ICCV 2025 Papers

As multi-modal large language models (MLLMs) frequently exhibit errors when solving scientific problems, evaluating the validity of their reasoning processes is critical for ensuring reliability and uncovering fine-grained model weaknesses. Since human evaluation is laborious and costly, prompting MLLMs as automated process judges has become a common practice. However, the reliability of these model-based judges remains uncertain. To address this, we introduce ProJudgeBench, the first comprehensive benchmark specifically designed for evaluating abilities of MLLM-based process judges. ProJudgeBench comprises 2,400 test cases and 50,118 step-level labels, spanning four scientific disciplines with diverse difficulty levels and multi-modal content. In ProJudgeBench, each step is meticulously annotated by human experts for correctness, error type, and explanation, enabling a systematic evaluation of judges' capabilities to detect, classify and diagnose errors. Evaluation on ProJudgeBench reveals a significant performance gap between open-source and proprietary models. To bridge this gap, we further propose ProJudge-173k, a large-scale instruction-tuning dataset, and a Dynamic Dual-Phase fine-tuning strategy that encourages models to explicitly reason through problem-solving before assessing solutions. Both contributions significantly enhance the process evaluation capabilities of open-source models. This project is available at: https://projudge.github.io.

Prosthetic legs play a pivotal role in clinical rehabilitation, allowing individuals with lower-limb amputations the ability to regain mobility and improve their quality of life. Gait analysis is fundamental for optimizing prosthesis design and alignment, directly impacting the mobility and life quality of individuals with lower-limb amputations. Vision-based machine learning (ML) methods offer a scalable and non-invasive solution to gait analysis, but face challenges in correctly detecting and analyzing prosthesis, due to their unique appearances and new movement patterns. In this paper, we aim to bridge this gap by introducing a multi-purpose dataset, namely ProGait, to support multiple vision tasks including Video Object Segmentation, 2D Human Pose Estimation, and Gait Analysis (GA). ProGait provides 412 video clips from four above-knee amputees when testing multiple newly-fitted prosthetic legs through walking trials, and depicts the presence, contours, poses, and gait patterns of human subjects with transfemoral prosthetic legs. Alongside the dataset itself, we also present benchmark tasks and fine-tuned baseline models to illustrate the practical application and performance of the ProGait dataset. We compared our baseline models against pre-trained vision models, demonstrating improved generalizability when applying the ProGait dataset for prosthesis-specific tasks. The ProGait dataset is available at https://huggingface.co/datasets/ericyxy98/ProGait, and the source codes of our benchmark tasks are available at https://github.com/pittisl/ProGait.

Capturing and visualizing motion using skeleton-based techniques is a key aspect of computer vision, particularly in virtual reality (VR) settings. Its popularity has surged, driven by the simplicity of obtaining skeleton data and the growing appetite for virtual interaction. Although this skeleton data appears to be non-identifiable, it can be exploited to derive personally identifiable information (PII), posing a risk of inadvertent privacy breaches. In this paper, we explore the application of motion retargeting and its ability to mitigate privacy leakages. Motion retargeting can effectively transfer the motion from an initial user onto a dummy skeleton with the purpose of hiding PII. We propose a Privacy-centric Deep Motion Retargeting model (PMR), which mitigates the PII through adversarial learning. In our evaluation, our proposed model achieves motion retargeting performance on par with the current state-of-the-art models. More importantly, it effectively prevents the attackers from identifying the initial user.

In panoptic segmentation, individual instances must be separated within semantic classes. As state-of-the-art methods rely on a pre-defined set of classes, they struggle with novel categories and out-of-distribution (OOD) data. This is particularly problematic in safety-critical applications, such as autonomous driving, where reliability in unseen scenarios is essential. We address the gap between outstanding benchmark performance and reliability by proposing Prior2Former (P2F), the first approach for segmentation vision transformers rooted in evidential learning. P2F extends the mask vision transformer architecture by incorporating a Beta prior for computing model uncertainty in pixel-wise binary mask assignments. This design enables high-quality uncertainty estimation that effectively detects novel and OOD objects, enabling state-of-the-art anomaly instance segmentation and open-world panoptic segmentation. Unlike most segmentation models addressing unknown classes, P2F operates without access to OOD data samples or contrastive training on void (i.e., unlabeled) classes, making it highly applicable in real-world scenarios where such prior information is unavailable. Additionally, P2F can be flexibly applied to anomaly instance and panoptic segmentation. Through comprehensive experiments on the Cityscapes, COCO, SegmentMeIfYouCan, and OoDIS datasets, P2F demonstrates state-of-the-art performance. Especially in OoDIS, P2F ranks first in its category.

3D hand pose estimation plays a critical role in various human-computer interaction tasks. Single-frame 3D hand pose estimation methods have poor temporal smoothness and are easily affected by self-occlusion, which severely impacts their practical applicability. Traditional joint-based sequential pose estimation methods primarily focus on the human body and struggle to handle the complex hand structure, high degrees of freedom in hand motion, and rapidly changing hand motion trends. To address these challenges, we propose a prior-aware dynamic temporal modeling framework for sequential 3D hand pose estimation. We introduce a flexible memory mechanism to model hand prior information, which alleviates the scale and depth ambiguity in single-frame hand pose estimation. Additionally, we propose a dynamic temporal convolution module that adjusts the receptive field size and feature aggregation weights based on the motion information at each moment, effectively capturing rapid motion trends. By decoupling dynamic temporal modeling at the joint and hand levels, our method captures both subtle short-term variations and long-term motion trends, significantly improving the smoothness and accuracy of hand pose estimation. Experiments on four public datasets demonstrate that our method achieves the state-of-the-art results in terms of hand pose estimation accuracy and temporal smoothness.

Video understanding has made huge strides in recent years, relying largely on the power of transformers. As this architecture is notoriously expensive and video data is highly redundant, research into improving efficiency has become particularly relevant. Some creative solutions include token selection and merging. While most methods succeed in reducing the cost of the model and maintaining accuracy, an interesting pattern arises: most methods do not outperform the baseline of randomly discarding tokens. In this paper we take a closer look at this phenomenon and observe 5 principles of the nature of visual tokens. For example, we observe that the value of tokens follows a clear Pareto-distribution where most tokens have remarkably low value, and just a few carry most of the perceptual information. We build on these and further insights to propose a lightweight video model, LITE, that can select a small number of tokens effectively, outperforming state-of-the-art and existing baselines across datasets (Kinetics-400 and Something-Something-V2) in the challenging trade-off of computation (GFLOPs) vs accuracy. Experiments also show that LITE generalizes across datasets and even other tasks without the need for retraining. The code is released at https://github.com/maggieHao/Efficient-LITE.

We introduce Princeton365, a large-scale diverse dataset of 365 videos with accurate camera pose. Our dataset bridges the gap between accuracy and data diversity in current SLAM benchmarks by introducing a novel ground truth collection framework that leverages calibration boards and a 360 camera. We collect indoor, outdoor, and object scanning videos with synchronized monocular and stereo RGB video outputs as well as IMU. We further propose a new scene scale-aware evaluation metric for SLAM based on the the optical flow induced by the camera pose estimation error. In contrast to the current metrics, our new metric allows for comparison between the performance of SLAM methods across scenes as opposed to existing metrics such as Average Trajectory Error (ATE), allowing researchers to analyze the failure modes of their methods. We also propose a challenging Novel View Synthesis benchmark that covers cases not covered by current NVS benchmarks, such as fully non-Lambertian scenes with 360 camera trajectories. Please visit https://princeton365.cs.princeton.edu for the dataset, code, videos, and submission.

Synthesizing realistic Human-Object Interaction (HOI) motions is essential for creating believable digital characters and intelligent robots. Existing approaches rely on data-intensive learning models that struggle with the compositional structure of daily HOI motions, particularly for complex multi-object manipulation tasks. The exponential growth of possible interaction scenarios makes comprehensive data collection prohibitively expensive. The fundamental challenge is synthesizing unseen, complex HOI sequences without extensive task-specific training data. Here we show that PrimHOI generates complex HOI motions through spatial and temporal composition of generalizable interaction primitives defined by relative geometry. Our approach demonstrates that repetitive local contact patterns -- grasping, clamping, and supporting -- serve as reusable building blocks for diverse interaction sequences. Unlike previous data-driven methods requiring end-to-end training for each task variant, PrimHOI achieves zero-shot transfer to unseen scenarios through hierarchical primitive planning. Experimental validation demonstrates substantial improvements in adaptability, diversity, and motion quality compared to existing approaches.

Panoramic optical flow enables a comprehensive understanding of temporal dynamics across wide fields of view. However, severe distortions caused by sphere-to-plane projections, such as the equirectangular projection (ERP), significantly degrade the performance of conventional perspective-based optical flow methods, especially in polar regions. To address this challenge, we propose PriOr-Flow, a novel dual-branch framework that leverages the low-distortion nature of the orthogonal view to enhance optical flow estimation in these regions. Specifically, we introduce the Dual-Cost Collaborative Lookup (DCCL) operator, which jointly retrieves correlation information from both the primitive and orthogonal cost volumes, effectively mitigating distortion noise during cost volume construction. Furthermore, our Ortho-Driven Distortion Compensation (ODDC) module iteratively refines motion features of the primitive branch, further suppressing polar distortions. Extensive experiments demonstrate that PriOr-Flow is compatible with various perspective-based iterative optical flow methods and consistently achieves state-of-the-art performance on publicly available panoramic optical flow datasets, setting a new benchmark for wide-field motion estimation.

Recent generative models based on score matching and flow matching have significantly advanced generation tasks, but their potential in discriminative tasks remains underexplored. Previous approaches, such as generative classifiers, have not fully leveraged the capabilities of these models for discriminative tasks due to their intricate designs. We propose Pretrained Reversible Generation (PRG), which extracts unsupervised representations by reversing the generative process of a pretrained continuous generation model. PRG effectively reuses unsupervised generative models, leveraging their high capacity to serve as robust and generalizable feature extractors for downstream tasks. This framework enables the flexible selection of feature hierarchies tailored to specific downstream tasks. Our method consistently outperforms prior approaches across multiple benchmarks, achieving state-of-the-art performance among generative model based methods, including 78% top-1 accuracy on ImageNet at a resolution of 64x64. Extensive ablation studies, including out-of-distribution evaluations, further validate the effectiveness of our approach. PRG is available at https://opendilab.github.io/PRG.

Recently, learning-based multi-agent cooperative perception has garnered widespread attention. However, the inherent vulnerabilities of neural networks, combined with the risks posed by cooperative communication as a wide-open backdoor, render these systems highly susceptible to adversarial attacks.Existing attack methods lack stealth as they perturb transmitted information indiscriminately, producing numerous false positives that are readily detected by consensus-based defenses. This paper proposes Pretend Benign (PB), a novel stealthy adversarial attack method that exploits vulnerabilities in cooperative perception to enable the attacker to disguise as a benign cooperator. To achieve this, we first introduce the Attack Region Selection (ARS) module, which divides the perception area into sub-regions based on confidence levels to pinpoint optimal attack locations. Then, we propose Multi-target Adversarial Perturbation Generation (MAPG), which maintains consensus, gain the victim's trust, and thereby reverse the normal cooperative role of perception. To mitigate the latency in adversarial signal generation and communication, we further propose a real-time attack by predicting future information through historical feature flow. Extensive experiments on the OPV2V and V2XSet datasets demonstrate that PB effectively bypasses state-of-the-art defense methods, underscoring its stealth and efficacy.

We introduce Preserve Anything, a novel method for con-trolled image synthesis that addresses key limitations in ob-ject preservation and semantic consistency in text-to-image(T2I) generation. Existing approaches often fail (i) to pre-serve multiple objects with fidelity, (ii) maintain semanticalignment with prompts, or (iii) provide explicit control overscene composition. To overcome these challenges, the pro-posed method employs an N-channel ControlNet that inte-grates (i) object preservation with size and placement ag-nosticism, color and detail retention, and artifact elimi-nation, (ii) high-resolution, semantically consistent back-grounds with accurate shadows, lighting, and prompt ad-herence, and (iii) explicit user control over background lay-outs and lighting conditions. Key components of our frame-work include object preservation and background guid-ance modules, enforcing lighting consistency and a high-frequency overlay module to retain fine details while mit-igating unwanted artifacts. We introduce a benchmarkdataset consisting of 240K natural images filtered for aes-thetic quality and 18K 3D-rendered synthetic images withmetadata such as lighting, camera angles, and object rela-tionships. This dataset addresses the deficiencies of existingbenchmarks and allows a complete evaluation. Empiricalresults demonstrate that our method achieves state-of-the-art performance, significantly improving feature-space fi-delity (FID 15.26) and semantic alignment (CLIP-S 32.85)while maintaining competitive aesthetic quality. We alsoconducted a user study to demonstrate the efficacy of theproposed work on unseen benchmark and observed a re-markable improvement of 25%, 19%, 13%, and 14% in terms of prompt alignment, photorealism, thepresence of AI artifacts, and natural aesthetics over existingworks.

Whether snipping with scissors or opening a box, humans can quickly understand the 3D configurations of familiar objects. For novel objects, we can resort to long-form inspection to build intuition. The more we observe the object, the better we get at predicting its 3D state immediately. Existing systems, however, are limited to either optimizing underlying representations from multi-view observations or training a feed-forward predictor from supervised datasets. We introduce Predict-Optimize-Distill (POD), a self-improving framework that interleaves prediction and optimization in a mutually reinforcing cycle to achieve better 4D object understanding with increasing observation time. Given a multi-view object scan and a long-form monocular video of human-object interaction, POD iteratively trains a neural network to predict local part poses from RGB frames, uses this predictor to initialize a global optimization which refines output poses through inverse rendering, then finally distills the results of optimization back into the model by generating synthetic self-labeled training data from novel viewpoints. Each iteration improves both the predictive model and the optimized motion trajectory, creating a virtuous cycle that bootstraps its own training data to learn about the pose configurations of an object. We also introduce a quasi-multiview mining strategy for reducing depth ambiguity by leveraging long video. We evaluate POD on 14 real-world and 5 synthetic objects with various joint types, including revolute and prismatic joints as well as multi-body configurations where parts detach or reattach independently. POD demonstrates significant improvement over a pure optimization baseline which gets stuck in local minima, particularly for longer videos. We also find that POD's performance improves with both video length and successive iterations of the self-improving cycle, highlighting its ability to scale performance with additional observations and compute.

We present visual action prompts, a unified action representation for action-to-video generation of complex high-DoF interactions while maintaining transferable visual dynamics across domains. Action-driven video generation faces a precision-generality tradeoff: existing methods using text, primitive actions, or coarse masks offer generality but lack precision, while agent-centric action signals provide precision at the cost of cross-domain transferability. To balance action precision and dynamic transferability, we propose to "render" actions into precise visual prompts as domain-agnostic representations that preserve both geometric precision and cross-domain adaptability for complex actions; specifically, we choose visual skeletons for its generality and accessibility. We propose robust pipelines to construct skeletons from two interaction-rich data sources -- human-object interactions (HOI) and dexterous robotic manipulation -- enabling cross-domain training of action-driven generative models. By integrating visual skeletons into pretrained video generation models via lightweight fine-tuning, we enable precise action control of complex interaction while preserving the learning of cross-domain dynamics. Experiments on EgoVid, RT-1 and DROID demonstrate the effectiveness of our proposed approach.

The paper-to-video task converts a research paper into a structured video abstract, distilling key concepts, methods, and conclusions into an accessible, well-organized format. While state-of-the-art video generation models demonstrate potential, they are constrained by limited LLM context windows, rigid video duration constraints, limited stylistic diversity, and an inability to represent domain-specific knowledge. To address these limitations, we introduce Preacher, the first paper-to-video agentic system. Preacher employs a top-down approach to decompose, summarize, and reformulate the paper, followed by bottom-up video generation, synthesizing diverse video segments into a coherent abstract. To align cross-modal representations, we define key scenes and introduce a Progressive Chain of Thought (P-CoT) for granular, iterative planning. Preacher successfully generates high-quality video abstracts across five research fields, demonstrating expertise beyond current video generation models.

To ensure safe autonomous driving in complex urban environments, it is essential not only to develop high-performance object detection models but also to establish a diverse and representative dataset that captures a wide range of urban scenarios and object characteristics. To address these challenges, we introduce a new multi-class 3D LiDAR dataset that comprehensively reflects various urban environments and object types, along with a robust semi-supervised 3D object detection (SSOD) framework. Our SSOD framework leverages a novel multiple teachers model, where similar object classes are grouped and supervised by category-specialized teacher networks. This category-specific collaborative guidance enables the student network to learn more effectively, leading to improved object detection performance. Additionally, we propose the Pseudo-points Generator (PointGen), a simple yet effective technique designed to enhance the generation of high-quality pseudo-labels for the teacher network, mitigating the impact of sparse LiDAR point clouds. Extensive experiments on the Waymo Open Dataset, KITTI, and our newly introduced dataset validate the effectiveness of both our dataset and SSOD framework. Experimental results demonstrate that our approach consistently outperforms state-of-the-art 3D SSOD methods across all evaluated datasets. To encourage further research in this domain, we will publicly release our multi-class LiDAR dataset and source code on our GitHub repository.

ate and locate the peak of the heatmap, while adaptively balancing the influence of landmarks and background pixels through self-weighting, addressing the extreme imbalance between landmarks and non-landmarks. More advanced is that our PossLoss is sample-sensitive, which can distinguish easy and hard landmarks and adaptively make the model focused more on hard landmarks. Moreover, it addresses the difficulty of accurately evaluating heatmap distribution, especially in addressing small errors due to peak mismatches. We analyzed and evaluated our PossLoss on three challenging facial landmark detection tasks. The experimental results show that our PossLoss significantly improves the performance of landmark detection and outperforms the state-of-the-art methods.

Despite considerable efforts to enhance the generalization of 3D pose estimators without costly 3D annotations, existing data augmentation methods struggle in real-world scenarios with diverse human appearances and complex poses. We propose PoseSyn, a novel data synthesis framework that transforms abundant in-the-wild 2D pose dataset into diverse 3D pose-image pairs. PoseSyn comprises two key components: Error Extraction Module (EEM), which identifies challenging poses from the 2D pose datasets, and Motion Synthesis Module (MSM), which synthesizes motion sequences around the challenging poses. Then, by generating realistic 3D training data via a human animation model--aligned with challenging poses and appearances--PoseSyn boosts the accuracy of various 3D pose estimators by up to 14% across real-world benchmarks including various backgrounds and occlusions, challenging poses, and multi-view scenarios. Extensive experiments further confirm that PoseSyn is a scalable and effective approach for improving generalization without relying on expensive 3D annotations, regardless of the pose estimator's model size or design.