe De Vleeschouwer. O-haze: a dehazing benchmark with real hazy and haze-free outdoor images. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 754-762, 2018. 6  
+[2] Codruta O Ancuti, Cosmin Ancuti, and Radu Timofte. Nh-haze: An image dehazing benchmark with nonhomogeneous hazy and haze-free images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 444-445, 2020. 8  
+[3] Shekoofeh Azizi, Simon Kornblith, Chitwan Sahara, Mohammad Norouzi, and David J Fleet. Synthetic data from diffusion models improves imagenet classification. arXiv preprint arXiv:2304.08466, 2023. 3  
+[4] Hritik Bansal and Aditya Grover. Leaving reality to imagination: Robust classification via generated datasets. arXiv preprint arXiv:2302.02503, 2023. 3  
+[5] 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. 4  
+[6] Jianrui Cai, Shuhang Gu, and Lei Zhang. Learning a deep single image contrast enhancer from multi-exposure images. IEEE Transactions on Image Processing, 27(4):2049-2062, 2018. 6  
+[7] A. Cantor. Optics of the atmosphere-scattering by molecules and particles. IEEE Journal of Quantum Electronics, 14(9): 698-699, 1978. 2  
+[8] Haoyu Chen, Jingjing Ren, Jinjin Gu, Hongtao Wu, Xuequan Lu, Haoming Cai, and Lei Zhu. Snow removal in video: A new dataset and a novel method. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 13165-13176. IEEE, 2023. 6  
+[9] Jingye Chen, Yupan Huang, Tengchao Lv, Lei Cui, Qifeng Chen, and Furu Wei. Textdiffuser: Diffusion models as text painters. Advances in Neural Information Processing Systems, 36, 2024. 5  
+[10] Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In European conference on computer vision, pages 17-33. Springer, 2022. 3, 5, 6, 7
+
+[11] Wei-Ting Chen, Hao-Yu Fang, Jian-Jiun Ding, Cheng-Che Tsai, and Sy-Yen Kuo. Jstasr: Joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal. Berlin, Heidelberg, 2020. Springer-Verlag. 1  
+[12] Wei-Ting Chen, Zhi-Kai Huang, Cheng-Che Tsai, Hao-Hsiang Yang, Jian-Jiun Ding, and Sy-Yen Kuo. Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model. 2022. 3  
+[13] Marcos V Conde, Gregor Geigle, and Radu Timofte. Instructir: High-quality image restoration following human instructions. In European Conference on Computer Vision, pages 1-21. Springer, 2025. 1, 3, 6  
+[14] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 5  
+[15] Yun Guo, Xueyao Xiao, Yi Chang, Shumin Deng, and Luxin Yan. From sky to the ground: A large-scale benchmark and simple baseline towards real rain removal. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 12097-12107, 2023. 2, 6  
+[16] Kaiming He, Jian Sun, and Xiaou Tang. Single image haze removal using dark channel prior. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 1956-1963, 2009. 1  
+[17] Junjun Jiang, Zengyuan Zuo, Gang Wu, Kui Jiang, and Xianming Liu. A survey on all-in-one image restoration: Taxonomy, evaluation and future trends. arXiv preprint arXiv:2410.15067, 2024. 1  
+[18] Yitong Jiang, Zhaoyang Zhang, Tianfan Xue, and Jinwei Gu. Autodir: Automatic all-in-one image restoration with latent diffusion. arXiv preprint arXiv:2310.10123, 2023. 1, 3, 5, 6  
+[19] Li-Wei Kang, Chia-Wen Lin, and Yu-Hsiang Fu. Automatic single-image-based rain streaks removal via image decomposition. IEEE Transactions on Image Processing, 21(4): 1742-1755, 2012. 1  
+[20] Diederik P Kingma. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 4  
+[21] 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 Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4015-4026, 2023. 2  
+[22] Xiangtao Kong, Jinjin Gu, Yihao Liu, Wenlong Zhang, Xi-angyu Chen, Yu Qiao, and Chao Dong. A preliminary exploration towards general image restoration. arXiv preprint arXiv:2408.15143, 2024. 1  
+[23] Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing, 28(1):492-505, 2019. 1, 2  
+[24] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730-19742. PMLR, 2023. 4
+
+[25] Ruoteng Li, Robby T. Tan, and Loong-Fah Cheong. All in one bad weather removal using architectural search. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3172-3182, 2020. 3  
+[26] Wei Li, Qiming Zhang, Jing Zhang, Zhen Huang, Xinmei Tian, and Dacheng Tao. Toward real-world single image deraining: A new benchmark and beyond. arXiv preprint arXiv:2206.05514, 2022. 2  
+[27] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swim transformer. In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pages 1833-1844, 2021. 1  
+[28] Yihao Liu, Jingwen He, Jinjin Gu, Xiangtao Kong, Yu Qiao, and Chao Dong. Degae: A new pretraining paradigm for low-level vision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23292-23303, 2023. 5  
+[29] Yuhao Liu, Zhanghan Ke, Fang Liu, Nanxuan Zhao, and Rynson WH Lau. Diff-plugin: Revitalizing details for diffusion-based low-level tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4197-4208, 2024. 1, 3, 5, 6, 7  
+[30] Yun-Fu Liu, Da-Wei Jaw, Shih-Chia Huang, and Jenq-Neng Hwang. Desnownet: Context-aware deep network for snow removal. IEEE Transactions on Image Processing, 27(6): 3064–3073, 2018. 1  
+[31] 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. 5  
+[32] Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjolund, and Thomas B Schon. Controlling vision-language models for universal image restoration. arXiv preprint arXiv:2310.01018, 3(8), 2023. 1, 2, 3, 5, 6, 7, 8  
+[33] Chaofan Ma, Yuhuan Yang, Chen Ju, Fei Zhang, Jinxiang Liu, Yu Wang, Ya Zhang, and Yanfeng Wang. Diffusionseg: Adapting diffusion towards unsupervised object discovery. arXiv preprint arXiv:2303.09813, 2023. 3  
+[34] Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3883-3891, 2017. 1, 2, 6  
+[35] Vaishnav Potlapalli, Syed Waqas Zamir, Salman H Khan, and Fahad Shahbaz Khan. Prompt: Prompting for all-in-one image restoration. Advances in Neural Information Processing Systems, 36, 2024. 1, 3, 5, 6, 7  
+[36] Ruijie Quan, Xin Yu, Yuanzhi Liang, and Yi Yang. Removing raindrops and rain streaks in one go. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9147-9156, 2021. 6  
+[37] 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 supervi
+
+sion. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 3, 4  
+[38] Sudarshan Rajagopalan and Vishal M Patel. Awracle: All-weather image restoration using visual in-context learning. arXiv preprint arXiv:2409.00263, 2024. 3  
+[39] Stephan R Richter, Hassan Abu AlHaija, and Vladlen Koltun. Enhancing photorealism enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2): 1700-1715, 2022. 3  
+[40] 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. 2, 4  
+[41] 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. 3  
+[42] Ziyi Shen, Wenguan Wang, Xiankai Lu, Jianbing Shen, Haibin Ling, Tingfa Xu, and Ling Shao. Human-aware motion deblurring. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5572-5581, 2019. 1, 2  
+[43] Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh, Wei Xiang, and Clinton Fookes. Diversity is definitely needed: Improving model-agnostic zero-shot classification via stable diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 769–778, 2023. 3  
+[44] Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh, Wei Xiang, and Clinton Fookes. Boosting zero-shot classification with synthetic data diversity via stable diffusion. arXiv preprint arXiv:2302.03298, 3(5), 2023. 3  
+[45] Aysim Token, Marvin Eisenberger, Daniel Cremers, and Laura Leal-Taixe. Satsynth: Augmenting image-mask pairs through diffusion models for aerial semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27695-27705, 2024. 3  
+[46] J. Jose Valanarasu, R. Yasarla, and V. M. Patel. Transweather: Transformer-based restoration of images degraded by adverse weather conditions. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2343-2353, 2022. 3  
+[47] Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson W.H. Lau. Spatial attentive single-image deraining with a high quality real rain dataset. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12262-12271, 2019. 1, 2  
+[48] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retina decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560, 2018. 6  
+[49] Weijia Wu, Yuzhong Zhao, Mike Zheng Shou, Hong Zhou, and Chunhua Shen. Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using dif
+
+fusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1206-1217, 2023. 3  
+[50] Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: A simple framework for masked image modeling. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9653-9663, 2022. 5  
+[51] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10371-10381, 2024. 2  
+[52] Rajeev Yasarla and Vishal M Patel. Uncertainty guided multi-scale residual learning-using a cycle spinning cnn for single image de-raining. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8405-8414, 2019. 1  
+[53] Rajeev Yasarla and Vishal M Patel. Confidence measure guided single image de-raining. IEEE Transactions on Image Processing, 29:4544-4555, 2020. 1  
+[54] Tianwei Yin, Michael Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6613-6623, 2024. 3  
+[55] Jianhao Yuan, Francesco Pinto, Adam Davies, Aarushi Gupta, and Philip Torr. Not just pretty pictures: Text-to-image generators enable interpretable interventions for robust representations. arXiv preprint arXiv:2212.11237, 3, 2022.3  
+[56] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In CVPR, 2021. 1  
+[57] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, 2022. 1  
+[58] He Zhang, Vishwanath Sindagi, and Vishal M. Patel. Joint transmission map estimation and dehazing using deep networks. IEEE Transactions on Circuits and Systems for Video Technology, 30(7):1975-1986, 2020. 1  
+[59] Kaihao Zhang, Rongqing Li, Yanjiang Yu, Wenhan Luo, and Changsheng Li. Deep dense multi-scale network for snow removal using semantic and depth priors. IEEE Transactions on Image Processing, 30:7419-7431, 2021. 1  
+[60] Xinyi Zhang, Hang Dong, Jinshan Pan, Chao Zhu, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, and Fei Wang. Learning to restore hazy video: A new real-world dataset and a new method. In CVPR, pages 9239–9248, 2021. 6  
+[61] Chenbo Zhao, Yoshiki Ogawa, Shenglong Chen, Zhehui Yang, and Yoshihide Sekimoto. Label freedom: Stable diffusion for remote sensing image semantic segmentation data generation. In 2023 IEEE International Conference on Big Data (BigData), pages 1022-1030. IEEE, 2023. 3  
+[62] Dian Zheng, Xiao-Ming Wu, Shuzhou Yang, Jian Zhang, Jian-Fang Hu, and Wei-shi Zheng. Selective hourglass
+
+mapping for universal image restoration based on diffusion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. 1, 3, 6

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+# GenFusion: Closing the Loop between Reconstruction and Generation via Videos
+
+Sibo Wu $^{1,2}$  Congrong Xu $^{1,3}$  Binbin Huang $^{4}$  Andreas Geiger $^{5}$  Anpei Chen $^{1,5,\dagger}$
+
+$^{1}$ Westlake University  $^{2}$ Technical University of Munich  $^{3}$ ShanghaiTech University  $^{4}$ The University of Hong Kong  $^{5}$ University of Tübingen, Tübingen AI Center  $^{\dagger}$ Corresponding author
+
+![](images/273754a9cbcce9735fb3c8de2a89ac84d36bad783632bb43f9a405a0f58f675f.jpg)  
+Figure 1. GenFusion introduces a reconstruction-driven generative model to enable artifact-free 3D asset generation and view synthesis for both view interpolation and extrapolation.
+
+![](images/58e9a611600b5fedc1125e27078b96df4941414b3b97e234b1d5734a06b3eeae.jpg)
+
+![](images/a4ba9a74185910edef6d1ef8881d31def3e0dee8ce84eafdd98af3fc93bf1324.jpg)
+
+# Abstract
+
+Recently, 3D reconstruction and generation have demonstrated impressive novel view synthesis results, achieving high fidelity and efficiency. However, a notable conditioning gap can be observed between these two fields, e.g., scalable 3D scene reconstruction often requires densely captured views, whereas 3D generation typically relies on a single or no input view, which significantly limits their applications. We found that the source of this phenomenon lies in the misalignment between 3D constraints and generative priors. To address this problem, we propose a reconstruction-driven video diffusion model that learns to condition video frames on artifact-prone RGB-D renderings. Moreover, we propose a cyclical fusion pipeline that iteratively adds restoration frames from the generative model to the training set, enabling progressive expansion and addressing the viewpoint saturation limitations seen in previous reconstruction and generation pipelines. Our evaluation, including view synthesis from sparse view and masked input, validates the effectiveness of our approach.
+
+# 1. Introduction
+
+Generating 3D assets is a fundamental task in computer vision and computer graphics, with broad applications in AR/VR, autonomous driving and robotics. Recent advances in Neural Radiance Fields (NeRF [36], Mildenhall et al.
+
+in 2020) and Gaussian Splatting (GS [27], Kerbl et al. in 2023) have enabled high-fidelity 3D scene reconstruction and novel view synthesis. They employ MLP or Gaussian primitives to represent scenes and optimize 3D representation through photometric loss. However, this line of work inherits a key limitation: faithful reconstruction relies on abundant viewpoint coverage; under-observed regions or viewpoints may lead to significant artifacts or missing content.
+
+This is primarily because reconstructing NeRFs or GSs from multi-view images is inherently underconstrained, as an infinite number of photo-consistent explanations may exist for the input images [40, 63]. Consequently, reconstruction models tend to generate "floaters" or "background collapse" artifacts to fake view-dependent effects, even when supplied with dense and well-captured high-quality images [46]. This observation has motivated a series of regularization techniques to constrain neural field training, including sparsity regularizers [22, 56], smoothness losses [37, 55, 62] and monocular geometric cues [61]. For example, ReconFusion [48] regularizes a NeRF-based 3D reconstruction pipeline by introducing a sample loss between novel random camera poses and images predicted by a PixelNeRF-style image diffusion model, yielding significant performance improvements over previous NeRF reconstruction methods in sparse setting. In contrast, feedforward reconstruction methods [5, 6, 10, 14, 57, 60] learn
+
+inductive biases directly from the dataset. While recent advances enable 3D reconstruction from as few as a single image, existing feedforward reconstruction methods exhibit performance saturation when processing more than 4-8 images. This limitation arises primarily from the architectural constraints of conventional feedforward networks in effectively aggregating and utilizing information from multiple viewpoints.
+
+Meanwhile, generative methods have demonstrated the potential of obtaining 3D assets without multi-view capture. Leveraging large-scale datasets and scalable architectures, models like Stable Diffusion (SD) have achieved remarkable progress in image and video generation [3, 21, 23]. Recent work has applied these approaches to generate 3D assets. For example, DreamFusion [39] introduces Score Distillation Sampling (SDS) to perform text-to-3D synthesis using a pre-trained 2D text-to-image diffusion model, while another line of research [4, 30, 59] explores single-view scene extrapolation by progressively in/outpainting layered depth image.
+
+Despite these advances in 3D reconstruction and generation, a notable conditioning gap remains: scalable 3D reconstruction typically requires dense view coverage, whereas generation methods often operate with single or even no input view. Our paper explores how 3D reconstruction and generation can complement each other in a scalable manner, relaxing the constraints on the number of input views.
+
+We introduce GenFusion, a novel reconstruction method that leverages video generative model to achieve artifact-free 3D scene reconstruction and content expansion along novel trajectories by leveraging the proposed reconstruction pipeline, as illustrated in Figure 2. The core of our approach is a simple and scalable reconstruction-driven video generation architecture that predicts realistic video from artifact-prone renderings. Specifically, we first fine-tune Dynam-iCrafter [51] using RGB-D videos reconstructed from a large-scale, real-world scene-level video dataset [34]. We patchify the capture videos into patches, then randomly select a patch sequence to perform 3D scene reconstruction, rendering full-frame RGB-D videos as input to our video diffusion model, which is subsequently supervised by the original video capture and its monocular depth. Our key insight is that masked 3D reconstruction enables flexible pre-training of video models. As shown in Figure 3, masking  $75\%$  of the input pixels during 3D reconstruction produces artifacts and missing regions sharing similar artifact patterns with far-field viewpoint rendering. The resulting artifact-prone video is encoded into latent space, with diffusion guided by a scene description token processed by a CLIP model on a randomly sampled frame. In addition, the baseline video diffusion model, conditioned only on text and RGB, lacks sequence input handling and geometry
+
+constraints for view consistency. To address this, we embed input views as latent sequences and incorporate depth by replacing the RGB VAE with an RGB-D VAE. Once trained, we introduce a cyclic reconstruction-generation fusion scheme for scalable 3D scene generation with artifact correction.
+
+Our GenFusion learns very high-capacity models that generalize well, we make the following contributions:
+
+- We introduce a reconstruction-driven video diffusion model that efficiently repairs reconstruction artifacts and generates new content in invisible regions.  
+- We design a masked 3D reconstruction for artifact-GT video pair generation, serving as a new novel view synthesis evaluation protocol for far-field viewpoints.  
+- Experiments on challenging benchmark datasets [1, 28, 34] demonstrate the effectiveness of GenFusion in synthesizing views distant from the training views.  
+- Our approach to bridging 3D reconstruction and generation through videos is principled and straightforward, picking the best of these two fields.
+
+# 2. Related Work
+
+Regularization Techniques: Optimizing 3D scene representations, e.g., NeRF [36] and 3DGS [27], from 2D images inherently involves an ill-posed inverse problem, where multiple solutions may exist for a given set of observations. To address these challenges, various techniques have been proposed to constrain the optimization of scene representations. In general, unsupervised regularization techniques are based on the assumption that the 3D representation should be sparse [22, 56], smooth [37, 55, 62], low rank [7-9]; rendering weights to be compact [1]; or geometry/texture to be consistent with nearby views in image space [15, 16, 18]. In addition to the multitude of regularization strategies available, many optimization techniques have been proposed to enhance training procedures. For example, gradient scaling [38] is applied for NeRF to address the issue of high-magnitude gradients in regions close to the camera, where more points are sampled, often leading to artifacts known as floaters. One of the most impactful approaches is the "coarse-to-fine" training [31, 32], which modulates the frequency band of positional encodings or hash grid resolution. Another line of research leverages pseudo-observations by using off-the-shelf models to predict cues that enhance reconstruction quality, such as sparse point clouds [17] and monocular depth/normal maps [61]. These approaches are also employed in 3DGS representations [29, 64]. Recently, combining generation models to generate novel views as regularization has proved to be effective in reconstruction objects [54] and scene [35, 48]. Our work aligns closely with ReconFusion [48] and the concurrent 3DGS-Enhancer [35], both of which utilize genera
+
+![](images/77626ef12fce8a7c0fd56b0ef9ddc254b88a7cb7dd505a78e42467fcbf01b349.jpg)  
+Video Diffusion Training  
+Figure 2. GenFusion pipeline. Our approach contains two stages: video diffusion pre-training (left) and zero-shot generalization (right). In pre-training, we first fine-tune DynamiCrafter [51] on RGB-D videos from a large-scale real-world scene video dataset [34]. Captured videos are patched, and a random patch sequence is selected for 3D scene reconstruction, rendering full-frame RGB-D videos as input to our video diffusion model, supervised by the original video capture and its monocular depth. During generalization, we treat reconstruction and generation as a cyclical process, iteratively adding restoration frames from the generative model to the training set for artifact removal and scene completion.
+
+![](images/f8a93bfbe62bcf8cda76a7737c28b6c250b00ebd307ede0d894095aa31ed7149.jpg)  
+Zero-shot Generalization
+
+tive priors to guide the optimization of 3D representations. Despite these methods achieve impressive view interpolation results in sparse-view scenarios, they still struggle with rendering trajectories that deviate significantly from the input views.
+
+Feed-forward 3D Reconstruction: In contrast to the per-scene optimization required by neural fields, recent research has explored feed-forward architectures capable of regressing 3D scene representations from a sparse set of input images. These methods learn 3D representations from input images and directly predict novel views in a feed-forward manner. PixelNeRF [58] predicts neural radiance representations from input images, using a convolutional network for efficient feature extraction. MVSNeRF [6] pioneers the paradigm of leveraging cost volumes to regress realistic images from novel viewpoints. Subsequent works [12, 26, 33, 52] further improve performance through enhanced feature matching architectures. In the context of 3DGS, pixelSplat [5] directly regresses scene-level 3D representations from paired images, incorporating an epipolar transformer module to effectively capture view-dependent geometric correspondences. MVSplat [14] builds a cost volume representation to learn cross-view feature similarity, achieving high-quality scene generation with improved efficiency. Recently, DepthSplat [53] further boosts the performance using a pre-train multi-view depth estimator, and Long-LRM [65] utilizes Mamba2 blocks to handle many input views. To handle single view inputs, Flash3D [44] extend monocular depth estimation to 3D shape and appear
+
+ance reconstruction. Recently, SplatFormer [13] proposes a learning-based model to refine Gaussian splats, enabling out-of-distribution novel-view synthesis. However, existing feed-forward methods are limited to a small number of views - typically fewer than 10 - which significantly restricts their application.
+
+View-Conditioned Generation: Generative models have emerged as a promising solution to synthesize plausible content for regions without observations. Thanks to their success in image generation, diffusion models are widely used for multi-view synthesis. Early work 3DiM [47] trains a pose-conditional image-to-image diffusion model for object-centric novel view synthesis. Follow-up research Zero-1-to-3 [20] advanced this approach by finetuning large-scale pre-trained diffusion models on synthetic datasets. SSDNeRF [11] jointly optimize diffusion and NeRF auto-decoder to synthesize novel views for object. Recently, ZeroNVS [41] and CAT3D [19] extended the paradigm by training diffusion models with camera pose and multi-view image conditioning. Nevertheless, relying solely on 2D features proves insufficient for maintaining 3D consistency across generated views. Several works [43, 50] inject 3D information into diffusion models for improved geometric understanding. For instance, ViewCrafter [60] explicitly utilizes 3D information from point clouds and expands it iteratively, facilitating consistent interpolation between two views. To enable generating long sequences of 3D scenes from a single input image, InfiniteNatureZero [30] and following work [4, 45, 59] explore single
+
+view scene extrapolation by progressively in/outpainting layered depth images predicted by a monocular estimator. While these generative methods achieve visually appealing 3D asset generation, their reconstruction quality and view coverage, particularly at scene scale, remains far from that of 3D scenes reconstructed from densely captured multiview data.
+
+# 3. GenFusion
+
+Our goal is to construct artifact-free 3D scenes with content augmentation given conditioning views  $\{\mathbf{I}_i\} (i > = 1)$ . The core idea to align 3D reconstruction and generation through video renderings. Specifically, we propose a cyclic fusion approach where 3D reconstruction and generation benefit each other in a virtuous cycle: during the reconstruction process, we iteratively leverage information from the generative model to improve reconstruction quality; meanwhile, more accurate reconstruction results help the generative model produce more realistic and consistent content. This bidirectional enhancement mechanism creates a positive feedback loop.
+
+In the pre-training stage, we aim to train a model that learns from large-scale scenes, which then serves as guidance for optimizing general ill-posed 3D lifting tasks, such as reconstructing 3D from sparse views. We use a video diffusion model as guidance, pre-trained on video captures and their artifact-prone renderings of 3D reconstructions. Specifically, we train a generative model  $G_{\phi}$  by maximizing the expected log-likelihood of generating the complete image given the reconstruction from a masked input:
+
+$$
+\arg \max  _ {\phi} \mathbb {E} \left[ \log p _ {\phi} \left(I \mid R _ {\theta} (\tilde {I})\right) \right] \tag {1}
+$$
+
+where  $R_{\theta}$  denotes a reconstruction and rendering function (e.g., NeRF or 3DGS) and  $\tilde{I}$  denotes the masked version of target image  $I$ . To make the generation process 3D aware, our video diffusion model learns to capture the underlying distribution of images by conditioning on artifact-prone rendering video  $R_{\theta}(\tilde{I})$  in our case.
+
+With the video diffusion model, we propose optimizing the 3D scene representation,  $\theta$ , guided by both input image conditions and generated video for a new scene. Specifically, we maximize the similarity between the novel view videos rendered from 3D representation and the videos generated from our pre-train diffusion model, therefore the gradient of 3D representation is propagated from the photometric loss:
+
+$$
+\arg \min  _ {\theta} \mathbb {E} \left[ \left| G \phi (\hat {I} _ {k + 1} \mid R _ {\theta} (\tilde {I} _ {k})) - R _ {\theta} (\hat {I} _ {k + 1}) \right| _ {2} \right] \tag {2}
+$$
+
+where  $k$  denotes the iteration index.
+
+In the following sections, we first introduce our reconstruction-driven generation architecture, i.e.
+
+![](images/0ec8d7e63ab548a37ee589b90fb8eb77c4247066e186921eb83b55dfb76c1cbf.jpg)  
+(a) Artifacts in GS reconstruction
+
+![](images/e6139527273d74168c3df76774200cc9d6a3553a348ef332f8dcb56dd8fe9420.jpg)  
+(b) Masked reconstruction  
+Figure 3. Artifact-GT video pair generation using masked reconstruction. a) current SOTA Gaussian Splitting methods render accurately near training views but produce artifacts for distant views due to limited angular supervision, like the red trajectory. b) we propose a masked reconstruction scheme to replicate such artifact patterns for training video diffusion models by masking  $75\%$  of pixels during 3D reconstruction and re-rendering the scene along the original trajectory, including the masked pixels.
+
+$G_{\phi}(I|R_{\theta}(\tilde{I}))$ , in Sec. 3.1. Next, we detail our cyclic generation and reconstruction fusion process in Sec. 3.2.
+
+# 3.1. Reconstruction-driven Generation
+
+We generate view-consistent video by conditioning the current fragment's generation on previous corrupted reconstructions. To generate corrupted reconstructions, we propose a masked 3D reconstruction approach for generating training data, along with a new video diffusion model that facilitates 3D-aware generation and regularization.
+
+Masked 3D Reconstruction. We now discuss how to learn novel view generalization capability in a reconstruction paradigm from open-world large-scale videos. Given video captures of a scene, a straightforward approach is to downsample frames at uniform intervals or split the input sequence at the midpoint into train/test segments for scene reconstruction, then render the reconstructed scene from the testing camera viewpoints to generate artifact-prone data. These rendered sequences and original video captures then serve as input and output pairs for video diffusion model training. However, we observe that these sampling schemes limit models to either view interpolation or pure generation. Specifically, scenes are often fully covered by sampled views, with target views located adjacent to these, while the alternative approach often leaves most content unseen in the training segment. Our work aims to learn regularization and generate new content in trajectories that deviate significantly far from the capture trajectories.
+
+To this end, we propose a masked 3D reconstruction technique to obtain corrupted 3D scenes from observations
+
+![](images/a6303b4c821141fc09e9ee26d13e02cef6074fb8bdaff2f893d83af7f18c81f2.jpg)  
+Figure 4. Reconstruction-driven Video Generation. Our video diffusion model is able to generate realistic RGB-D video from artifact-prone RGB-D renderings, which is then used as photometric guidance in our cyclic fusion period.
+
+across both spatial and temporal dimensions, then render images for the full input sequence to generate the training dataset. In specific, we divide input image captures into 4 regular non-overlapping patches, i.e. top-left, top-right, bottom-left and bottom-right, as shown in Figure 3. Then we sample the top-left or bottom-right patch region and mask out (i.e., remove) the remaining three for each scene. The sampled patch sequence is used to conduct 3D reconstruction using the standard approaches, i.e. 2D Gaussian Splatting (2DGS) [25] in this paper. Note that the mask is applied per scene rather than per view, forcing the reconstruction process to have limited view coverage. In practice, since we use open-world large-scale video sequence captures I for training, which further increases sparsity - views are dense only on the trajectory but extremely sparse in angle coverage - unlike standard multi-view datasets such as Mip-NeRF 360 [1].
+
+With masked reconstruction, we render full-view videos along the same camera poses as the input to form real capture and reconstruction rendering pairs for our video diffusion model training. Our insight is that reconstructing from masked images simulates a narrower field-of-view camera, requiring that context outside the mask be inferred from views deviating from the current viewpoint, which promotes view extrapolation. Moreover, rendering to full view introduces unconstrained regions with extensive black backgrounds, facilitating content outpainting.
+
+# Video Diffusion.
+
+In essence, we build our video generation model upon the foundation of pre-trained DynamiCrafter [51] - an image-to-animation model - and adapt the model to reconstruction-related video restoration tasks.
+
+More specifically, we enhance frame consistency by incorporating geometric information into the generation pro
+
+cess. This is achieved by replacing the VAE in the baseline model with a pre-trained RGB-D VAE, where encoder and decoded are denoted as  $\mathcal{E}$  and  $\mathcal{D}$  respectively. It allows depth to be integrated without altering the diffusion architecture. In the training process, the ground truth RGBD video  $I_{RGB - D}$  are encoded into latent space  $z\coloneqq \mathcal{E}(I_{RGB - D})$ , which we add noise in different timestep  $t$  and obtain  $z_{t}$ . To guide the generation process under reconstruction result, We provide two conditions  $c$ . The artifact-prone RGB-D video  $I_{RGB - D}^{\prime}$  is encoded and concatenated with per-frame initial noise to support sequence conditioning. This allows rich visual details from the rendered videos to condition the generation process effectively. Additionally, we select the input view closest to the rendering trajectory and embed its ground-truth image into the CLIP feature space. This high-level conditioning provides global information about the scene content. Thus the video denoising network  $\epsilon_{\theta}$  is optimized by:
+
+$$
+\mathcal {L} = \mathbb {E} _ {\mathcal {E} (x), c, \epsilon \sim \mathcal {N} (0, 1), t} \left[ \| \epsilon - \epsilon_ {\theta} \left(z _ {t}, t, c\right) \| _ {2} ^ {2} \right], \tag {3}
+$$
+
+The refined latent is subsequently decoded into the final RGB-D video through a pre-trained VAE decoder  $\hat{V} = \mathcal{D}(z)$  [43].
+
+# 3.2. Cyclic Fusion
+
+We build our fusion process upon the popular 2D Gaussian Splatting and use a number of 2D oriented planar Gaussian disks to represent 3D scenes. Gaussian disks are parameterized by center position  $p \in \mathbb{R}^{3 \times 1}$ , opacity (scale)  $\alpha \in [0,1]$ , two principal tangential vectors  $\mathbf{t}_u$  and  $\mathbf{t}_v$  for orientation, a calling vector  $\mathbf{S} = (s_u, s_v)$ , and Spherical Harmonices (SH) coefficients. We refer the reader to the original paper for representation and splatting details [25]. In the follow-
+
+ing, we introduce how to initialize and progressively update the Gaussian primitives by fusing the 3D reconstruction and video diffusion output.
+
+Fusion. We follow the original 3DGS approach by reusing the calibration point cloud for initialization. We initialize Gaussians and their attributes, which are then updated end-to-end based on rendering losses. The optimization process operates as a reconstruction and generation cycle, supervising Gaussians with both input conditioning and novel generated views. More concretely, for every  $K$  iteration, we begin with sample new trajectories and render RGB-D videos based on the current reconstruction. We feed these rendering sequences to our video diffusion model to generate artifact-free videos, which are then added to the supervision set, as shown in Figure 2. The cyclic process enables artifact correction in under-observed regions and generates new content for areas that are invisible within the input views.
+
+Among these, we find that novel trajectory sampling is the most critical component. To ensure comprehensive view and angle coverage, we employ two types of trajectories: view interpolation between neighboring input views and a spiral/spherical path generated across all input camera poses.
+
+Content Expansion. Large unobserved areas can appear as black or noisy pixels when the sampling trajectories are away from the input views. Although we use generation outputs as supervision for these regions, we found it challenging to split and clone new Gaussians [27] due to the absence of surrounding Gaussians. We solve this issue by adaptively adding new Gaussian points to the scene during optimization, using an unreliable depth-based mapping. Pixels are considered unreliable when
+
+$$
+T <   \tau_ {T} \text {o r} | D - \hat {D} | > \tau_ {D} \tag {4}
+$$
+
+where  $T$  is cumulative opacity,  $D$  and  $\hat{D}$  are rendered depth and aligned generated depth D respectively.  $\tau_{T}$  and  $\tau_{D}$  are hyperparameter thresholds.
+
+For these unreliable areas, we add new Gaussians by back-projecting the generated RGB-D points into 3D space, similar to the initialization stage. Note that for the newly added Gaussians, position and color values are directly obtained from the RGB-D video, while other attributes are initialized as in 2DGS.
+
+Loss function. During cyclic fusion, we freeze the video diffusion model and optimize the 3D representation end-to-end, using simple photometric losses between rendered RGB-D images and input (i.e.  $\mathcal{L}_{\text {recon }}$ ) and generated views (i.e.  $\mathcal{L}_{\text {gen }}$ ):
+
+$$
+\mathcal {L} = \mathcal {L} _ {\text {r e c o n}} + \lambda \mathcal {L} _ {\text {g e n}} \tag {5}
+$$
+
+where  $\mathcal{L}_{\text{recon}} = \lambda_{l_1} \cdot \mathcal{L}_{l1} + \lambda_{SSIM} \cdot \mathcal{L}_{SSIM} + \lambda_{mono} \cdot \mathcal{L}_{mono}$ , and the  $\mathcal{L}_{mono}$  is a scale-invariant depth loss, as used in [61] that enforces consistency between our rendered rendered depth  $\hat{D}$  and the monocular depth  $D$  predicted by our video diffusion model. The generation loss  $\mathcal{L}_{gen}$  shares components but is instead applied to generated images.
+
+To stabilize the optimization process, we use an Sinusoidal warm-up and annealing strategy for the generation loss weight  $\lambda$ , which is defined as:
+
+$$
+\lambda (k) = 1. 0 \cdot \sin \left(\frac {k - K _ {\text {s t a r t}}}{K _ {\text {e n d}} - K _ {\text {s t a r t}}} \cdot \pi\right) \tag {6}
+$$
+
+where  $k$  is the iteration, and  $K_{\mathrm{start}}$  and  $K_{\mathrm{end}}$  are the start and end iteration of the diffusion term.
+
+# 4. Experiments
+
+We begin with the experimental setup for training and evaluating our GenFusion. Next, we present our reconstruction-driven video diffusion model and compare it quantitatively/qualitatively with state-of-the-art methods for sparse-view 3D reconstruction and view synthesis. Finally, we demonstrate the generative capabilities of our method with scene completion.
+
+# 4.1. Experimental Setup
+
+Training set: We train our diffusion model on DL3DV10K [34], a large-scale dataset containing 10,510 videos, including 140 benchmark scenes. To prepare reconstruction-gt video pairs for diffusion model training, we optimize each scene for 7K steps using our masked 3D reconstruction scheme introduced in Section 3.1. The number of training views are uniformly downsampled to  $1/4$  frames for each video to ensure sufficient sparsity, and we render the reconstructed scenes along the original trajectories at a resolution of  $960 \times 540$  and augment the dataset with depth information using the current SOTA monocular depth estimator [24].
+
+Evaluation Dataset: We evaluate our method on diverse scenes from three datasets, including 24 scenes from the DL3DV-Benchmark [34], 7 scenes from Tanks and Temples (TnT) [28], and 9 scenes from the Mip-NeRF360 [1] dataset, see supplement for details.
+
+Implementation Details: In this work, we adopt Dynami-Crafter [51] as the video diffusion backbone and fine-tune the model on artifact-GT RGB-D video pairs. The finetuning includes both coarse and fine stages: in the coarse stage, the video resolution is set to  $16 \times 320 \times 512 \times 4$  with a latent space dimension of  $16 \times 40 \times 64 \times 4$ . We train this stage for 30K steps with a learning rate of  $1 \times 10^{-5}$  and a batch size of 2 on four H100 GPUs. The model is then finetuned to a higher resolution of  $16 \times 512 \times 960 \times 4$  for another
+
+<table><tr><td>Method</td><td>RGB VAE</td><td>RGB-D VAE</td><td>RGB-D VAE</td><td>RGB-D VAE</td></tr><tr><td>Frames</td><td>16</td><td>16</td><td>48</td><td>16</td></tr><tr><td>Resolution</td><td>512×320</td><td>512×320</td><td>512×320</td><td>960×512</td></tr><tr><td>FID↓</td><td>26.1607</td><td>25.4006</td><td>29.3545</td><td>22.5526</td></tr></table>
+
+Table 1. Analysis on reconstruction-driven video diffusion model.  
+
+<table><tr><td rowspan="2"></td><td colspan="4">PSNR ↑</td><td colspan="4">SSIM ↑</td><td colspan="4">LPIIS ↓</td></tr><tr><td>3-view</td><td>6-view</td><td>9-view</td><td>Avg.</td><td>3-view</td><td>6-view</td><td>9-view</td><td>Avg.</td><td>3-view</td><td>6-view</td><td>9-view</td><td>Avg.</td></tr><tr><td>Zip-NeRF [2]</td><td>12.77</td><td>13.61</td><td>14.30</td><td>13.56</td><td>0.271</td><td>0.284</td><td>0.312</td><td>0.289</td><td>0.705</td><td>0.663</td><td>0.633</td><td>0.667</td></tr><tr><td>DiffusioNeRF [49]</td><td>11.05</td><td>12.55</td><td>13.37</td><td>12.32</td><td>0.189</td><td>0.255</td><td>0.267</td><td>0.237</td><td>0.735</td><td>0.692</td><td>0.680</td><td>0.702</td></tr><tr><td>FreeNeRF [55]</td><td>12.87</td><td>13.35</td><td>14.59</td><td>13.60</td><td>0.260</td><td>0.283</td><td>0.319</td><td>0.287</td><td>0.715</td><td>0.717</td><td>0.695</td><td>0.709</td></tr><tr><td>SimpleNeRF [42]</td><td>13.27</td><td>13.67</td><td>15.15</td><td>14.03</td><td>0.283</td><td>0.312</td><td>0.354</td><td>0.316</td><td>0.741</td><td>0.721</td><td>0.676</td><td>0.713</td></tr><tr><td>ZeroNVS [41]</td><td>14.44</td><td>15.51</td><td>15.99</td><td>15.31</td><td>0.316</td><td>0.337</td><td>0.350</td><td>0.334</td><td>0.680</td><td>0.663</td><td>0.655</td><td>0.666</td></tr><tr><td>ReconFusion [48]</td><td>15.50</td><td>16.93</td><td>18.19</td><td>16.87</td><td>0.358</td><td>0.401</td><td>0.432</td><td>0.397</td><td>0.585</td><td>0.544</td><td>0.511</td><td>0.547</td></tr><tr><td>3DGS [27]</td><td>13.06</td><td>14.96</td><td>16.79</td><td>14.94</td><td>0.251</td><td>0.355</td><td>0.447</td><td>0.351</td><td>0.576</td><td>0.505</td><td>0.446</td><td>0.509</td></tr><tr><td>2DGS [25]</td><td>13.07</td><td>15.02</td><td>16.67</td><td>14.92</td><td>0.243</td><td>0.338</td><td>0.423</td><td>0.335</td><td>0.580</td><td>0.506</td><td>0.449</td><td>0.512</td></tr><tr><td>FSGS [62]</td><td>14.17</td><td>16.12</td><td>17.94</td><td>16.08</td><td>0.318</td><td>0.415</td><td>0.492</td><td>0.408</td><td>0.578</td><td>0.517</td><td>0.468</td><td>0.521</td></tr><tr><td>GenFusion (Ours)</td><td>15.29</td><td>17.16</td><td>18.36</td><td>16.93</td><td>0.369</td><td>0.447</td><td>0.496</td><td>0.437</td><td>0.585</td><td>0.500</td><td>0.465</td><td>0.517</td></tr></table>
+
+34K steps in the fine stage. To handle the RGB-D format, we use a frozen RGB-D VAE from LDM3D [43]. During inference, we apply DDIM sampling with 25 steps and set the classifier-free guidance scale to 3.2.
+
+For zero-shot generalization, we use 2DGS as the 3D representation and initialize with the COLMAP point cloud. In our experiments, views are masked and frames are downsampled; we filter and retain only the points visible from the training views for point cloud initialization.
+
+# 4.2. Video Generation
+
+In this paper, we introduce a reconstruction-driven video diffusion architecture that enables novel view regularization and content generation. We report the VAE design and its impact on video resolution in Table 1. Surprisingly, we find that our model fine-tuned with RGB-D VAE achieves a better FID score than the RGB VAE, even though the diffusion backbone was originally pre-trained on the RGB VAE latent space. Significant improvement can be observed when increasing the spatial resolution from  $512 \times 320$  to  $960 \times 512$ . We show the visualization results of our diffusion model in Figure 4, our video diffusion model effectively removes "floaters" from the input video while generating realistic content in black-pixel regions.
+
+# 4.3. View Interpolation
+
+Next, we evaluate our method in a view interpolation scenario where the target scene is fully covered by the input views, and testing views lie between these inputs—a common setup in prior regularization evaluations. We compare our method with previous regularization techniques on the Mip-NeRF 360 test set using 3, 6, and 9 input views, as
+
+![](images/31cf64e6e3fa054b8b21c3126339c93d42519344459f32e88234493a624830a9.jpg)  
+Figure 5. Qualitative comparison of novel view synthesis using sparse view input on Mip-NeRF360 scenes [1].
+
+Table 2. Quantitative evaluation of sparse view 3D reconstruction methods on Mip-NeRF360 dataset. Our approach demonstrates strong performance across a variety of domains, surpassing baseline methods in most cases. We color each column as: best, second best, and third best. The NeRF baseline results above are taken from Reconfusion.  
+
+<table><tr><td rowspan="2"></td><td colspan="3">DL3DV 1/2 fps.</td><td colspan="3">TnT 1/2 fps.</td><td colspan="3">DL3DV 1/4 fps.</td><td colspan="3">TnT 1/4 fps.</td></tr><tr><td>PSNR↑</td><td>SSIM↑</td><td>LPIPS↓</td><td>PSNR↑</td><td>SSIM↑</td><td>LPIPS↓</td><td>PSNR↑</td><td>SSIM↑</td><td>LPIPS↓</td><td>PSNR↑</td><td>SSIM↑</td><td>LPIPS↓</td></tr><tr><td>3DGS [27]</td><td>17.22</td><td>0.740</td><td>0.314</td><td>15.95</td><td>0.653</td><td>0.414</td><td>16.90</td><td>0.728</td><td>0.321</td><td>14.75</td><td>0.609</td><td>0.440</td></tr><tr><td>2DGS [25]</td><td>16.56</td><td>0.717</td><td>0.323</td><td>15.46</td><td>0.640</td><td>0.409</td><td>16.02</td><td>0.693</td><td>0.336</td><td>14.38</td><td>0.589</td><td>0.440</td></tr><tr><td>FSGS [62]</td><td>18.25</td><td>0.722</td><td>0.362</td><td>16.72</td><td>0.625</td><td>0.465</td><td>17.83</td><td>0.710</td><td>0.370</td><td>16.04</td><td>0.607</td><td>0.473</td></tr><tr><td>Ours</td><td>20.47</td><td>0.788</td><td>0.284</td><td>17.45</td><td>0.662</td><td>0.427</td><td>20.01</td><td>0.780</td><td>0.292</td><td>16.29</td><td>0.630</td><td>0.447</td></tr></table>
+
+Table 3. Quantitative comparison on DL3DV [34] and TnT [28] datasets. Each method is trained for 7,000 steps. Our method outperforms baselines by a significant margin.
+
+shown in Table 2.
+
+Our results demonstrate that our method achieves more realistic novel view synthesis than the 3DGS and 2DGS baselines, as well as recent FSGS, by a significant margin. It is worth noting that Gaussian Splatting is known to be more challenging to train than NeRF, especially in sparse view settings. We significantly narrow this gap, and, for the first time, show that Gaussian Splatting achieves performance comparable to state-of-the-art NeRF on the challenging Mip-NeRF360 dataset in sparse view settings.(see Table 2 and Figure 5).
+
+# 4.4. View Extrapolation
+
+Unlike sparse view reconstruction, our paper focuses on a more practical scenario: generating complete, artifact-free 3D scenes from video captures. In reality, video offers dense sampling along the trajectory but highly sparse viewpoint coverage. While video is easy to capture and calibrate, it's challenging for 3D reconstruction, even reconstruction with state-of-the-art reconstruction methods can easily overfit to training views and produce serious artifacts
+
+![](images/9c286058a67af75c4c8a0a4dfbfc996fe06115cf258ed2a2792790b2e0df7a11.jpg)  
+3DGS [27]  
+Figure 6. Qualitative comparison of novel view synthesis using masked input on DL3DV scenes [34]. Gaussian Splatting methods can easily overfit to training views, producing holes and needle-like artifacts when viewed from distant viewpoints. Our method effectively repairs these artifacts while augmenting invisible regions.
+
+![](images/5ea6027a08400dc894de1e22f9325cc900b31a759b77dc229aab02ca12a4a6d9.jpg)  
+2DGS [25]
+
+![](images/a8d3deef5dbefe4ef5ae0e61d49d4076acc296ae996d61e10dc93c571e0ebe48.jpg)  
+FSGS [64]
+
+![](images/487b666be0f3eb05f7706e59855b05496830054cc741f3da3d734a3b5634dca6.jpg)  
+Ours
+
+![](images/9152ea40024264087e3d275ba67316664ec3f113395e642a7afe24ee8f521380.jpg)  
+GT
+
+for the viewpoints that are far away from the training views, see Figure 3. Therefore, we care about the novel view synthesis quality from the far field.
+
+To this end, we propose a new evaluation protocol and reuse the masked reconstruction to mime the far-field rendering while preserving the reference, as introduced in Section 3.1. Specifically, we downsample the video sequence at different time ratios for the train/test split (e.g.,  $1/2$  and  $1/4$  in Table 3) and select a patch covering about  $25\%$  of the pixels in the training viewpoint for reconstruction, while evaluating on the full field of view of test viewpoints. Note that fixing the path location can result in many invisible and black regions, leading to bias in our generative methods, where the baselines may have significant errors in these areas. To ensure a reasonable comparison, we assign a trajectory to the masking path to provide more complete content coverage, see supplementary video.
+
+Table 3 and Figure 6 show quantitative and qualitative results comparing our method with baselines on the DL3DV-benchmark and TnT scenes. GenFusion achieves significantly better rendering quality than the baselines, thanks to the strong prior from the diffusion model. It effec
+
+tively removes needle-like artifacts and augments realistic 3D content for the invisible regions.
+
+Scene Completion. Furthermore, GenFusion not only provides content augmentation at the 3D scene boundary but also achieves scene-level completion, as shown in Figure 1. Please refer to our webpage for more results.
+
+# 5. Discussion
+
+We have presented GenFusion, a novel model for artifact-free 3D asset generation. First, we adapt an existing well-trained 2D video diffusion model to drive a powerful 3D guidance with minimal modifications. Second, cyclical fusion enables scalable and robust 3D lifting, efficiently closing the loop between 3D reconstruction and generation through video synthesis. Several limitations are evident: our method requiring additional denoising steps and slightly increasing training time (about 40 minutes per scene). Additionally, filling large invisible regions can cause blurriness due to inconsistency between video fragments. Modeling and addressing this inconsistency in the fusion module will be a key step toward achieving the next level of quality.
+
+Acknowledgements: This work was supported by the Westlake Education Foundation, supported by the Natural Science Foundation of Zhejiang province, China (No. QKWL25F0301), by the ERC Starting Grant LEGO-3D (850533), by the DFG EXC number 2064/1 - project number 390727645.
+
+# References
+
+[1] Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 5, 6, 7  
+[2] Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Zip-nerf: Anti-aliased grid-based neural radiance fields. In Proc. of the IEEE International Conf. on Computer Vision (ICCV), 2023. 7  
+[3] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, Varun Jampani, and Robin Rombach. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv.org, 2023. 2  
+[4] Shengqu Cai, Eric Ryan Chan, Songyou Peng, Mohamad Shahbazi, Anton Obukhov, Luc Van Gool, and Gordon Wetzstein. Diffdreamer: Towards consistent unsupervised single-view scene extrapolation with conditional diffusion models. In Proc. of the IEEE International Conf. on Computer Vision (ICCV), 2023. 2, 3  
+[5] David Charatan, Sizhe Lester Li, Andrea Tagliasacchi, and Vincent Sitzmann. Pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 3  
+[6] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. MVSNeRF: Fast generalizable radiance field reconstruction from multi-view stereo. In Proc. of the IEEE International Conf. on Computer Vision (ICCV), 2021. 1, 3  
+[7] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. 2022. 2  
+[8] Anpei Chen, Zexiang Xu, Xinyue Wei, Siyu Tang, Hao Su, and Andreas Geiger. Factor fields: A unified framework for neural fields and beyond. arXiv preprint arXiv:2302.01226, 2023.  
+[9] Anpei Chen, Zexiang Xu, Xinyue Wei, Siyu Tang, Hao Su, and Andreas Geiger. Dictionary fields: Learning a neural basis decomposition. ACM Trans. Graph., 2023. 2  
+[10] Anpei Chen, Haofei Xu, Stefano Esposito, Siyu Tang, and Andreas Geiger. Lara: Efficient large-baseline radiance fields. In Proc. of the European Conf. on Computer Vision (ECCV), 2024. 1  
+[11] Hansheng Chen, Jiatao Gu, Anpei Chen, Wei Tian, Zhuowen Tu, Lingjie Liu, and Hao Su. Single-stage diffusion nef: A unified approach to 3d generation and reconstruction. In ICCV, 2023. 3  
+[12] Yuedong Chen, Haofei Xu, Qianyi Wu, Chuanxia Zheng, Tat-Jen Cham, and Jianfei Cai. Explicit correspondence
+
+matching for generalizable neural radiance fields. arXiv preprint arXiv:2304.12294, 2023. 3  
+[13] Yutong Chen, Marko Mihajlovic, Xiyi Chen, Yiming Wang, Sergey Prokudin, and Siyu Tang. Splatformer: Point transformer for robust 3d gaussian splatting. arXiv preprint arXiv:2411.06390, 2024. 3  
+[14] Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. Mvsplat: Efficient 3d gaussian splattering from sparse multi-view images. arXiv preprint arXiv:2403.14627, 2024. 1, 3  
+[15] Zheng Chen, Chen Wang, Yuanchen Guo, and Song-Hai Zhang. Structnerf: Neural radiance fields for indoor scenes with structural hints. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 2022. 2  
+[16] François Darmon, Bénédicte Bascle, Jean-Clément Devaux, Pascal Monasse, and Mathieu Aubry. Improving neural implicit surfaces geometry with patch warping. 2022. 2  
+[17] Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan. Depth-supervised nerf: Fewer views and faster training for free. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022. 2  
+[18] Qiancheng Fu, Qingshan Xu, Yew-Soon Ong, and Wenbing Tao. Geo-neus: Geometry-consistent neural implicit surfaces learning for multi-view reconstruction. Advances in Neural Information Processing Systems (NeurIPS), 2022. 2  
+[19] Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, and Ben Poole. CAT3D: create anything in 3d with multi-view diffusion models. arXiv preprint arXiv:2405.10314, 2024. 3  
+[20] Weibo Gao, Qi Liu, Hao Wang, Linan Yue, Haoyang Bi, Yin Gu, Fangzhou Yao, Zheng Zhang, Xin Li, and Yuanjing He. Zero-1-to-3: Domain-level zero-shot cognitive diagnosis via one batch of early-bird students towards three diagnostic objectives. In Proc. of the Conf. on Artificial Intelligence (AAAI), 2024. 3  
+[21] Xun Guo, Mingwu Zheng, Liang Hou, Yuan Gao, Yufan Deng, Pengfei Wan, Di Zhang, Yufan Liu, Weiming Hu, Zhengjun Zha, et al. 12v-adapter: A general image-to-video adapter for diffusion models. In SIGGRA, 2024. 2  
+[22] Peter Hedman, Pratul P. Srinivasan, Ben Mildenhall, Jonathan T. Barron, and Paul Debevec. Baking neural radiance fields for real-time view synthesis. ICCV, 2021. 1, 2  
+[23] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems (NIPS), 2020. 2  
+[24] Wenbo Hu, Xiangjun Gao, Xiaoyu Li, Sijie Zhao, Xiaodong Cun, Yong Zhang, Long Quan, and Ying Shan. Depthcrafter: Generating consistent long depth sequences for open-world videos. arXiv preprint arXiv:2409.02095, 2024. 6  
+[25] Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accurate radiance fields. In SIGGRAPH Asia, 2024. 5, 7, 8  
+[26] Mohammad Mahdi Johari, Yann Lepoittevin, and François Fleuret. Geonerf: Generalizing nerf with geometry priors.
+
+In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022. 3  
+[27] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. on Graphics, 2023. 1, 2, 6, 7, 8  
+[28] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics, 36(4), 2017. 2, 6, 7  
+[29] Jiahe Li, Jiawei Zhang, Xiao Bai, Jin Zheng, Xin Ning, Jun Zhou, and Lin Gu. Dngaussian: Optimizing sparse-view 3d gaussian radiance fields with global-local depth normalization. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 2  
+[30] Zhengqi Li, Qianqian Wang, Noah Snavely, and Angjoo Kanazawa. Infinitenature-zero: Learning perpetual view generation of natural scenes from single images. In Proc. of the European Conf. on Computer Vision (ECCV), 2022. 2, 3  
+[31] Zhaoshuo Li, Thomas Müller, Alex Evans, Russell H Taylor, Mathias Unberath, Ming-Yu Liu, and Chen-Hsuan Lin. Neuralangelo: High-fidelity neural surface reconstruction. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023. 2  
+[32] Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey. Barf: Bundle-adjusting neural radiance fields. In Proc. of the IEEE International Conf. on Computer Vision (ICCV), 2021. 2  
+[33] Haotong Lin, Sida Peng, Zhen Xu, Yunzhi Yan, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Efficient neural radiance fields for interactive free-viewpoint video. In ACM Trans. on Graphics, 2022. 3  
+[34] Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao, Cheng Xin, Kun Wan, Lantao Yu, Qianyu Guo, Zixun Yu, Yawen Lu, Xuanmao Li, Xingpeng Sun, Rohan Ashok, Aniruddha Mukherjee, Hao Kang, Xiangrui Kong, Gang Hua, Tianyi Zhang, Bedrich Benes, and Aniket Bera. DL3DV-10K: A large-scale scene dataset for deep learning-based 3d vision. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 2, 3, 6, 7, 8  
+[35] Xi Liu, Chaoyi Zhou, and Siyu Huang. 3dgs-enhancer: Enhancing unbounded 3d gaussian splattering with view-consistent 2d diffusion priors. Advances in Neural Information Processing Systems (NeurIPS), 2024. 2  
+[36] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. 2020. 1, 2  
+[37] Michael Niemeyer, Jonathan Barron, Ben Mildenhall, Mehdi S. M. Sajjadi, Andreas Geiger, and Noha Radwan. Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs. 2022. 1, 2  
+[38] Julien Philip and Valentin Deschaintre. Floaters No More: Radiance Field Gradient Scaling for Improved Near-Camera Training. In Eurographics Symposium on Rendering, 2023. 2
+
+[39] Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. In Proc. of the International Conf. on Learning Representations (ICLR), 2023. 2  
+[40] Barbara Roessle, Jonathan T. Barron, Ben Mildenhall, Pratul P. Srinivasan, and Matthias Nießner. Dense depth priors for neural radiance fields from sparse input views. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022. 1  
+[41] Kyle Sargent, Zizhang Li, Tanmay Shah, Charles Herrmann, Hong-Xing Yu, Yunzhi Zhang, Eric Ryan Chan, Dmitry Lagun, Li Fei-Fei, Deqing Sun, and Jiajun Wu. Zeronvs: Zero-shot 360-degree view synthesis from a single image. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 3, 7  
+[42] Nagabhushan Somraj, Adithyan Karanayil, and Rajiv Soundararajan. SimpleNeRF: Regularizing sparse input neural radiance fields with simpler solutions. In SIGGRAPH Asia, 2023. 7  
+[43] Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden, Will Saxton, Jean Yu, Estelle Aflalo, Shao-Yen Tseng, Fabio Nonato, Matthias Müller, and Vasudev Lal. LDM3D: latent diffusion model for 3d. arXiv preprint arXiv:2305.10853, 2023. 3, 5, 7  
+[44] Stanislaw Szymanowicz, Eldar Insafutdinov, Chuanxia Zheng, Dylan Campbell, João F. Henriques, Christian Rupprecht, and Andrea Vedaldi. Flash3d: Feed-forward generalisable 3d scene reconstruction from a single image. arXiv preprint arXiv:2406.04343, 2024. 3  
+[45] Haiping Wang, Yuan Liu, Ziwei Liu, Zhen Dong, Wenping Wang, and Bisheng Yang. Vistadream: Sampling multiview consistent images for single-view scene reconstruction. arXiv preprint arXiv:2410.16892, 2024. 3  
+[46] Frederik Warburg*, Ethan Weber*, Matthew Tancik, Aleksander Holski, and Angjoo Kanazawa. Nerfbusters: Removing ghostly artifacts from casually captured nerfs. 2023. 1  
+[47] Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel view synthesis with diffusion models. In Proc. of the International Conf. on Learning Representations (ICLR), 2023. 3  
+[48] Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, and Aleksander Holynski. Reconfusion: 3d reconstruction with diffusion priors. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 2, 7  
+[49] Jamie Wynn and Daniyar Turmukhambetov. Diffusionerf: Regularizing neural radiance fields with denoising diffusion models. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023. 7  
+[50] Jianfeng Xiang, Jiaolong Yang, Binbin Huang, and Xin Tong. 3d-aware image generation using 2d diffusion models. In ICCV, 2023. 3  
+[51] Jinbo Xing, Menghan Xia, Yong Zhang, Haoxin Chen, Wangbo Yu, Hanyuan Liu, Xintao Wang, Tien-Tsin Wong,
+
+and Ying Shan. Dynamiccafter: Animating open-domain images with video diffusion priors. Proc. of the European Conf. on Computer Vision (ECCV), 2024. 2, 3, 5, 6  
+[52] Haofei Xu, Anpei Chen, Yuedong Chen, Christos Sakaridis, Yulun Zhang, Marc Pollefeys, Andreas Geiger, and Fisher Yu. Murf: Multi-baseline radiance fields. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 3  
+[53] Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, and Marc Pollefeys. Depthsplat: Connecting gaussian splatting and depth. arXiv preprint arXiv:2410.13862, 2024. 3  
+[54] Chen Yang, Sikuang Li, Jiemin Fang, Ruofan Liang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, and Qi Tian. Gaussianobject: High-quality 3d object reconstruction from four views with gaussian splatting. ACM Trans. Graph., 2024. 2  
+[55] Jiawei Yang, Marco Pavone, and Yue Wang. Freenerf: Improving few-shot neural rendering with free frequency regularization. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023. 1, 2, 7  
+[56] Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. Plenoctrees for real-time rendering of neural radiance fields. In Proc. of the IEEE International Conf. on Computer Vision (ICCV), 2021. 1, 2  
+[57] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelnerf: Neural radiance fields from one or few images. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021. 1  
+[58] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelNeRF: Neural radiance fields from one or few images. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021. 3  
+[59] Hong-Xing Yu, Haoyi Duan, Junhwa Hur, Kyle Sargent, Michael Rubinstein, William T. Freeman, Forrester Cole, Deqing Sun, Noah Snavely, Jiajun Wu, and Charles Herrmann. Wonderjourney: Going from anywhere to everywhere. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 2, 3  
+[60] Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis. arXiv preprint arXiv:2409.02048, 2024. 1, 3  
+[61] Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, and Andreas Geiger. Monosdf: Exploring monocular geometric cues for neural implicit surface reconstruction. In Advances in Neural Information Processing Systems (NeurIPS), 2022. 1, 2, 6  
+[62] Jiahui Zhang, Fangneng Zhan, Muyu Xu, Shijian Lu, and Eric P. Xing. Fregs: 3d gaussian splatting with progressive frequency regularization. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 2, 7  
+[63] Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun. Nerf++: Analyzing and improving neural radiance fields. arXiv:2010.07492, 2020. 1  
+[64] Zehao Zhu, Zhiwen Fan, Yifan Jiang, and Zhangyang Wang. FSGS: real-time few-shot view synthesis using gaussian
+
+splatting. In Proc. of the European Conf. on Computer Vision (ECCV), 2024. 2, 7, 8  
+[65] Chen Ziwen, Hao Tan, Kai Zhang, Sai Bi, Fujun Luan, Yi-ong Hong, Li Fuxin, and Zexiang Xu. Long-lrm: Long-sequence large reconstruction model for wide-coverage gaussian splats. arXiv preprint 2410.12781, 2024. 3

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+# GenPC: Zero-shot Point Cloud Completion via 3D Generative Priors
+
+An Li† Zhe Zhu† Mingqiang Wei‡ Nanjing University of Aeronautics and Astronautics
+
+{lian, zhuzhe0619, mqwei}@nuaa.edu.cn*
+
+# Abstract
+
+Existing point cloud completion methods, which typically depend on predefined synthetic training datasets, encounter significant challenges when applied to out-of-distribution, real-world scans. To overcome this limitation, we introduce a zero-shot completion framework, termed GenPC, designed to reconstruct high-quality real-world scans by leveraging explicit 3D generative priors. Our key insight is that recent feed-forward 3D generative models, trained on extensive internet-scale data, have demonstrated the ability to perform 3D generation from single-view images in a zero-shot setting. To harness this for completion, we first develop a Depth Prompting module that links partial point clouds with image-to-3D generative models by leveraging depth images as a stepping stone. To retain the original partial structure in the final results, we design the Geometric Preserving Fusion module that aligns the generated shape with input by adaptively adjusting its pose and scale. Extensive experiments on widely used benchmarks validate the superiority and generalizability of our approach, bringing us a step closer to robust real-world scan completion. Our code is available at https://github.com/liannuaa/GenPC.
+
+# 1. Introduction
+
+Point clouds, as an essential form of 3D representation, are widely used in various applications. However, due to factors such as self-occlusion, camera viewpoint limitations, and sensor resolution, the acquired point clouds are often incomplete. This issue significantly hinders downstream tasks. Therefore, developing effective and robust methods for completing real-world partial point clouds is crucial for achieving a comprehensive understanding of the real world.
+
+In recent years, numerous deep learning-based point cloud completion methods [18, 40, 45, 47-49, 55] have shown remarkable success. These approaches utilize carefully designed neural networks to extract shape patterns from
+
+![](images/8cebc50b1fcf759fd32dc7144ec1075ed34979684af2839a34d0bf2e14f75504.jpg)
+
+![](images/d07657684eeedc27924e359dba48eeb87ce379e79c1793826197ddd3a7003aa7.jpg)  
+Figure 1. Difference between our GenPC with previous zero-shot point cloud completion method [19]. (a) SDS-Complete [19] uses the SDS loss to directly extract prior knowledge from a 2D diffusion model, featuring time-consuming optimization and suboptimal completion results. (b) The proposed GenPC leverages explicit priors provided by a 3D generative model, achieving improved completion quality with significantly reduced inference time.
+
+input point clouds, enabling them to generate detailed geometric structures to complete missing portions of the point cloud. Although these techniques perform well on trained or similar categories, they rely on labeled 3D training data and exhibit limited generalization to categories unseen during training. Moreover, constrained by domain gaps between synthetic training data and real-world scans, these models tend to perform poorly when applied to downstream tasks.
+
+With the impressive zero-shot generation capabilities of pre-trained 2D diffusion models [31], numerous studies [24, 27, 33] have emerged that utilize these models for 3D generation tasks. Enlightened by these successes, sdscomeplete [19] first utilized 2D priors for zero-shot shape completion. This method fits the input partial point cloud surface using Signed Distance Functions (SDF) and leverages Score Distillation Sampling (SDS) [27] to extract 2D diffusion priors for completion. Later, Huang et al. [17] proposed a similar SDS-based framework, but used 3D Gaussian splatt ing [21] to initialize the partial point cloud as 3D Gaussians. Although these methods demonstrate improved zero-shot completion capabilities compared to training-based counterparts, they are time-consuming, as they require training a radiance field from scratch for each incomplete point cloud.
+
+![](images/a971c585b243cdbf8747ded4469ae7d174b538df321c186983dcae28a871c9bc.jpg)  
+Figure 2. The architecture of GenPC. The Depth Prompting module first prompts the depth-guided 2D generative model with the partial input and generates an RGB image, which is fed into an image-to-3D generative model, producing a 3D shape. The Geometric Preserving Fusion module then integrates the generated shape with the partial point cloud.
+
+Additionally, SDS loss often leads to coarse geometric details, limiting their reconstructing quality.
+
+Recently, scalable network architectures and large-scale 3D datasets have propelled the success of feed-forward 3D generative models [15, 16, 43]. Once trained, these models achieve impressive zero-shot generation quality within seconds. This raises an intriguing question: "Can we leverage this 3D generative capability for point cloud completion?" To answer it, we introduce a novel zero-shot point cloud completion framework, GenPC. Unlike previous 2D diffusion-based approaches [17, 19], GenPC utilizes explicit 3D priors from an image-to-3D generative model to enhance zero-shot completion quality, significantly improving inference speed.
+
+As illustrated in Figure 2. To leverage the powerful zero-shot generation capabilities of the image-to-3D generative model, we first need an image input. To address the issue of partial point clouds not directly providing image input for these models, we introduce a Depth Prompting module. This module estimates the scanning viewpoint of the partial point cloud and extracts depth, effectively bridging the modality gap between the point cloud and the generative model. After generating the 3D shape, a significant issue arises: the generated 3D shape may differ from the input partial point cloud in terms of scale, pose, and shape. To align it with the input point cloud and retain the original geometric structure, we introduce the Geometric Preserving Fusion module. This module first dynamically adjusts the scale and pose using a scaling factor at both geometric and semantic levels. In addition, we can further refine the point cloud using the SDS loss, minimizing shape detail discrepancies caused by multi-stage error accumulation. By leveraging explicit geometric priors offered by the 3D generative model, our approach avoids the need for optimization from scratch, enabling faster inference and superior completion quality.
+
+In summary, our contributions are as follows:
+
+- We design a novel zero-shot completion framework called GenPC, which significantly improves real-world scan completion by prompting a pre-trained 3D generative model.  
+- We propose a Depth Prompting module to bridge the
+
+modality gap between partial scans and generative models by utilizing depth images as a stepping stone.
+
+- We introduce the novel Geometric Preserving Fusion module for refining the initial generated results. It adaptively aligns the generated content with partial input, ensuring that the final result is both semantically accurate and structurally faithful.  
+- Extensive experiments demonstrate that GenPC achieves state-of-the-art performance on real-world datasets while significantly reducing completion time.
+
+# 2. Related Work
+
+# 2.1. Point cloud completion
+
+Early methods [8, 13, 34, 42] primarily used voxels as intermediate representations and performed completion using 3D convolutions. However, they are often limited by the resolution of the voxels. With the development of point-based networks like PointNet [28], various point cloud tasks can be handled by end-to-end networks [2, 29, 30, 46, 54]. Among them, PCN [49] is the first work that directly generates high-resolution complete point clouds in a coarse-to-fine manner for point cloud completion. A similar generation strategy is also adopted in a series of following works [18, 23, 36, 37, 39]. Transformer [35] has also been leveraged in recent works. PointTr [47] treats the point cloud as a token sequence, using transformer encoder-decoder to predict the missing parts. SnowflakeNet [40] designs a transformer decoder with skip connections to refine the point cloud. Another line of works [53, 56] enhances completion performance using 2D information. Different from the above approaches, SVDFormer [55] and GeoFormer [45] project point clouds into 2D depth images, requiring information from only partial input.
+
+Although these methods perform well on synthetic datasets, their reliance on training data causes performance degradation on out-of-distribution real-world scans and previously unseen categories. Recent unsupervised [4, 38, 41, 51] and self-supervised approaches [6, 14, 26] have allevi
+
+ated this issue to some extent; however, the completion results remain suboptimal. To address these limitations, SDS-Complete [19] formulates point cloud completion as a test-time optimization problem, introducing a zero-shot method that fits a Signed Distance Function (SDF) to the input partial point cloud. It leverages Score Distillation Sampling (SDS) to extract 2D priors from the Stable Diffusion [31] model to complete the missing regions. Subsequently, Huang et al.[17] propose initializing the partial point cloud as 3D Gaussians and distilling prior knowledge from zero123[24]. Although these methods exhibit impressive zero-shot completion capabilities, they require optimization from scratch for each incomplete point cloud, making them time-intensive. Moreover, reliance on implicit 2D diffusion priors limits the reconstruction of fine geometric details. In this work, we leverage explicit priors from a pre-trained 3D generative model to enhance zero-shot point cloud completion quality while significantly reducing processing time.
+
+# 2.2.3D Generation
+
+DreamFusion [27] is the first method to use 2D priors for 3D generation, introducing Score Distillation Sampling (SDS) to extract 2D priors from a pretrained diffusion model and guide the 3D generation process, inspiring numerous impressive works. Magic3D [22] adopts DMTet [32] as the 3D representation instead of NeRF [25] and then performs optimization using SDS. Fantasia3D [3] decouples the optimization of geometry and material properties. With the emergence of 3D Gaussian Splatting [21], a highly expressive 3D representation, the optimization time for 3D generation with SDS has been significantly reduced. DreamGaussian [33] firstly attempts to use SDS optimization for 3D Gaussians, reducing the optimization time to just a few minutes while achieving excellent results. GaussianDreamer [44] initializes 3D Gaussians using point cloud priors, yielding impressive results. Although the above methods are effective, they require several minutes or even hours for optimization. The emergence of large-scale datasets [9, 10] has driven the development of faster feed-forward methods. Once trained, these methods can generate 3D objects within seconds through a single forward inference. Recently, LRM [16] demonstrated that a regression model can predict a NeRF from a single image within seconds. Based on this, InstantMesh [43] generates additional multi-view images from a single image and then reconstructs the mesh. However, both methods are limited by resolution. To address this, LGM [15] introduces an efficient representation of multi-view Gaussian features, enabling the prediction of high-resolution 3D Gaussian models.
+
+These feed-forward methods can generate high-quality 3D objects from a single image in a very short time while demonstrating strong generalization ability. We are motivated to leverage this advantage for point cloud completion, aiming to achieve superior zero-shot completion results
+
+![](images/5be1b9d989215545a5fe58cfc1bbe902ac272ed371f84f1a5ab065c6f15f360f.jpg)  
+Figure 3. Illustration of Depth Prompting. (a) Overview. First, we uniformly position cameras around the partial point cloud  $P_{in}$  to select a scanning viewpoint. From this viewpoint, we project to obtain depth and the corresponding mask, and then apply mask inpainting to achieve high-quality depth. (b) Viewpoint Selection: For each viewpoint  $V_{i}$ , we perform a spherical flip on  $P_{in}$  for each camera to obtain a mirrored point cloud  $\vec{P_{in}}$ , then create a convex hull around  $\vec{P_{in}} \cup V_{i}$ , identifying the points on this hull as visible points. The camera with the greatest number of visible points is chosen as the scan viewpoint  $V_{scan}$ . The top of (b) is a true viewpoint, all points lie on the convex hull. The bottom of (b) is the opposite viewpoint, only two lie on the convex hull.
+
+![](images/162ab0d8ba61e30050330e7e3421fc2d0018ac17bac869354c3a98f8cfc54601.jpg)
+
+while reducing optimization time.
+
+# 3. Method
+
+The input of GenPC consists of a partial point cloud  $P_{in} \subseteq \mathbb{R}^{N \times 3}$  and a corresponding text prompt  $T_{in}$ , where  $N$  represents the number of points in  $P_{in}$ . Our goal is to obtain a complete shape  $P_{out}$  that preserves the original structure in input. As illustrated in Figure 2, our method seamlessly incorporates an image-to-3D generative model into the point cloud completion process through the introduction of two innovative modules. First, current image-to-3D models are designed to accept only 2D images as input. To adapt them for point cloud completion, we introduce the Depth Prompting module, which leverages depth images as a stepping stone to bridge the modality gap between partial point clouds and generative models. After generating a 3D shape from the image-to-3D model, a key challenge arises: the original points in  $P_{in}$  are not retained in the generated shape. To address this, we propose the Geometric Preserving Fusion module, which further aligns the initial generated shape with  $P_{in}$ , ensuring that the final result is both semantically accurate and structurally faithful.
+
+# 3.1. Depth Prompting
+
+Figure 3 describes the proposed Depth Prompting module. This module generates an RGB image from the input partial point cloud  $P_{in}$  by first projecting it to a coarse depth map  $D_{raw}$  as an intermediary. Through masked inpainting of missing areas, a smooth depth map  $D_{c}$  is produced to enhance robustness to point cloud sparsity. Finally,  $D_{c}$  and
+
+the text prompt  $T_{in}$  are input into a depth-conditioning ControlNet [52] to produce the corresponding RGB image. To project a high-quality depth image from an incomplete point cloud, we propose to find the viewpoint from which the point cloud was captured. Although Huang et al. [17] employs a distance-based method for viewpoint estimation, this approach can sometimes result in issues such as depth reversal. To address these problems, we follow the approach proposed by [20], framing the viewpoint estimation as a hidden point removal task. As illustrated in Figure 3(a), We start by evenly positioning  $M$  cameras  $V_{i}$  (where  $i = 1,2,\dots ,M$ ) around the input point cloud  $P_{in}$ . For each camera, as shown in Figure 3(b), we perform a spherical flip on  $P_{in}$  to obtain a mirrored point cloud  $\hat{P_{in}}$ . We then create a convex hull around  $\hat{P_{in}}\cup V_i$ , identifying the points on this hull as visible points. The camera with the greatest number of visible points is chosen as the scan viewpoint  $V_{scan}$ . By constructing the convex hull, our approach effectively prevents depth reversal and projects  $P_{in}$  to an initial depth map  $D_{raw}$ .
+
+However, some partial point clouds, such as cars in the KITTI dataset, are extremely sparse, leading to sparse depth projections that hinder subsequent completion. To address this issue, we use a pre-trained 2D inpainting diffusion model [11] to fill the missing holes in the sparse depth  $D_{raw}$ , resulting in a complete, high-quality depth image  $D_{c}$ . To create an inpainting mask, we first project the point cloud with a large pixel size to obtain  $M_{FULL}$ . We then apply an XOR operation between  $M_{FULL}$  and the inverted depth map  $(\neg D_{raw})$ , which generates the required mask for inpainting. Using this mask, the inpainting model fills the missing depth regions and smooths any irregular edges, producing  $D_{c}$ . Note that any inpainting model capable of filling masked areas can be applied here. Finally, we use  $D_{c}$  as conditioning input, along with the text prompt  $T_{in}$ , to generate the image  $I_{gen}$  corresponding to the partial input. This is achieved by leveraging a pre-trained depth-conditional image generation model, such as ControlNet [52].
+
+# 3.2. Geometric Preserving Fusion
+
+In the Dynamic Scale Adaptation stage, we first colorize the input point cloud  $P_{in}$  using the generated image  $I_{gen}$ , resulting in  $P_{partial}$ . Then,  $P_{partial}$  and  $P_{gen}$  are aligned at dynamic scales, producing an initial, completed point cloud  $P_{all}$ . Then, we apply an optional Refining stage. In this stage,  $P_{all}$  is initialized as 3D Gaussians  $G_{all}$ , with different regions having distinct Gaussian parameter settings to preserve the original geometric details of the input point cloud while optimizing the shape of missing areas. This step helps to eliminate error accumulation and enhance overall completion quality.
+
+# 3.2.1 Dynamic Scale Adaptation
+
+We first use the generated image  $I_{gen}$  to obtain the 3D shape  $P_{gen}$  through the Image-to-3D generation model. Thanks to the powerful zero-shot generation performance of the pre-trained models, the generated  $I_{gen}$  and  $P_{gen}$  are highly consistent in category and shape with the input point cloud. Next, we use  $P_{gen}$  to fill in the missing areas of the input point cloud, as shown in Figure 4. To improve the fusion process, we color  $P_{in}$  using the RGB information from  $I_{gen}$ , creating a colored partial point cloud  $P_{partial}$ . Since different parts of the object exhibit distinct colors, these colors can be regarded as semantic cues, enriching the fusion with additional contextual information for more accurate integration. Both  $P_{partial}$  and  $P_{gen}$  are then normalized to a unified scale within the range [-0.5, 0.5], reducing the search space for subsequent integration.
+
+To eliminate the impact of both scale and pose differences, we scale  $P_{gen}$  within the range [0.8, 1.2] at intervals of 0.1, and perform ICP [1] alignment at each scale, using the Chamfer Distance to evaluate the alignment results. We treat the color of the point cloud as semantic information, which allows us to not only supervise the alignment geometrically but also consider color information as an additional supervision signal. During the iterative registration, we calculate both the Euclidean and RGB Chamfer Distance between  $P_{partial}$  and  $P_{gen}$ . The Chamfer Distance ensures accurate geometric alignment, while the RGB Chamfer Distance supervises the alignment of the semantic information, thereby improving the overall quality of the fusion. Together, they form the following objective: arg  $\min_{s\in [0.8,1.2]}(\alpha \cdot CD_{XYZ}(P_{partial},s\cdot P_{gen}) + \beta \cdot CD_{RGB}(P_{partial},s\cdot P_{gen}))$
+
+where  $\alpha$  and  $\beta$  are regularization terms, and  $s$  represents the scaling factor. Finally, we select the registration result that minimizes the combined XYZ and RGB Chamfer distances and remove points from  $P_{\mathrm{gen}}$  that are adjacent to  $P_{\mathrm{partial}}$  to avoid point cloud overlap, resulting in the missing portion of the point cloud  $P_{\mathrm{miss}}$ . Together,  $P_{\mathrm{miss}}$  and  $P_{\mathrm{partial}}$  form the preliminary complete point cloud  $P_{\mathrm{all}}$ .
+
+# 3.2.2 Refining
+
+To further enhance the accuracy of point cloud completion and reduce error accumulation, we optimize the preliminarily completed point cloud, as shown in Figure 4. First, the point cloud is initialized as 3D Gaussians, and then distinct parameter configurations are applied to different parts of the 3D Gaussian. This approach maintains the integrity of the original part  $G_{\text{partial}}$  while optimizing the geometry of the missing part  $G_{\text{miss}}$ , thereby improving the overall quality and consistency of the point cloud completion.
+
+Partial setup: For the partial point cloud  $P_{\text{partial}}$ , we initialize it as a 3D Gaussians  $G_{\text{partial}}$ . To preserve the original geometry, we fix parameters such as the coordinates, color,
+
+![](images/1e0eb2b780dbbfcdbe8ffe93aca5ac32e11041a1b018f4778f72d7996a560465.jpg)  
+Figure 4. Illustration of Geometric Preserving Fusion. In the Dynamic Scale Adaptation stage, an optimal scale factor is selected to align  $P_{\text{partial}}$  and  $P_{\text{gen}}$ , producing an initial completed point cloud  $P_{\text{all}}$ . Then, to reduce the accumulated error in the previous steps, an optional Refining operation can be performed, where  $P_{\text{all}}$  is initialized as 3D Gaussians and optimized by the SDS loss.
+
+scale, and opacity, making them non-trainable. This ensures that the geometric of the partial point cloud remains unaffected during the optimization process, thereby maintaining consistency with the original input.
+
+Miss setup: For the missing point cloud  $P_{miss}$ , we initialize it as a 3D Gaussians  $G_{miss}$ . The scale remains fixed, as these points are uniformly sampled from the mesh surface and already have a reasonable scale. Opacity is set to 1 and remains non-trainable to ensure the stability of the Gaussian points on the surface. The color parameters are not fixed, but the learning rate is set relatively low because color carries semantic information. This allows for adjustments to the color during optimization while preserving its semantic characteristics as much as possible. The Gaussian coordinates are the main focus of the optimization, ensuring that the missing point cloud fits the shape of the partial input.
+
+SDS Guidance Optimization: Next, under the viewpoint  $V_{\text{scan}}$ , we render an image  $I_{\text{optim}}$  and a depth map  $D_{\text{optim}}$  from  $G_{\text{partial}}$ . We then incorporate both  $G_{\text{miss}}$  and  $G_{\text{partial}}$ , and render an image  $\tilde{I}_{\text{optim}}^i$  from a random viewpoint. This process is iterated multiple times, where in each iteration, we apply SDS to extract 2D priors from the pre-trained novel view synthesis diffusion model Zero123 [24], refining  $G_{\text{miss}}$  based on  $I_{\text{optim}}$  until satisfactory completion is achieved. The SDS Loss can be formulated as:
+
+$$
+\nabla_ {G _ {a l l}} \mathcal {L} _ {\mathrm {S D S}} = \mathbb {E} _ {t, p, \epsilon} \left[ (\epsilon_ {\phi} (I _ {\mathrm {o p t i m}}; t, \tilde {I} _ {\mathrm {o p t i m}} ^ {i}, \Delta p) - \epsilon) \frac {\partial I _ {\mathrm {o p t i m}}}{\partial G _ {a l l}} \right]
+$$
+
+where  $\epsilon_{\phi}(\cdot)$  is the predicted noise from the 2D diffusion prior  $\phi$ ,  $t$  is the time step,  $\epsilon$  is the standard noise and  $\Delta p$  represents the relative camera pose change from the scan viewpoint  $V_{scan}$ , respectively.
+
+Additionally, to prevent other 3D Gaussians in the optimization process from affecting the geometric information
+
+of the input in the  $G_{\text{partial}}$  region, we also render images  $I_{\text{optim}}^i$  and depth maps  $D_{\text{optim}}^i$  under the viewpoint  $V_{\text{scan}}$  during the optimization iterations, and set a preservation loss  $L_{\text{Presv}}$  for the partial region:
+
+$$
+L _ {P r e s v} = w _ {1} \cdot \mathrm {M S E} (I _ {o p t i m}, I _ {o p t i m} ^ {i}) + w _ {2} \cdot \mathrm {M S E} (D _ {o p t i m}, D _ {o p t i m} ^ {i})
+$$
+
+where MSE is the Mean Squared Error between the optimized and reference images  $I_{optim}^{i}$  and  $I_{optim}$ , as well as the depth maps  $D_{optim}^{i}$  and  $D_{optim}$ .  $w_{1}$  and  $w_{2}$  are weights that balance the importance of image and depth losses. By incorporating  $L_{Presv}$  and  $L_{SDS}$ , our method preserves the geometry of the partial point cloud while optimizing the missing areas, reducing multi-stage error accumulation and improving the overall completion quality.
+
+# 4. Experiment
+
+# 4.1. Dataset and Evaluation Metric
+
+We validate our method on three real-world datasets Redwood [5], ScanNet [7], and KITTI [12]. For the Redwood [5] dataset, we follow prior approaches [19], using single-view scans as partial inputs and multi-frame aggregations as ground truth. Since previous deep learning-based methods were trained on standardized synthetic datasets, we normalize the Redwood dataset point clouds to the range [-0.5, 0.5] and set the elevation angle to  $0^{\circ}$  to ensure a fair comparison with their input requirements. For the ScanNet dataset, which contains partial point clouds extracted from RGB-D scans, we focused on tables and chairs due to their complex structures and additional supports that introduce challenging self-occlusion cases for our method. For each category, we select 16 objects for validation. In addition, we used the
+
+Table 1. Quantitative results on the Redwood [5] [19] dataset. Ours* represents our results without Refining ( $\ell^1$  CD and  $\mathrm{EMD} \times 10^{2}$ ).  
+
+<table><tr><td>Objects Metrics</td><td>Table CD/EMD</td><td>Swivel-Chair CD/EMD</td><td>Arm-Chair CD/EMD</td><td>Chair CD/EMD</td><td>Sofa CD/EMD</td><td>Vase CD/EMD</td><td>Off-Can CD/EMD</td><td>Vespa CD/EMD</td><td>Wheelie-Bin CD/EMD</td><td>Tricycle CD/EMD</td><td>Avg↓ CD/EMD</td></tr><tr><td>PoinTr [47]</td><td>1.86/3.50</td><td>4.08/8.49</td><td>1.95/4.22</td><td>2.69/5.38</td><td>2.96/5.02</td><td>4.05/7.28</td><td>4.82/6.92</td><td>2.00/4.06</td><td>2.78/3.51</td><td>1.70/3.99</td><td>2.89/5.24</td></tr><tr><td>SnowflakeNet [40]</td><td>3.44/6.92</td><td>3.40/7.58</td><td>2.15/4.45</td><td>2.35/5.28</td><td>2.64/5.00</td><td>4.63/7.69</td><td>4.36/6.75</td><td>2.07/4.42</td><td>3.14/5.03</td><td>1.44/3.32</td><td>2.96/5.64</td></tr><tr><td>Adapointr [48]</td><td>5.20/6.44</td><td>5.09/8.03</td><td>3.67/4.53</td><td>4.40/5.96</td><td>3.59/5.18</td><td>6.23/7.56</td><td>6.04/7.69</td><td>3.21/4.65</td><td>4.13/7.63</td><td>2.90/4.25</td><td>4.45/6.19</td></tr><tr><td>SDS-Complete [19]</td><td>1.67/2.92</td><td>2.24/3.09</td><td>2.18/3.16</td><td>2.62/3.61</td><td>2.95/4.56</td><td>3.26/5.89</td><td>4.03/4.36</td><td>3.46/5.94</td><td>2.69/3.21</td><td>2.11/3.87</td><td>2.72/4.06</td></tr><tr><td>Ours*</td><td>1.41/2.24</td><td>1.69/2.37</td><td>1.38/1.76</td><td>1.47/2.48</td><td>1.61/2.93</td><td>3.15/5.24</td><td>3.04/4.62</td><td>1.59/2.83</td><td>2.65/3.64</td><td>1.79/3.52</td><td>1.98/3.16</td></tr><tr><td>Ours</td><td>1.28/2.07</td><td>1.43/2.29</td><td>1.16/1.68</td><td>1.36/2.20</td><td>1.58/2.78</td><td>2.86/4.85</td><td>2.72/4.36</td><td>1.36/2.47</td><td>2.31/3.17</td><td>1.38/2.97</td><td>1.74/2.88</td></tr></table>
+
+![](images/ccfcac8fa469835c50376103717c005a9bb30847676e35309c98099cfd945ce8.jpg)  
+Figure 5. Visual comparisons with recent methods [40, 47, 48] on the Redwood dataset.
+
+Table 2. Quantitative results on ScanNet  $\left( {{\ell }^{1}\mathrm{{CD}}\text{and}\mathrm{{EMD}} \times  {10}^{2}}\right)$  .  
+
+<table><tr><td>Methods Metrics</td><td>SnowflakeNet CD/EMD</td><td>AdaPoinTr CD/EMD</td><td>Ours CD/EMD</td></tr><tr><td>Table</td><td>1.80/4.78</td><td>2.34/6.12</td><td>1.67/3.86</td></tr><tr><td>Chair</td><td>1.68/3.76</td><td>2.07/3.09</td><td>1.57/3.24</td></tr><tr><td>Avg</td><td>1.74/4.27</td><td>2.21/2.38</td><td>1.62/3.55</td></tr></table>
+
+Table 3. Performance of ablation variant C (w/o depth inpainting) on different datasets. Variant C performs relatively well on Redwood's dense point clouds but shows significant performance drops with the sparse point clouds in ScanNet ( $\ell^1$  CD and EMD  $\times 10^2$ ).  
+
+<table><tr><td>Methods</td><td>variant C</td><td>Ours</td></tr><tr><td>Metrics</td><td>CD/EMD</td><td>CD/EMD</td></tr><tr><td>Redwood</td><td>2.23/3.60</td><td>1.74/2.88</td></tr><tr><td>ScanNet</td><td>3.57/6.10</td><td>1.62/3.55</td></tr></table>
+
+Table 4. Performance of ablation variants on the Redwood dataset  $(\ell^1$  CD and EMD  $\times 10^{2}$  
+
+<table><tr><td>Methods</td><td>CD↓</td><td>EMD↓</td></tr><tr><td>A: w/o Viewpoint Selection</td><td>2.44</td><td>3.79</td></tr><tr><td>B: w/o ControlNet</td><td>4.31</td><td>6.80</td></tr><tr><td>C: w/o Depth Inpainting</td><td>2.23</td><td>3.60</td></tr><tr><td>D: w/o 3D Generative Model</td><td>4.65</td><td>6.13</td></tr><tr><td>E: w/o Dynamic Scale Adaptation</td><td>4.38</td><td>4.52</td></tr><tr><td>F: w/o SDS Optimization</td><td>1.98</td><td>3.16</td></tr><tr><td>Ours</td><td>1.74</td><td>2.88</td></tr></table>
+
+ground truth provided by [50], consisting of 2048 points for quantitative evaluation.
+
+For quantitative evaluation, we followed prior methods by sampling 16,384 points from the Redwood dataset and 2,048 points from the ScanNet dataset using Farthest Point Sampling (FPS) to enable direct comparison with the ground truth. To assess the quality of point cloud completion, we used the widely adopted Chamfer Distance (CD) and Earth Mover's Distance (EMD) metrics, scaling the loss values by a factor of 100 for clearer interpretation. We also conduct a qualitative evaluation on KITTI [12] to assess the performance on sparse LiDAR scans.
+
+# 4.2. Results on the Redwood dataset
+
+The quantitative and qualitative results are presented in Table 1 and Figure 5. With or without the SDS Refining step, GenPC consistently achieves state-of-the-art performance across the entire dataset. These results indicate that existing learning-based methods [40, 47, 48] struggle to complete
+
+![](images/7a30ba357cd2dc3e91d13aad2d32633566571091e09ac912d64e7724b6918aac.jpg)  
+Figure 6. Visual comparisons with recent methods [40, 48] on the ScanNet dataset.
+
+![](images/e081dcc43dbac5e0e92c97b7d453683622ea60af1726ebd4fb8a884c5f193f7e.jpg)  
+Figure 7. (Top) Visual comparisons with AdaPoinTr [48] on KITTI [12]. (Bottom) We display the point clouds in different colors: blue for the Partial Input, red for AdaPoinTr, and gray for Ours. Our result maintains a consistent scale with the input.
+
+out-of-distribution data, even when these data belong to categories seen during training (e.g., chairs and couches). Additionally, these methods are sensitive to scale variations, leading to inconsistent outputs when the input scale changes. Compared with the only zero-shot method, SDS-Complete [19], GenPC achieves an average reduction in CD by  $36\%$  and EMD by  $29\%$ . Furthermore, Figure 5 clearly illustrates that GenPC outputs finer structure details than SDS-Complete, attributed to the rich geometric priors provided by the pre-trained 3D generative model.
+
+# 4.3. Results on the ScanNet dataset
+
+Comparison with two cutting-edge learning-based methods [40, 48] are presented in Table 2 and Figure 6. Our method demonstrates advanced performance in completion quality, maintaining reliable results even when dealing with sparse and noisy point clouds. As shown in Figure 6, our method generates completion outputs with high fidelity to the
+
+# Variant A
+
+Our  $D_{c}$  Reversed  $D_{c}$
+
+![](images/d6b05be0608ff8201b2abdf614b9dcd539097c59ae63fc1dad36382a5f085f28.jpg)
+
+![](images/ee84b57e1bf6d683e1f5f0b7fe71f347f4322cdfee91b8f8722f5e996ca119ed.jpg)  
+$I_{gen}$  
+Figure 8. Depth and RGB images produced by variant A (w/o Viewpoint Selection) and Variant C (w/o Depth Inpainting). (Left) The above is the complete depth  $D_{c}$  obtained from the scanning viewpoint and the opposite viewpoint, and below are the corresponding generated images  $I_{gen}$ . (Right) The above are the sparse depth  $D_{\mathrm{raw}}$  and the complete depth  $D_{c}$ , and below are the generated corresponding images  $I_{gen}$ .
+
+# Variant C
+
+![](images/956a0d176db7b1dd347ebaf8f2c64a44f4481bc61541e82ed8025e3b7854eca1.jpg)  
+$D_{raw}$
+
+![](images/7c82c68b4b46dbe8b95d3f9c41af2c0b7001af81dec771bebd2c52d16e703203.jpg)  
+$D_{c}$
+
+![](images/e308330ea192c8c4e73dc28216707a622d52d37dbba851946ad2c490a1739831.jpg)  
+$I_{gen}$
+
+![](images/d72b9937d45a28869daf2fd93bb97b78dd7c9f3cdce9dcdcbbc6cf42c8d6598e.jpg)
+
+input point cloud and rich geometric details, while learning-based methods are affected by domain gaps, leading to numerous noisy points in their results.
+
+# 4.4. Results on KITTI
+
+A qualitative comparison on the KITTI dataset is presented in Figure 7, which shows that GenPC produces results with a complete and realistic shape without any extraneous noise. In contrast, previous methods trained on ShapeNet produce completed point clouds that are smaller in scale than the original, as shown in the bottom of 7. The proposed Dynamic Scale Adaptation allows the completed results to maintain scale consistency with the original point cloud.
+
+# 4.5. Ablation Study
+
+# 4.5.1 Ablation on Depth Prompting Module
+
+To investigate the impact of the depth extraction method, we compare three variants of Depth Prompting. In variant A, we replace our viewpoint selection with a distance-based method similar to [17], leading to significantly increased CD and EMD values. Meanwhile, as shown in Figure 8, although this method correctly identifies the viewpoint in some cases, it may select the reverse viewpoint, causing depth flipping. This flipped depth map disrupts accurate image generation and severely impacts completion quality. In variant B, ControlNet is removed, and the inpainted depth  $D_{c}$  is used as input to the image-to-3D generative model to examine the effects of color information on subsequent processes. In some cases, experimental observations show
+
+that, even with high-quality depth, the generated 3D shapes are reasonable but lack color, rendering them unsuitable for SDS optimization in the second stage. In variant C, we skip the depth inpainting step to evaluate the effect of low-quality depth on downstream processes. As shown in Figure 8, depth maps projected from sparse point clouds fail to generate accurate images, resulting in a significant drop in performance. Therefore, while this variant performs well on dense point cloud datasets like Redwood, it struggles on sparse point cloud datasets like ScanNet, as shown in Table 3.
+
+# 4.5.2 Ablation on 3D Generative Model
+
+To examine the effect of the image-to-3D generative model in our pipeline, we form variant D by replacing the generated 3D shape with a set of Gaussian noise point clouds. The Refine step is then applied, optimizing over 5000 iterations in an attempt to complete the missing regions. The results in Table 4, the absence of explicit geometric priors significantly impacts the completion performance.
+
+# 4.5.3 Ablation on Geometric Preserving Fusion Module
+
+In variant E, we directly align the generated 3D shape  $P_{\mathrm{gen}}$  with  $P_{\mathrm{partial}}$  without using Dynamic Scale Adaptation to validate the effectiveness of this process. Due to scale inconsistency, the direct alignment fails to properly match the two point clouds, thereby wasting the rich geometric priors provided by the 3D shape. In variant F, we omit the Refining process and use the merged point cloud  $P_{\mathrm{all}}$  directly as the completion result. While quantitative metrics show that the Refining process can further enhance the overall completion quality, our experiments reveal that the merged point cloud  $P_{\mathrm{all}}$  often performs competitively in both visualization and quantitative metrics. Therefore, we make the Refining process optional to improve completion speed.
+
+# 5. Conclusion
+
+In this study, we make the first attempt to leverage a pretrained 3D generative model for zero-shot point cloud completion and introduce GenPC. To capitalize on the generative model's inherent generalization ability, our framework consists of two key components: Depth Prompting and Geometric-Preserving Fusion. The Depth Prompting module prompts an image-to-3D generative model with the partial point cloud. Then, the Geometric Preserving Fusion module aligns the partial input with the generated 3D shape by dynamically adjusting its pose and scale. Experiments on widely used datasets demonstrate that GenPC achieves state-of-the-art performance. With the explicit geometric prior from the 3D generative model, GenPC takes a step closer towards robust real-world scan completion.
+
+# References
+
+[1] Paul J. Besl and Neil D. McKay. A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell., 14(2): 239-256, 1992. 4  
+[2] Honghua Chen, Zeyong Wei, Xianzhi Li, Yabin Xu, Mingqiang Wei, and Jun Wang. Repcd-net: Feature-aware recurrent point cloud denoising network. Int. J. Comput. Vis., 130(3):615-629, 2022. 2  
+[3] Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. In IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023, pages 22189-22199. IEEE, 2023. 3  
+[4] Xuelin Chen, Baoquan Chen, and Niloy J. Mitra. Unpaired point cloud completion on real scans using adversarial training. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020. 2  
+[5] Sungjoon Choi, Qian-Yi Zhou, Stephen Miller, and Vladlen Koltun. A large dataset of object scans. CoRR, abs/1602.02481, 2016. 5, 6  
+[6] Ruikai Cui, Shi Qiu, Saeed Anwar, Jiawei Liu, Chaoyue Xing, Jing Zhang, and Nick Barnes. P2C: self-supervised point cloud completion from single partial clouds. In IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023, pages 14305-14314. IEEE, 2023. 2  
+[7] Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5828-5839, 2017. 5  
+[8] Angela Dai, Charles Ruizhongtai Qi, and Matthias Nießner. Shape completion using 3d-encoder-predictor cnns and shape synthesis. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5868–5877, 2017. 2  
+[9] Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objverse-xl: A universe of  $10\mathrm{m} + 3\mathrm{d}$  objects. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023, 2023. 3  
+[10] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A universe of annotated 3d objects. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, pages 13142-13153. IEEE, 2023. 3  
+[11] Prafulla Dhariwal and Alexander Quinn Nichol. Diffusion models beat gans on image synthesis. In Advances in Neural Information Processing Systems 34: Annual Conference on
+
+Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 8780-8794, 2021. 4  
+[12] Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11):1231-1237, 2013. 5, 7  
+[13] Xiaoguang Han, Zhen Li, Haibin Huang, Evangelos Kalogerakis, and Yizhou Yu. High-resolution shape completion using deep neural networks for global structure and local geometry inference. In IEEE/CVF International Conference on Computer Vision, pages 85-93, 2017. 2  
+[14] Sangmin Hong, Mohsen Yavartanoo, Reyhaneh Neshatavar, and Kyoung Mu Lee. ACL-SPC: adaptive closed-loop system for self-supervised point cloud completion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, pages 9435-9444. IEEE, 2023. 2  
+[15] Yicong Hong, Kai Zhang, Jiumiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. LRM: large reconstruction model for single image to 3d. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. 2, 3  
+[16] Yicong Hong, Kai Zhang, Jiuming Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. LRM: large reconstruction model for single image to 3d. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. 2, 3  
+[17] Tianxin Huang, Zhiwen Yan, Yuyang Zhao, and Gim Hee Lee. Zero-shot point cloud completion via 2d priors. CoRR, abs/2404.06814, 2024. 1, 2, 3, 4, 8  
+[18] Zitian Huang, Yikuan Yu, Jiawen Xu, Feng Ni, and Xinyi Le. Pf-net: Point fractal network for 3d point cloud completion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7662-7670, 2020. 1, 2  
+[19] Yoni Kasten, Ohad Rahamim, and Gal Chechik. Point cloud completion with pretrained text-to-image diffusion models. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023, 2023. 1, 2, 3, 5, 6, 7  
+[20] Sagi Katz, Ayellet Tal, and Ronen Basri. Direct visibility of point sets. ACM Trans. Graph., 26(3):24, 2007. 4  
+[21] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139:1-139:14, 2023. 1, 3  
+[22] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, pages 300-309. IEEE, 2023. 3  
+[23] Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, and Shi-Min Hu. Morphing and sampling network for dense point cloud completion. In AAAI conference on artificial intelligence, pages 11596-11603, 2020. 2
+
+[24] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot one image to 3d object. In IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023, pages 9264–9275. IEEE, 2023. 1, 3, 5  
+[25] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part I, pages 405-421. Springer, 2020. 3  
+[26] Himangi Mittal, Brian Okorn, Arpit Jangid, and David Held. Self-supervised point cloud completion via inpainting. In 32nd British Machine Vision Conference 2021, BMVC 2021, Online, November 22-25, 2021, page 7. BMVA Press, 2021. 2  
+[27] Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. 1, 3  
+[28] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 652-660, 2017. 2  
+[29] Charles R. Qi, Or Litany, Kaiming He, and Leonidas J. Guibas. Deep though voting for 3d object detection in point clouds. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, pages 9276-9285. IEEE, 2019. 2  
+[30] Marie-Julie Rakotaona, Vittorio La Barbera, Paul Guerrero, Niloy J. Mitra, and Maks Ovsjanikov. Pointcleannet: Learning to denoise and remove outliers from dense point clouds. Comput. Graph. Forum, 39(1):185-203, 2020. 2  
+[31] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pages 10674-10685. IEEE, 2022. 1, 3  
+[32] Tianchang Shen, Jun Gao, Kangxue Yin, Ming-Yu Liu, and Sanja Fidler. Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 6087-6101, 2021. 3  
+[33] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024. 1, 3  
+[34] Jacob Varley, Chad DeChant, Adam Richardson, Joaquín Rucales, and Peter Allen. Shape completion enabled robotic grasping. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2442-2447, 2017. 2  
+[35] 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. 2  
+[36] Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee. Cascaded refinement network for point cloud completion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 790-799, 2020. 2  
+[37] Xin Wen, Tianyang Li, Zhizhong Han, and Yu-Shen Liu. Point cloud completion by skip-attention network with hierarchical folding. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1939–1948, 2020. 2  
+[38] Xin Wen, Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, and Yu-Shen Liu. Cycle4completion: Unpaired point cloud completion using cycle transformation with missing region coding. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021, pages 13080-13089. Computer Vision Foundation / IEEE, 2021. 2  
+[39] Xin Wen, Peng Xiang, Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, and Yu-Shen Liu. Pmp-net: Point cloud completion by learning multi-step point moving paths. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7443-7452, 2021. 2  
+[40] Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng, and Zhizhong Han. Snowflake point deconvolution for point cloud completion and generation with skip-transformer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):6320-6338, 2023. 1, 2, 6, 7  
+[41] Chulin Xie, Chuxin Wang, Bo Zhang, Hao Yang, Dong Chen, and Fang Wen. Style-based point generator with adversarial rendering for point cloud completion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4619-4628, 2021. 2  
+[42] Haozhe Xie, Hongxun Yao, Shangchen Zhou, Jiageng Mao, Shengping Zhang, and Wenxiu Sun. Grnet: Gridding residual network for dense point cloud completion. In European Conference on Computer Vision, pages 365-381, 2020. 2  
+[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. CoRR, abs/2404.07191, 2024. 2, 3  
+[44] Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussian dreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024, pages 6796-6807. IEEE, 2024. 3  
+[45] Jinpeng Yu, Binbin Huang, Yuxuan Zhang, Huaxia Li, Xu Tang, and Shenghua Gao. Geoformer: Learning point cloud completion with tri-plane integrated transformer. In Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024 - 1 November 2024, pages 8952-8961. ACM, 2024. 1, 2  
+[46] Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng. Pu-net: Point cloud upsampling network. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-
+
+22, 2018, pages 2790-2799. Computer Vision Foundation / IEEE Computer Society, 2018. 2  
+[47] Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, and Jie Zhou. Pointr: Diverse point cloud completion with geometry-aware transformers. In IEEE/CVF International Conference on Computer Vision, pages 12498-12507, 2021. 1, 2, 6, 7  
+[48] Xumin Yu, Yongming Rao, Ziyi Wang, Jiwen Lu, and Jie Zhou. Adapointr: Diverse point cloud completion with adaptive geometry-aware transformers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12):14114-14130, 2023. 6, 7  
+[49] Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert. Pcn: Point completion network. In International Conference on 3D Vision, pages 728-737, 2018. 1, 2  
+[50] Wu Yushuang, Yan Zizheng, Chen Ce, Wei Lai, Li Xiao, Li Guanbin, Li Yihao, Cui Shuguang, and Han Xiaoguang. Scoda: Domain adaptive shape completion for real scans. In The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023. 7  
+[51] Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, and Chen Change Loy. Unsupervised 3d shape completion through GAN inversion. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021, pages 1768-1777. Computer Vision Foundation / IEEE, 2021. 2  
+[52] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023, pages 3813-3824. IEEE, 2023. 4  
+[53] Xuancheng Zhang, Yutong Feng, Siqi Li, Changqing Zou, Hai Wan, Xibin Zhao, Yandong Guo, and Yue Gao. View-guided point cloud completion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15890-15899, 2021. 2  
+[54] Haoran Zhou, Honghua Chen, Yidan Feng, Qiong Wang, Jing Qin, Haoran Xie, Fu Lee Wang, Mingqiang Wei, and Jun Wang. Geometry and learning co-supported normal estimation for unstructured point cloud. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pages 13235-13244. Computer Vision Foundation / IEEE, 2020. 2  
+[55] Zhe Zhu, Honghua Chen, Xing He, Weiming Wang, Jing Qin, and Mingqiang Wei. Svdformer: Complementing point cloud via self-view augmentation and self-structure dual-generator. In IEEE/CVF International Conference on Computer Vision, pages 14508–14518, 2023. 1, 2  
+[56] Zhe Zhu, Liangliang Nan, Haoran Xie, Honghua Chen, Jun Wang, Mingqiang Wei, and Jing Qin. Csdn: Cross-modal shape-transfer dual-refinement network for point cloud completion. IEEE Transactions on Visualization and Computer Graphics, 30(7):3545-3563, 2024. 2

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+# GenVDM: Generating Vector Displacement Maps From a Single Image
+
+Yuezhi Yang $^{1*}$  Qimin Chen $^{2}$  Vladimir G. Kim $^{3}$  Siddhartha Chaudhuri $^{3}$  Qixing Huang $^{1}$  Zhiqin Chen $^{3}$
+
+<sup>1</sup>The University of Texas at Austin <sup>2</sup>Simon Fraser University <sup>3</sup>Adobe Research
+
+# Abstract
+
+We introduce the first method for generating Vector Displacement Maps (VDMs): parameterized, detailed geometric stamps commonly used in 3D modeling. Given a single input image, our method first generates multi-view normal maps and then reconstructs a VDM from the normals via a novel reconstruction pipeline. We also propose an efficient algorithm for extracting VDMs from 3D objects, and present the first academic VDM dataset. Compared to existing 3D generative models focusing on complete shapes, we focus on generating parts that can be seamlessly attached to shape surfaces. The method gives artists rich control over adding geometric details to a 3D shape. Experiments demonstrate that our approach outperforms existing baselines. Generating VDMs offers additional benefits, such as using 2D image editing to customize and refine 3D details. Code and data are available at https://yyuezhi.github.io/GenVDM/.
+
+# 1. Introduction
+
+Generative neural models for 3D shape synthesis is a rapidly advancing research area [61]. However, they are still not widely adopted in artistic workflows for two main reasons. First, synthesizing fine geometric details is challenging due to the heterogeneity of 3D representations and the lack of detailed 3D training data. Second, existing neural tools lack the precise spatial and compositional controls needed by 3D artists. To address these limitations, instead of reinventing the 3D modeling stack to accommodate generative AI, we draw inspiration from an existing workflow in which an artist starts with a base mesh and "stamps" the desired details onto the 3D surface (see Figure 1). These smaller stamps are easier to generate than full-scale 3D models, fit seamlessly into existing workflows, eliminate artists' dependence on expensive and limited third-party stamp libraries, and provide full artistic control over spatial arrangement and composition.
+
+We chose the vector displacement map or VDM as our
+
+![](images/4371f7a74f8be93a42fda36749e98ec7184d50548c96a61f6da29ed5945ceb5e.jpg)  
+Figure 1. We introduce GenVDM, a method that can generate a highly detailed Vector Displacement Map (VDM) from a single input image. The generated VDMs can be directly applied to mesh surfaces to create intricate geometric details. Note that the thumbnails represent plain 2D RGB image sources.
+
+stamp representation. A VDM assigns an arbitrary 3D displacement to every point in a 2D rectangle, warping the sheet to form a curved surface with complex geometric features, such as overhangs and cavities. It is widely supported in 3D software [1-4] and compactly stored as a vector field over a UV image domain. While using VDMs is commonplace, authoring them is extremely challenging, and artists usually depend on packs of VDMs created by third parties (analogous to brushes in digital painting tools), with limited customization or generality. Image or text-driven stamp generation could drastically expand the scope of VDM usage by providing artists with custom stamps on demand.
+
+In this paper, we propose the first neural pipeline to generate a VDM from a single RGB image. To achieve this, we address two main technical challenges. The first challenge is that existing generative models are not suitable for VDM generation: generating a 3D object usually does not also produce a parametric 2D domain for stamp application, and predicting a depth map from a single image does not capture complex high-amplitude variations, overhangs,
+
+and occlusions; see Figure 6. Thus, we develop a three-step method. First, given an input RGB image (which can also be generated with existing text-to-image models), we predict normal maps from multiple viewing directions to resolve occlusions that may be hidden in a single view. Second, we reconstruct a mesh (which need not have disk topology) by fitting a neural SDF to the multi-view normal maps and polygonizing the result. Third, we use a neural deformation model to displace points on a 2D rectangle to fit the mesh, forming the final VDM.
+
+The second challenge in training a generative VDM model is the absence of training data. We tackle it by building an interactive tool to segment interesting semantic and geometric regions from Objaverse 3D models [19], and then develop a geometry processing pipeline for converting these regions into a VDM representation, creating a dataset of 1,200 VDM patches used for training. Our pipeline is robust enough to analyze polygon soups in the wild, which we achieve by re-sampling the selected regions and reconstructing a single connected surface after removing outliers. We then deform the resulting mesh to obtain a co-planar boundary that can be seamlessly attached to a flat base tile over which the VDMs are typically defined. The processed shapes can then be rendered and used to finetune the multiview normal generation model.
+
+We compare our method to state-of-the-art shape generation techniques [27, 42, 54], as well as to reconstructing a heightfield (i.e. a scalar displacement map) from estimated depth [84]. We use a collection of images depicting parts commonly used in VDMs (e.g., facial elements, decorations), and evaluate using visual fidelity [57] and semantic similarity [55] metrics. Our method outperforms others due to its ability to handle smaller VDM-like regions. Note also that other mesh generation methods do not produce a displacement map – which can have both “outward” and “inward” displacements – and thus their output can only be additively combined with the base shape, e.g., they are not able to introduce cavities like an eye or a mouth in Figure 1.
+
+To summarize, our contributions are:
+
+- The first generative ML pipeline for VDMs;  
+- A robust method to reconstruct VDMs from multi-view normal maps produced by image diffusion models;  
+- A novel VDM extraction pipeline to efficiently extract and process patches from 3D objects to produce VDMs;  
+- The first public dataset of VDMs for academic research.
+
+# 2. Related work
+
+Vector Displacement Maps. Texture mapping [10, 26] is the dominant solution in the industry to add complex surface details to shapes without increasing mesh complexity. Accompanying it are many techniques that hallucinate complex geometric details, such as bump mapping [9], horizon
+
+mapping [46], and parallax mapping [31]. Unlike those techniques that do not change the geometry of the shape, displacement mapping [17, 18, 64] adds geometric details by subdividing the original geometry into finer polygons and then displacing each vertex in its normal direction by a height value indexed from the displacement map (although some versions of displacement mapping can be done in the pixel space without changing the original geometry [69]).
+
+While a displacement map can be considered as a single-channel image or heightfield, a vector displacement map (VDM) can be seen as a three-channel image, where each pixel contains a 3D displacement vector. VDMs naturally support representing more complex geometries with less distortion compared to displacement maps, and both are used in 3D modeling tools to create geometric details. Research on displacement maps and VDMs has focused on texture synthesis from examples [85], and synthesis of human body and face meshes for shape reconstruction [6, 83]. VDMs conceptually resemble Geometry Images [23], and some recent works adopt image diffusion models for generating Geometry Images to synthesize 3D shapes [20, 82]. To our knowledge, there is no prior work on generative models of VDMs, nor a public research dataset for VDMs.
+
+Image-to-3D. Early works on single-view 3D reconstruction [15, 16, 22, 48, 70, 81, 86] mostly adopt feed-forward neural networks trained on limited data [11]. More recent work [30, 49, 88, 90] trained on large 3D datasets [19] has shown significantly improved generalizability to novel shape categories. With the introduction of text-to-image diffusion models [52, 56], a line of work [47, 66] achieved zero-shot single-image-to-3D with score distillation sampling (SDS) [53] by distilling 2D diffusion priors into 3D representations with per-shape optimization.
+
+Another line of work [40, 74] utilizes image diffusion models for novel view synthesis conditioned on an input image and a relative camera pose. Such models produce images of the object from different views, therefore 3D object can be reconstructed by SDS-based optimization [54] or a feed-forward reconstruction network [39]. These methods inspired a series of subsequent work that finetunes pretrained image diffusion models to directly generate 3D-consistent multi-view images of target output shape given a single-view image, where output shape can be reconstructed from generated multi-view images via optimizing a neural field or mesh [41, 42, 60], a 3D diffusion reconstruction network [38], or a feed-forward large reconstruction model powered by Transformers [27, 36, 67, 71, 73, 77, 79, 89, 91]. Most recently, image diffusion models have been replaced by video diffusion models to achieve better 3D consistency of generated views [24, 68].
+
+Modeling by Parts. The use of small building components to compose complex shapes has been widely studied
+
+![](images/73381186bf4901715ffa5746788d1e9fb2ceeba60c9096be69386dc66bf6a281.jpg)  
+Figure 2. Overview of our image-to-VDM pipeline. Given an input image, we first add a gray square behind the object/part in the image as background, so the image resembles a textured VDM applied to a square mesh, as in (a). Then we utilize a multi-view image diffusion model to generate six normal maps with pre-defined camera poses, as in (b). The multi-view normal maps effectively represent the geometry of the VDM when applied to a square mesh, and thus we can reconstruct the VDM from these normal maps, as in (c). The reconstructed VDM can then be applied to various surfaces as in (d).
+
+in modeling-by-assembly systems [21, 34]. Before generative AI rose to prominence, these systems relied on part databases [12] (or shape databases from which parts could be cut out), and focused on building tools to help users find the right parts [7, 13, 59, 78] and assemble them meaningfully [29, 63, 80]. As a variation, methods were developed to extract and transfer detailed patches from a shape to another [65]. A few papers studied joint synthesis and layout of parts [37], but the synthesis was conditioned only on the layout and not on user input, and the focus was on whole-shape generation and not adding detail to existing ones.
+
+Relying on existing part datasets or part generation without user control, and on complex, non-standard, topologysensitive mesh fusion algorithms limits the utility of these older methods. Our approach generates detailed complementary geometry in-situ from the image prompt, and our generated VDMs are defined over parameterized 2D domains which are suitable for seamlessly blending onto 3D models, with industry-wide support.
+
+# 3. Method
+
+Our image-to-VDM pipeline is shown in Figure 2. Similar to other methods in the literature, we follow an approach that first generates multi-view images of the target object with an image diffusion model and then reconstructs the object from the generated images. In particular, we only generate normal maps of the object as we are only interested in the geometric details. Details of the multi-view normal generation are described in Section 3.1. Next, we reconstruct the VDM from the multi-view normals. As VDMs have specific properties and constraints, reconstructing them is highly non-trivial. We report our attempts and solutions in Section 3.2. Finally, as there is no publicly available dataset for VDMs, we designed an efficient tool for extracting shape patches from Objaverse [19], and devised algorithms to process those patches for use as training data. We describe the data processing pipeline in Section 3.3.
+
+# 3.1. Multi-View Normal Map Generation
+
+We opt to finetune an image diffusion model to generate multi-view images, as the pretrained image diffusion model offers strong generalizability. As will be shown in our experiments, our model, trained on a small dataset of 1,200 examples, works on a large variety of shapes.
+
+Specifically, we adopt Zero123++ [60] as the backbone for our multi-view diffusion model. Zero123++ is an image-to-multiview model based on Stable Diffusion [56]. Given an input image, Zero123++ generates a  $960 \times 640$  image representing six multi-view images in a  $3 \times 2$  grid, where the six images have pre-defined camera poses so they can be easily used for 3D reconstruction. However, the pre-defined camera poses in Zero123++ fully surround the object, e.g., there are front views and back views of the object. In our pipeline, since we are aiming to generate VDMs, the back views of the object are unnecessary. Therefore, we re-designed the camera poses of the six images. As shown in Figure 2 (b), assuming that the front view (see (a) for an example) has (elevation angle, azimuth angle) =  $(0^{\circ}, 0^{\circ})$ , we define the six camera poses as  $(0^{\circ}, -60^{\circ})$ ,  $(0^{\circ}, -30^{\circ})$ ,  $(0^{\circ}, 30^{\circ})$ ,  $(0^{\circ}, 60^{\circ})$ ,  $(45^{\circ}, 0^{\circ})$ ,  $(-45^{\circ}, 0^{\circ})$ . We also adopt orthographic cameras to reduce distortion, and let the model generate a normal map of the object for each camera pose. To train the model, we render single-view RGB images as input and multi-view normal maps as ground truth output. Details about training data are described in Section 3.3. Note that the input image does not have to be a front view; we render random views for training so the model can handle images from various viewpoints. We fine-tuned the checkpoint provided by Zero123++ [60] on 8 NVIDIA A100 GPUs for 3 days.
+
+# 3.2. VDM Reconstruction
+
+Reconstructing 3D shapes from multi-view images has been well studied in the text/image-to-3D literature. Most recent
+
+![](images/53266734ec5a430b766ffd5a5c374a98c8d4f70904f64a66275f5f5091510acb.jpg)  
+Figure 3. Reconstructing VDM from multi-view normal maps. We adopt a two-step approach. First, we reconstruct an accurate (but perhaps noisy) mesh (b) from the multi-view normals (a) with differentiable rendering and neural SDF representation. Then we parameterize the mesh by fitting a deformable square to it with a neural deformation field, as in (c). An VDM image can thus be obtained by discretizing the square into pixels and infer each pixel's displacement from the neural deformation field. The whole reconstruction pipeline takes about 6 minutes for each shape on an NVIDIA A100 GPU, where each step takes about 3 minutes.
+
+methods adopt a feed-forward large reconstruction model (LRM) to directly generate a 3D shape from multiple input images of different viewpoints [27, 36, 67, 71, 75, 89]. Therefore, a straightforward way for reconstructing VDMs is to train a similar LRM to take the normal maps as input and directly regress a VDM image. However, given limited VDM training shapes, our LRM trained on a small dataset is unlikely to generalize as well as other LRM models trained on larger datasets, therefore leading to suboptimal results.
+
+Given the above discussions, we adopt a slower but more robust per-shape optimization approach. Given the six normal maps with pre-defined fixed camera poses, we want to optimize a 3D representation to converge to the target 3D shape with supervision provided by differentiable rendering. A naive approach would be to initialize with a discretized square mesh and optimize with mesh-based differentiable rendering. However, as has been shown in other methods [35, 50], differentiable rendering on meshes is often problematic and requires careful design of regularization losses and tuning of hyperparameters. As we will show later, even with ground truth 3D supervision, optimizing a discretized mesh to fit the target shape is not an easy task.
+
+Therefore, we devise a two-step approach, as shown in Figure 3, to first optimize a neural SDF field to reconstruct a 3D shape from the multi-view normal maps, and then parameterize the 3D shape into a VDM image. We utilize the method proposed in Wonder3D [42] for the first step, with the only modification being that we removed  $L_{rgb}$ , the loss term to punish the difference between rendered RGB images and the ground truth, as we do not predict multiview RGB images. Since we always put a grey square as background in our input images, the shape we obtained via optimization has a solid plane-like primitive where the object/part is attached to, see Figure 3 (b); then we can extract a mesh from the neural SDF field and easily separate a single layer of mesh that represents the VDM.
+
+![](images/324dc1c7f680ca78e19d3e8d11e5e6caba939d3e5dbf683ae7c7108a9860a70a.jpg)  
+Figure 4. Comparison of different approaches for parameterizing a shape into VDM. (a) Topology fixing and Tutte embedding with classic tools leads to noise and distortion. (b) Fitting a plane mesh to the target mesh leads to large distortion. (c) Our approach by applying a neural deformation field to a parametric square leads to clean and high-quality reconstruction.
+
+The next step is to parameterize the mesh into a VDM image. Since the mesh is reconstructed from sparse-view images, its geometry is often noisy and riddled with small holes and large gaps, see Figure 4 (a) left. To convert it into a VDM, we will need to fix its topology so that it is topologically equivalent to a plane; and then we will apply a mesh parametrization method to obtain its Tutte embedding on a square, so that each pixel on the square can be assigned with a displacement vector. However, as shown in Figure 4 (a), although the state-of-the-art topology fixing algorithms [87] can fix the topology, the result is often not satisfactory, e.g., a gap that should have been filled is being cut, see Figure 4 (a) middle where the helix of the ear is cut in half. As a result, after applying [58] to obtain its
+
+![](images/e23c681f70db97182d74cf3355ff5b0ad26dbe72d5ce24a99c719381bd8c678a.jpg)  
+Figure 5. Data preparation. For each interesting object (a), we use a 3D lasso tool to segment out interesting parts. For each part, we densely sample points on the part's surface and then perform Screened Poisson Surface Reconstruction [32] to obtain a single connected mesh (b). We then stitch the mesh to a square mesh with an algorithm inspired by Poisson Image Editing [51] (c). Afterwards, we can color the part and render RGB images (d) and normal maps (e) for training the image diffusion model.
+
+embedding on a plane, we see large distortions and noise in the final VDM, see Figure 4 (a) right where the upper part of the ear is missing due to distortion.
+
+An alternative is to initialize with an estimable square mesh and optimize it using a reconstruction loss with respect to the target mesh, as shown in Figure 4 (b). However, as mentioned, it is often required to have carefully designed regularization losses when a mesh is to be optimized. When adopting a naive optimization method proposed in [14], the resulting mesh exhibits large distortion.
+
+Therefore, instead of tuning the mesh optimization algorithm, inspired by AtlasNet [22] and Deep Geometric Prior [76], we propose to deform the square mesh with a neural deformation field parameterized by a Multilayer Perceptron (MLP). The MLP acts as a natural regularizer, as its inductive smoothness bias encourages smoothness of the deformation. We define the square as  $\{p\mid p\in [0,1]^2\}$ , and the MLP  $\phi_{\theta}$  with estimizable parameters  $\theta$ . Then, given any 2D point  $p$  in the square, we obtain its corresponding 3D point  $p^{\prime} = \phi_{\theta}(p)$  in the deformed shape. Therefore, for each optimization step, we sample a grid of 2D points in  $[0,1]^2$ , apply  $\phi_{\theta}$  to obtain the deformed 3D points, and then compute the symmetric Chamfer Distance between the deformed 3D points and the ground truth points sampled from the target mesh. We also include a loss to maintain the square boundary. Therefore our optimization objective is
+
+$$
+\begin{array}{l} \underset {\theta} {\operatorname {a r g m i n}} \mathbb {E} _ {P, Q} \frac {1}{| P |} \sum_ {p \in P} \underset {q \in Q} {\min } \| \phi_ {\theta} (p) - q \| _ {2} ^ {2} + \frac {1}{| Q |} \sum_ {q \in Q} \underset {p \in P} {\min } \| \phi_ {\theta} (p) - q \| _ {2} ^ {2} + \tag {1} \\ \frac {1}{| \partial P |} \sum_ {p \in \partial P} \| \phi_ {\theta} (p) - \operatorname {p r o j} (p) \| _ {2} ^ {2}, \\ \end{array}
+$$
+
+where  $P$  and  $Q$  are sets of sampled points from  $[0,1]^2$  and the target mesh, respectively.  $\partial P$  contains all the boundary points in  $P$  and  $\mathrm{proj}(p)$  maps  $p$  to a corresponding 3D point in a pre-defined square boundary. After optimization, we can sample a regular grid of points in  $[0,1]^2$  and compute their 3D displacement vectors from  $\phi_{\theta}$  to obtain the VDM image, as shown in Figure 4 (c).
+
+# 3.3. Data Preparation
+
+To the best of our knowledge, there is no publicly available dataset for VDMs. Therefore, we developed a data processing pipeline so we can efficiently annotate interesting parts from objects and then convert the parts into VDMs. In fact, our data processing pipeline does not produce true VDMs, but rather, shapes that look like VDMs, which are good enough to train our multiview generation model, see Figure 5. If needed, our VDM reconstruction method in Section 3.2 can be used to obtain readily usable VDMs.
+
+To construct our VDM training dataset, we crop parts from the Objaverse [19] dataset. We first create a keyword filtering list and apply the filter on Objaverse shape captions [43, 44]. As VDMs are mostly used to model organic parts, we select objects likely to contain such parts, e.g., animals and characters.
+
+We then developed a UI to precisely crop a part from a 3D object. This is achieved by a 3D lasso tool, where the user only needs to select a ring of points along the cutting boundary of the desired part. Our algorithm connects the points to form a cut and extracts the part from the object. Note that the part may not be a single connected mesh – it may comprise several sub-meshes. Hence, we remesh the part into a single connected mesh. We first densely sample points on the part, and then remove interior points by computing winding numbers [8]. For the remaining points, we performed screen Poisson surface reconstruction [32] to obtain a single connected mesh (Figure 5 (b)). Our 3D lasso tool has proven to be quite efficient. Annotating our entire dataset with 1,200 parts took only 24 man-hours.
+
+After obtaining the parts, we will then stitch each part to a square mesh to mimic the appearance of a VDM applied to a plane. Note that in almost all cases, the vertices on the boundary of each part are not coplanar, therefore, additional steps are required to make them coplanar. We first determine the plane via least squares plane fitting with respect to
+
+the boundary vertices. Then we project the boundary vertices to the plane, and adopt a method similar to Poisson Image Editing [51] to deform the part so that it follows the new coplanar boundary. Denote the set of all boundary vertices in the part (before projection) as  $B$  and non-boundary vertices as  $A$ ; also denote the set of all edges as  $E$ . Denote the coplanar boundary vertices after projection as  $B'$ , and the non-boundary vertices after deformation as  $A'$ . For each point  $p$  in  $A$  or  $B$ , denote its corresponding point in  $A'$  or  $B'$  as  $p'$ . Then our new vertices after mesh deformation can be obtained by solving a quadratic error function
+
+$$
+\operatorname {a r g m i n} _ {A ^ {\prime}} \mathbb {E} _ {(p, q) \in E} \| \left(p ^ {\prime} - q ^ {\prime}\right) - (p - q) \| _ {2} ^ {2}. \tag {2}
+$$
+
+The minimization objective is to ensure that the gradients on the mesh are preserved after deformation, while the target coplanar boundary points  $B'$  are also strictly followed.
+
+We then place the deformed part on a square mesh so that the boundary vertices and the square mesh vertices are coplanar. Once the part is attached to the square mesh, we perform one additional Laplacian smoothing step on the vertices close to the boundary to remove boundary noise, see Figure 5 (c). We always keep the square mesh gray and assign a random color to the part. We also perform translation, scaling, and rotation augmentation to the part to enrich the diversity of the dataset. Finally, for each shape, we render several RGB images from different viewpoints to serve as the training input to the multi-view normal generation model, and six normal maps in pre-defined camera poses as the ground truth output, see Figure 5 (d, e).
+
+# 4. Experiments
+
+In this section, we verify the effectiveness of our method by comparing it with various state-of-the-art methods. We also validate our design choices in ablation studies. Finally, we present additional results produced by our method, show applications of VDMs on adding details to geometry, and demonstrate how users can customize VDMs by simply editing the input images. We will make our code, trained model weights, and dataset available to the public.
+
+# 4.1. Vector Displacement Map Generation
+
+Baselines. Since there is no prior work on generating VDMs from single view images, we compare our method with methods that perform a similar task, namely, single-view image to 3D reconstruction. Specifically, we compare our method with Wonder3D [42], Magic123 [54], Large Reconstruction Model (LRM) [27], as well as a scalar displacement map (scalar DM) reconstruction method based on DepthAnything [84]. Given an input image, Wonder3D [42] generates multi-view RGB and normal images and optimizes a neural SDF field to reconstruct the 3D shape from the multi-view images. Magic123 [54] uses
+
+<table><tr><td>Method</td><td>CLIPImg↑</td><td>CLIPText↑</td><td>3D-FID↓</td></tr><tr><td>Wonder3D [42]</td><td>0.8246</td><td>0.2542</td><td>199.5</td></tr><tr><td>Magic123 [54]</td><td>0.8293</td><td>0.2510</td><td>213.2</td></tr><tr><td>LRM [27]</td><td>0.8144</td><td>0.2510</td><td>239.9</td></tr><tr><td>Scalar DM</td><td>0.8223</td><td>0.2564</td><td>213.0</td></tr><tr><td>Ours</td><td>0.8520</td><td>0.2701</td><td>192.7</td></tr></table>
+
+Table 1. Quantitative comparison with baselines. Scalar DM is scalar displacement map produced from DepthAnything [84].  
+
+<table><tr><td>Method</td><td>CLIPImg↑</td><td>CLIPText↑</td><td>3D-FID↓</td></tr><tr><td>Recon. Mesh</td><td>0.8440</td><td>0.2636</td><td>198.0</td></tr><tr><td>Topo. Fix(a)</td><td>0.8401</td><td>0.2617</td><td>209.9</td></tr><tr><td>Mesh Opt.(b)</td><td>0.8245</td><td>0.2525</td><td>217.2</td></tr><tr><td>Ours(c)</td><td>0.8521</td><td>0.2701</td><td>192.7</td></tr></table>
+
+Table 2. Quantitative ablation on VDM Reconstruction.
+
+the SDS loss [53] to optimize the 3D shape while applying a reconstruction loss on the input view. LRM [27] generates multi-view RGB images and trains a Transformer-based feed-forward model to reconstruct the 3D shape from the multi-view images. To validate the necessity of generating vector displacement map instead of regular scalar displacement map, we also compare with a state-of-the-art depth prediction method, DepthAnything [84], by converting the predicted depth of the object into a scalar DM. We run these baseline models with official implementation and pretrained weights; except that LRM does not release the official code, so we use open-source implementation OpenLRM [25] instead. For all reconstructed shapes, we render textureless images for visualization and evaluation. For Wonder3D, Magic123, and LRM, as they generate complete objects and not VDMs, we put a square plane behind their generated shapes to make the visualization more consistent and to have a fair quantitative comparison.
+
+Evaluation Dataset and Metrics. As there is no existing benchmark dataset for VDMs, we collected a dataset of 50 RGB images from the Internet and a text-to-image model [5] for evaluation. All images depict common VDM categories used by artists such as facial elements and decorations. For quantitative evaluation, we measure CLIP similarity [55] and 3D-FID score [72] between the input image and the rendered images of the generated shapes from different views, denoted as CLIPImg and 3D-FID, respectively. For CLIP, we additionally assess semantic alignment by measuring CLIP similarity between rendered images and texts describing the categories of the input images, denoted as CLIPText. We use public implementation of CLIP [62] and 3D-FID [57] for computing the metrics.
+
+The quantitative results are summarized in Table 1 and qualitative results are presented in Figure 6. Quantitatively, our method outperforms others by a significant margin. The closest competitors to our method are Wonder3D and scalar DM, which is also reflected in the qualitative results in Fig-
+
+![](images/5138cc15103b67126f122acae3eac9a6a2b0aed98f4557bdcc5d018d1e723781.jpg)  
+Figure 6. Qualitative results compared with baseline methods. As Magic123 [54], LRM [27], and Wonder3D [42] generate complete objects and not VDMs, we put a square plane behind their generated shapes to make the visualization more consistent.
+
+![](images/14e669df05dbd11aab9e849d2782754d44ee6a86d9fc3684cac0e951ee31c60c.jpg)  
+Figure 7. Qualitative results of ablation study.
+
+![](images/2b02f7029cff4d050475fa0124972ba7676bb4e2b92b8ea93f09b195854c1ff1.jpg)
+
+![](images/65d26152ff24b20dae1613f1fb33afe8dc585d9fdd1e5c28ad1253db12ed066a.jpg)
+
+![](images/92e33ce3121f4a5f92c3251a6b443f6a605df46bae47ad948b9d425a0f9f1c4f.jpg)
+
+![](images/fd60045fb3b2cc2d01cd65eb7d89cfa030d3dee51b838727506e17a3a0ba3ecd.jpg)  
+(a) Original
+
+![](images/80db3d25e6f604a9b12bf80a6de5c75d97dc907503ee98399e516ca1633ef3a9.jpg)  
+(b) Edition 1
+
+![](images/d8309018fe4755ba2baaf1a2394f1a17f2a47d5cc129d0b68d4403cc3255391e.jpg)  
+(c) Edition 2  
+Figure 8. Customizing VDMs by editing images. Here we show original input images and generated VDMs in (a) and edited images and their generated VDMs in (b)(c).
+
+ure 6. Magic123 and LRM lack geometric detail as they rely heavily on textures which often hallucinate details in geometry. Wonder3D has a shape generation pipeline similar to ours, yet it was designed to generate complete objects. Therefore, it struggles to generate partial shapes, e.g., noses and ears. Although the results of scalar DM look reasonable from the front view, its side view suffers as scalar DM cannot represent unseen regions of the front view.
+
+# 4.2. Ablation Study
+
+As discussed in Section 3.2, we compare the following settings for parameterizing the reconstructed mesh into a VDM image: (a) Topology fixing and Tutte embedding, (b) fitting a square mesh into reconstructed mesh, and (c) our approach; see Figure 4. We also include the reconstructed mesh before parameterization as a reference baseline. Table 2 summarizes the quantitative results and Figure 7 shows the qualitative comparisons. Topological fixing and Tutte embedding suffer when the topology of the reconstructed mesh is complex due to noisy reconstruction results, as shown in Figure 7 (a). This is because the topological fixing algorithm does not consider the distortion after parameterization as one of its optimization goals, thus some topological fixes may significantly increase distortion. Figure 7 (b) shows that mesh optimization is not reliable in our setting and is likely to fall into local minima during optimization. In contrast, our method, shown in Figure 7 (c), not only reconstructs high quality VDMs with correct topology, but also smooths out noise induced in neural SDF reconstruction, leading to visually more pleasing results.
+
+# 4.3. Application
+
+Shape modeling. With our method, users can generate parts of the shape from single-view images or text prompts (via text-to-image to obtain input to our method). Compared with methods that generate complete shapes, our method naturally provides more controllability, as users can
+
+![](images/6b63c3bc07b2c6ba6e1529e930bee6224e0aa183b15dd8fac7dfc9d4bde04005.jpg)  
+(a) Input image
+
+![](images/761b9fdf732dceaca134460cb34ad5955c8222489db8f32bfc1821d8c5a96bd0.jpg)  
+(b) Normal maps  
+Figure 9. Failure case.
+
+![](images/5ab6abf955625a12b4174d88e9bc914ae75024bbb3a347d9cedf83d28481f9dd.jpg)  
+(c) Reconstructed mesh
+
+![](images/9f677fc7799a22578a756d6a363bf3ac9347fcf45e84892c0802ec9f9fdfc9dc.jpg)  
+(d) Final VDM
+
+start with a coarse shape and add customization details and shape parts, see Figure 1. We also show a video in the Supplementary Material to demonstrate the modeling process with VDMs generated by our method.
+
+Part editing. With our image-to-VDM, one can perform editing in 2D image space and change the appearance of the part in 3D, see Figure 8. Editing in image space is typically much more convenient than sculpting 3D shapes, therefore allowing users to customize their parts with ease.
+
+# 5. Conclusion, Limitation, and Future Work
+
+In this work, we propose a method to generate a VDM from an input single-view image. Our method first finetunes a pretrained image diffusion model to generate multi-view normal maps from the input image, and then reconstructs a VDM image from the multi-view normals. The generated VDMs can be used directly in shape modeling, which provide more freedom to the users on the appearance and position of each part on the shape. We also propose an efficient pipeline for creating a VDM dataset from 3D objects. Our method outperforms state-of-the-art image-to-3D models and scalar displacement map baseline, proving that our approach is more suited for VDM generation.
+
+As discussed in Section 3.2, our VDM reconstruction involves per-shape optimization, making its inference time significantly slower than the current image-to-3D methods with feed-forward LRM. Investigating the possibility of a VDM-LRM with limited training data is of great interest to us. For certain shapes with thin structures, our method cannot produce plausible results, while the generated normals look reasonable, see Figure 9. We suspect it is due to the multi-view images being inconsistent across different views, as observed by many other works [24, 68].
+
+VDMs are predominantly used for modeling organic shapes, yet the idea of modeling-by-parts can be applied to the majority of 3D shapes. There are exciting further avenues for part-based 3D generative models.
+
+Acknowledgment. Yuezhi Yang and Qixing Huang were supported by NSF IIS-2047677, NSF IIS-2413161, and Gifts from Adobe and Google.
+
+# References
+
+[1] Blender. vector displacement node. https://docs.blender.org/manual/en/latest/render/shader_nodes/vector/vector_displacement.html.1  
+[2] Maya. vector displacement - arnold for maya. https:// help.autodesk.com/view/ARNOL/ENU/?guid= arnold_for_maya_displacement_am_Vector_ Displacement_html.  
+[3] Mudbox. vector displacement maps overview. https://download.autodesk.com/us/mudbox/help2011_5/index.html?url  $=$  ./files/ WS73099cc142f487552b5ac6c412649166e6e 6762.htm,topicNumber  $\equiv$  d0e21754.  
+[4] Zbrush. vector displacement maps. https://help.maxon.net/zbr/en-us/Content/html/user-guide / 3d - modeling / exporting - your - model/vector-displacement-maps/vector-displacement-maps.html.1  
+[5] Adobe firefly. https://www.adobe.com/sensei/generative-ai/firefly.html.6,1  
+[6] Thiemo Alldieck, Gerard Pons-Moll, Christian Theobalt, and Marcus Magnor. Tex2shape: Detailed full human body geometry from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2293-2303, 2019. 2  
+[7] Melinos Averkiou, Vladimir Kim, Youyi Zheng, and Niloy J. Mitra. Shapesynth: Parameterizing model collections for coupled shape exploration and synthesis. Computer Graphics Forum (Special issue of Eurographics 2014), 2014. 3  
+[8] Gavin Barill, Nia Dickson, Ryan Schmidt, David I.W. Levin, and Alec Jacobson. Fast winding numbers for soups and clouds. ACM Transactions on Graphics, 2018. 5, 1  
+[9] James F Blinn. Simulation of wrinkled surfaces. In Seminal graphics: pioneering efforts that shaped the field, pages 111-117. 1998. 2  
+[10] Edwin Earl Catmull. A subdivision algorithm for computer display of curved surfaces. The University of Utah, 1974. 2  
+[11] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 2  
+[12] Siddhartha Chaudhuri, Evangelos Kalogerakis, Leonidas Guibas, and Vladlen Koltun. Probabilistic reasoning for assembly-based 3d modeling. ACM Trans. Graph., 30(4), 2011. 3  
+[13] Siddhartha Chaudhuri, Evangelos Kalogerakis, Stephen Giguere, and Thomas Funkhouser. AttribIt: Content creation with semantic attributes. ACM Symposium on User Interface Software and Technology (UIST), 2013. 3  
+[14] Yun-Chun Chen, Selena Ling, Zhiqin Chen, Vladimir G. Kim, Matheus Gadelha, and Alec Jacobson. Text-guided controllable mesh refinement for interactive 3d modeling. In Proceedings of the ACM SIGGRAPH. ACM, 2024. 5  
+[15] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In Proceedings of the IEEE/CVF
+
+conference on computer vision and pattern recognition, pages 5939-5948, 2019. 2  
+[16] Christopher B Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese. 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII 14, pages 628-644. Springer, 2016. 2  
+[17] Robert L Cook. Shade trees. In Proceedings of the 11th annual conference on Computer graphics and interactive techniques, pages 223-231, 1984. 2  
+[18] Robert L Cook, Loren Carpenter, and Edwin Catmull. The reyes image rendering architecture. ACM SIGGRAPH Computer Graphics, 21(4):95-102, 1987. 2  
+[19] Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objverse: A universe of annotated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13142-13153, 2023. 2, 3, 5  
+[20] Slava Elizarov, Ciara Rowles, and Simon Donne. Geometry image diffusion: Fast and data-efficient text-to-3d with image-based surface representation. arXiv preprint arXiv:2409.03718, 2024. 2  
+[21] Thomas Funkhouser, Michael Kazhdan, Philip Shilane, Patrick Min, William Kiefer, Ayellet Tal, Szymon Rusinkiewicz, and David Dobkin. Modeling by example. In ACM SIGGRAPH 2004 Papers, 2004. 3  
+[22] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. A papier-mâché approach to learning 3d surface generation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 216-224, 2018. 2, 5  
+[23] Xianfeng Gu, Steven J Gortler, and Hugues Hoppe. Geometry images. In Proceedings of the 29th annual conference on Computer graphics and interactive techniques, pages 355-361, 2002. 2  
+[24] Junlin Han, Filippos Kokkinos, and Philip Torr. Vfusion3d: Learning scalable 3d generative models from video diffusion models. In European Conference on Computer Vision, pages 333-350. Springer, 2025. 2, 8  
+[25] Zexin He and Tengfei Wang. Openlrm: Open-source large reconstruction models. https://github.com/3DTopia/OpenLRM, 2023.6  
+[26] Paul S Heckbert. Survey of texture mapping. IEEE computer graphics and applications, 6(11):56-67, 1986. 2  
+[27] Yicong Hong, Kai Zhang, Juixiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. In The Twelfth International Conference on Learning Representations. 2, 4, 6, 7, 1  
+[28] Alec Jacobson, Daniele Panozzo, et al. libigl: A simple  $\mathrm{C}++$  geometry processing library, 2018. https://libigl.github.io/. 1  
+[29] Arjun Jain, Thorsten Thormählen, Tobias Ritschel, and Hans-Peter Seidel. Exploring shape variations by 3d-model decomposition and part-based recombination. Comput. Graph. Forum, 31(2pt3):631-640, 2012. 3
+
+[30] Heewoo Jun and Alex Nichol. Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463, 2023. 2  
+[31] Tomomichi Kaneko, Toshiyuki Takahei, Masahiko Inami, Naoki Kawakami, Yasuyuki Yanagida, Taro Maeda, and Susumu Tachi. Detailed shape representation with parallax mapping. In Proceedings of ICAT, pages 205-208, 2001. 2  
+[32] Michael Kazhdan and Hugues Hoppe. Screened poisson surface reconstruction. ACM Transactions on Graphics (ToG), 32(3):1-13, 2013. 5  
+[33] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 1  
+[34] Vladislav Kreavoy, Dan Julius, and Alla Sheffer. Model composition from interchangeable components. In Proceedings of the 15th Pacific Conference on Computer Graphics and Applications, page 129-138, USA, 2007. IEEE Computer Society. 3  
+[35] Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, and Timo Aila. Modular primitives for high-performance differentiable rendering. ACM Transactions on Graphics (ToG), 39(6):1-14, 2020. 4  
+[36] Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg Shakhnarovich, and Sai Bi. Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. In The Twelfth International Conference on Learning Representations. 2, 4  
+[37] Jun Li, Kai Xu, Siddhartha Chaudhuri, Ersin Yumer, Hao Zhang, and Leonidas Guibas. GRASS: Generative recursive autoencoders for shape structures. ACM Trans. Graphics (Proc. SIGGRAPH), 36(4), 2017. 3  
+[38] Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10072–10083, 2024. 2  
+[39] Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund Varma T, Zexiang Xu, and Hao Su. One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization. Advances in Neural Information Processing Systems, 36, 2024. 2  
+[40] Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9298–9309, 2023. 2  
+[41] Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Generating multiview-consistent images from a single-view image. In The Twelfth International Conference on Learning Representations. 2  
+[42] Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Sin
+
+gle image to 3d using cross-domain diffusion. CVPR, 2023. 2,4,6,7,1  
+[43] Tiange Luo, Chris Rockwell, Honglak Lee, and Justin Johnson. Scalable 3d captioning with pretrained models. arXiv preprint arXiv:2306.07279, 2023. 5  
+[44] Tiange Luo, Justin Johnson, and Honglak Lee. View selection for 3d captioning via diffusion ranking. arXiv preprint arXiv:2404.07984, 2024. 5  
+[45] Andrew L Maas, Awni Y Hannun, and Andrew Y Ng. Rectifier nonlinearities improve neural network acoustic models. In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013. 1  
+[46] Nelson L Max. Horizon mapping: shadows for bump-mapped surfaces. The Visual Computer, 4:109-117, 1988. 2  
+[47] Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, and Andrea Vedaldi. Realfusion: 360deg reconstruction of any object from a single image. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8446-8455, 2023. 2  
+[48] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460-4470, 2019. 2  
+[49] Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751, 2022. 2  
+[50] Baptiste Nicolet, Alec Jacobson, and Wenzel Jakob. Large steps in inverse rendering of geometry. ACM Transactions on Graphics (TOG), 40(6):1-13, 2021. 4  
+[51] Patrick Pérez, Michel Gangnet, and Andrew Blake. Poisson image editing. In Seminal Graphics Papers: Pushing the Boundaries, Volume 2, pages 577-582. 2023. 5, 6  
+[52] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 2  
+[53] Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 2, 6  
+[54] Guocheng Qian, Jinjie Mai, Abdullah Hamdi, Jian Ren, Aliaksandr Siarohin, Bing Li, Hsin-Ying Lee, Ivan Skorokhodov, Peter Wonka, Sergey Tulyakov, et al. Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors. arXiv preprint arXiv:2306.17843, 2023. 2, 6, 7, 1  
+[55] 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 (ICML), 2021. 2, 6  
+[56] 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. 2, 3  
+[57] Maximilian Seitzer. pytorch-fid: FID Score for PyTorch. https://github.com/mseitzer/pytorch-fid, 2020. Version 0.3.0. 2, 6, 1  
+[58] Alla Sheffer, Bruno Levy, Maxim Mogilnitsky, and Alexander Bogomyakov. Abf++: Fast and robust angle based flattening. In ACM Transactions on Graphics (TOG), pages 311-330. ACM, 2005. 4  
+[59] Chao-Hui Shen, Hongbo Fu, Kang Chen, and Shi-Min Hu. Structure recovery by part assembly. ACM Trans. Graph., 31 (6), 2012. 3  
+[60] Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, and Hao Su. Zero123++: a single image to consistent multi-view diffusion base model. arXiv preprint arXiv:2310.15110, 2023. 2, 3, 1  
+[61] Zifan Shi, Sida Peng, Yinghao Xu, Yiyi Liao, and Yujun Shen. Deep generative models on 3d representations: A survey. CoRR, abs/2210.15663, 2022. 1  
+[62] Zhengwentai Sun. clip-score: Clip score for pytorch. https://github.com/taited/clip-score, 2023.6  
+[63] Minhyuk Sung, Hao Su, Vladimir G Kim, Siddhartha Chaudhuri, and Leonidas Guibas. ComplementMe: Weakly-supervised component suggestions for 3D modeling. ACM Transactions on Graphics (TOG), 36(6):226, 2017. 3  
+[64] László Szirmay-Kalos and Tamás Umenhoffer. Displacement mapping on thegpu—state of the art. In Computer graphics forum, pages 1567–1592. Wiley Online Library, 2008. 2  
+[65] Kenshi Takayama, Ryan Schmidt, Karan Singh, Takeo Igarashi, Tamy Boubekeur, and Olga Sorkine. GeoBrush: Interactive mesh geometry cloning. Comput. Graph. For. (Proc. EUROGRAPHICS), 30(2):613-622, 2011. 3  
+[66] Junshu Tang, Tengfei Wang, Bo Zhang, Ting Zhang, Ran Yi, Lizhuang Ma, and Dong Chen. Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior. In Proceedings of the IEEE/CVF international conference on computer vision, pages 22819–22829, 2023. 2  
+[67] Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation. In European Conference on Computer Vision, pages 1-18. Springer, 2025. 2, 4  
+[68] Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion. In European Conference on Computer Vision, pages 439-457. Springer, 2025. 2, 8  
+[69] Lifeng Wang, Xi Wang, Xin Tong, Stephen Lin, Shimin Hu, Baining Guo, and Heung-Yeung Shum. View-dependent displacement mapping. ACM Transactions on graphics (TOG), 22(3):334-339, 2003. 2  
+[70] Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang. Pixel2mesh: Generating 3d mesh
+
+models from single rgb images. In Proceedings of the European conference on computer vision (ECCV), pages 52-67, 2018. 2  
+[71] Peng Wang, Hao Tan, Sai Bi, Yinghao Xu, Fujun Luan, Kalyan Sunkavalli, Wenping Wang, Zexiang Xu, and Kai Zhang. Pf-lrm: Pose-free large reconstruction model for joint pose and shape prediction. In The Twelfth International Conference on Learning Representations. 2, 4  
+[72] Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. arXiv preprint arXiv:2305.16213, 2023. 6  
+[73] Zhengyi Wang, Yikai Wang, Yifei Chen, Chengdong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, and Jun Zhu. Crm: Single image to 3d textured mesh with convolutional reconstruction model. CoRR, 2024. 2  
+[74] Daniel Watson, William Chan, Ricardo Martin Brulla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel view synthesis with diffusion models. In The Eleventh International Conference on Learning Representations. 2  
+[75] Xinyue Wei, Kai Zhang, Sai Bi, Hao Tan, Fujun Luan, Valentin Deschaintre, Kalyan Sunkavalli, Hao Su, and Zexiang Xu. Meshlrm: Large reconstruction model for high-quality mesh. arXiv preprint arXiv:2404.12385, 2024. 4  
+[76] Francis Williams, Teseo Schneider, Claudio Silva, Denis Zorin, Joan Bruna, and Daniele Panozzo. Deep geometric prior for surface reconstruction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10130-10139, 2019. 5  
+[77] Desai Xie, Sai Bi, Zhixin Shu, Kai Zhang, Zexiang Xu, Yi Zhou, Soren Pirk, Arie Kaufman, Xin Sun, and Hao Tan. Lrm-zero: Training large reconstruction models with synthesized data. In NeurIPS, 2024. 2  
+[78] Xiaohua Xie, Kai Xu, Niloy J. Mitra, Daniel Cohen-Or, Wenyong Gong, Qi Su, and Baoquan Chen. Sketch-to-design: Context-based part assembly. Computer Graphics Forum, xx(xx):xx, 2013. 3  
+[79] 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  
+[80] Kai Xu, Hao Zhang, Daniel Cohen-Or, and Baoquan Chen. Fit and diverse: Set evolution for inspiring 3d shape galleries. ACM Transactions on Graphics, (Proc. of SIGGRAPH 2012), 31(4):57:1-57:10, 2012. 3  
+[81] Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann. Disn: Deep implicit surface network for high-quality single-view 3d reconstruction. Advances in neural information processing systems, 32, 2019. 2  
+[82] Xingguang Yan, Han-Hung Lee, Ziyu Wan, and Angel X Chang. An object is worth 64x64 pixels: Generating 3d object via image diffusion. arXiv preprint arXiv:2408.03178, 2024. 2  
+[83] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. Facescape: a large-scale
+
+high quality 3d face dataset and detailed riggable 3d face prediction. In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pages 601-610, 2020. 2  
+[84] Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In CVPR, 2024. 2, 6  
+[85] Lexing Ying, Aaron Hertzmann, Henning Biermann, and Denis Zorin. Texture and shape synthesis on surfaces. In Rendering Techniques 2001: Proceedings of the Eurographics Workshop in London, United Kingdom, June 25-27, 2001 12, pages 301-312. Springer, 2001. 2  
+[86] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelnerf: Neural radiance fields from one or few images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4578-4587, 2021. 2  
+[87] Dan Zeng, Erin Cahmbers, David Letscher, and Tao Ju. To cut or to fill: a global optimization approach to topological simplification. ACM Transactions on Graphics, (Proc. of SIGGRAPH 2020), 39(6):201:1-201:18, 2020. 4  
+[88] 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  
+[89] Kai Zhang, Sai Bi, Hao Tan, Yuanbo Xiangli, Nanxuan Zhao, Kalyan Sunkavalli, and Zexiang Xu. Gs-lrm: Large reconstruction model for 3d gaussian splatting. In European Conference on Computer Vision, pages 1-19. Springer, 2025. 2, 4  
+[90] Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creating high-quality 3d assets. ACM Transactions on Graphics (TOG), 43(4):1-20, 2024. 2  
+[91] Xin-Yang Zheng, Hao Pan, Yu-Xiao Guo, Xin Tong, and Yang Liu. Mvd'2: Efficient multiview 3d reconstruction for multiview diffusion. In ACM SIGGRAPH 2024 Conference Papers, pages 1-11, 2024. 2  
+[92] Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. Open3D: A modern library for 3D data processing. arXiv:1801.09847, 2018. 1

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