GenAI4ELab/papercli-papers huggingface dataset:
PS-Mamba: Spatial-Temporal Graph Mamba for Pose Sequence Refinement
Authors: Haoye Dong; Gim Hee Lee
Venue: ICCV 2025 (main)

Abstract: Human pose sequence refinement plays a crucial role in improving the temporal coherence of pose estimation across the sequence of frames. Despite its importance in real-world applications, human pose sequence refinement has received less attention than human pose estimation. In this paper, we propose PS-Mamba, a novel framework that refines human pose sequences by integrating spatial-temporal graph learning with state space modeling. Specifically, we introduce the Spatial-Temporal Graph State Space (ST-GSS) block, which captures spatial and temporal dependencies across joints to smooth pose sequences while preserving structural integrity. The spatial-temporal graph learns intricate joint interactions, while the state space component effectively manages temporal dynamics, reducing both short- and long-term pose instability. Besides, we incorporate a dynamic graph weight matrix to adaptively model the relative influence of joint interactions, further mitigating pose ambiguity. Experiments on challenging benchmarks show that our PS-Mamba outperforms SOTAs, achieving -14.21 mm MPJPE (+18.5%\uparrow), -13.59 mm PA-MPJPE (+22.1%\uparrow), and -0.42 mm/s2 ACCEL (+9.7%\uparrow) compared to SynSP on AIST++, significantly reducing jitters and enhancing pose stability.
Model Reveals What to Cache: Profiling-Based Feature Reuse for Video Diffusion Models
Authors: Xuran Ma; Yexin Liu; Yaofu Liu; Xianfeng Wu; Mingzhe Zheng; Zihao Wang; Ser-Nam Lim; Harry Yang
Venue: ICCV 2025 (main)

Abstract: Video generation using diffusion models has shown remarkable progress, yet it remains computationally expensive due to the repeated processing of redundant features across blocks and steps. To address this, we propose a novel adaptive feature reuse mechanism that dynamically identifies and caches the most informative features by focusing on foreground and caching more on background, significantly reducing computational overhead with less sacrificing video quality. By leveraging the step and block caching, our method achieves up to 1.8x speed up on HunyuanVideo while maintaining competitive performance on Vbench, PSNR, SSIM, FID and LPIPS. Extensive experiments demonstrate that our approach not only improves efficiency but also enhances the quality of generated videos. The proposed method is generalizable and can be integrated into existing diffusion transformer frameworks.
IRASim: A Fine-Grained World Model for Robot Manipulation
Authors: Fangqi Zhu; Hongtao Wu; Song Guo; Yuxiao Liu; Chilam Cheang; Tao Kong
Venue: ICCV 2025 (main)

Abstract: World models allow autonomous agents to plan and explore by predicting the visual outcomes of different actions. However, for robot manipulation, it is challenging to accurately model the fine-grained robot-object interaction within the visual space using existing methods which overlook precise alignment between each action and the corresponding frame. In this paper, we present IRASim, a novel world model capable of generating videos with fine-grained robot-object interaction details, conditioned on historical observations and robot action trajectories. We train a diffusion transformer and introduce a novel frame-level action-conditioning module within each transformer block to explicitly model and strengthen the action-frame alignment. Extensive experiments show that: (1) the quality of the videos generated by our method surpasses all the baseline methods and scales effectively with increased model size and computation; (2) policy evaluations using IRASim exhibit a strong correlation with those using the ground-truth simulator, highlighting its potential to accelerate real-world policy evaluation; (3) testing-time scaling through model-based planning with IRASim significantly enhances policy performance, as evidenced by an improvement in the IoU metric on the Push-T benchmark from 0.637 to 0.961; (4) IRASim provides flexible action controllability, allowing virtual robotic arms in datasets to be controlled via a keyboard or VR controller. Video and code are available at https://gen-irasim.github.io/.
Diffusion-Based Imaginative Coordination for Bimanual Manipulation
Authors: Huilin Xu; Jian Ding; Jiakun Xu; Ruixiang Wang; Jun Chen; Jinjie Mai; Yanwei Fu; Bernard Ghanem; Feng Xu; Mohamed Elhoseiny
Venue: ICCV 2025 (main)

Abstract: Bimanual manipulation is crucial in robotics, enabling complex tasks in industrial automation and household services. However, it poses significant challenges due to the high-dimensional action space and intricate coordination requirements. While video prediction has been recently studied for representation learning and control, leveraging its ability to capture rich dynamic and behavioral information, its potential for enhancing bimanual coordination remains underexplored. To bridge this gap, we propose a unified diffusion-based framework for the joint optimization of video and action prediction. Specifically, we propose a multi-frame latent prediction strategy that encodes future states in a compressed latent space, preserving task-relevant features. Furthermore, we introduce a unidirectional attention mechanism where video prediction is conditioned on the action, but action prediction remains independent of video prediction. This design allows us to omit video prediction during inference, significantly enhancing efficiency. Experiments on two simulated benchmarks and a real-world setting demonstrate a significant improvement in the success rate over the strong baseline ACT using our method, achieving a 24.9% increase on ALOHA, an 11.1% increase on RoboTwin, and a 32.5% increase in real-world experiments. Our models and code are publicly available at https://github.com/return-sleep/Diffusion_based_imaginative_Coordination.
Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding
Authors: Ta Duc Huy; Duy Anh Huynh; Yutong Xie; Yuankai Qi; Qi Chen; Phi Le Nguyen; Sen Kim Tran; Son Lam Phung; Anton van den Hengel; Zhibin Liao; Minh-Son To; Johan W. Verjans; Vu Minh Hieu Phan
Venue: ICCV 2025 (main)

Abstract: Visual grounding (VG) is the capability to identify the specific regions in an image associated with a particular text description. In medical imaging, VG enhances interpretability by highlighting relevant pathological features corresponding to textual descriptions, improving model transparency and trustworthiness for wider adoption of deep learning models in clinical practice. Current models struggle to associate textual descriptions with disease regions due to inefficient attention mechanisms and a lack of fine-grained token representations. In this paper, we empirically demonstrate two key observations. First, current VLMs assign high norms to background tokens, diverting the model's attention from regions of disease. Second, the global tokens used for cross-modal learning are not representative of local disease tokens. This hampers identifying correlations between the text and disease tokens. To address this, we introduce simple, yet effective Disease-Aware Prompting (DAP) process, which uses the explainability map of a VLM to identify the appropriate image features. This simple strategy amplifies disease-relevant regions while suppressing background interference. Without any additional pixel-level annotations, DAP improves visual grounding accuracy by 20.74% compared to state-of-the-art methods across three major chest X-ray datasets.
Dataset Distillation as Data Compression: A Rate-Utility Perspective
Authors: Youneng Bao; Yiping Liu; Zhuo Chen; Yongsheng Liang; Mu Li; Kede Ma
Venue: ICCV 2025 (main)

Abstract: Driven by the "scale-is-everything" paradigm, modern machine learning increasingly demands ever-larger datasets and models, yielding prohibitive computational and storage requirements. Dataset distillation mitigates this by compressing an original dataset into a small set of synthetic samples, while preserving its full utility. Yet, existing methods either maximize performance under fixed storage budgets or pursue suitable synthetic data representations for redundancy removal, without jointly optimizing both objectives. In this work, we propose a joint rate-utility optimization method for dataset distillation. We parameterize synthetic samples as optimizable latent codes decoded by extremely lightweight networks. We estimate the Shannon entropy of quantized latents as the rate measure and plug any existing distillation loss as the utility measure, trading them off via a Lagrange multiplier. To enable fair, cross-method comparisons, we introduce bits per class (bpc), a precise storage metric that accounts for sample, label, and decoder parameter costs. On CIFAR-10, CIFAR-100, and ImageNet-128, our method achieves up to 170xgreater compression than standard distillation at comparable accuracy. Across diverse bpc budgets, distillation losses, and backbone architectures, our approach consistently establishes better rate-utility trade-offs.
GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding
Authors: Rui Hu; Lianghui Zhu; Yuxuan Zhang; Tianheng Cheng; Lei Liu; Heng Liu; Longjin Ran; Xiaoxin Chen; Wenyu Liu; Xinggang Wang
Venue: ICCV 2025 (main)

Abstract: Pixel grounding, encompassing tasks such as Referring Expression Segmentation (RES), has garnered considerable attention due to its potential for bridging the gap between vision and language modalities. However, advancements in this domain are currently constrained by limitations inherent in existing datasets, including limited object categories, insufficient textual diversity, and a scarcity of high-quality annotations. To mitigate these limitations, we introduce GroundingSuite, which comprises: (1) an automated data annotation framework leveraging multiple Vision-Language Model (VLM) agents; (2) a large-scale training dataset encompassing 9.56 million diverse referring expressions and their corresponding segmentations; and (3) a meticulously curated evaluation benchmark consisting of 3,800 images. The GroundingSuite dataset boosts model performance to state-of-the-art levels. Specifically, a cIoU of 68.9 on gRefCOCO and a gIoU of 55.3 on RefCOCOm. Moreover, the GroundingSuite annotation framework demonstrates superior efficiency compared to the current leading data annotation method, i.e., 4.5x faster than the GLaMM. Codes are available at: https://github.com/hustvl/GroundingSuite.
Consensus-Driven Active Model Selection
Authors: Justin Kay; Grant Van Horn; Subhransu Maji; Daniel Sheldon; Sara Beery
Venue: ICCV 2025 (main)

Abstract: The widespread availability of off-the-shelf machine learning models poses a challenge: which model, of the many available candidates, should be chosen for a given data analysis task? This question of model selection is traditionally answered by collecting and annotating a validation dataset---a costly and time-intensive process. We propose a method for active model selection, using predictions from candidate models to prioritize the labeling of test data points that efficiently differentiate the best candidate. Our method, CODA, performs consensus-driven active model selection by modeling relationships between classifiers, categories, and data points within a probabilistic framework. The framework uses the consensus and disagreement between models in the candidate pool to guide the label acquisition process, and Bayesian inference to update beliefs about which model is best as more information is collected. We validate our approach by curating a collection of 26 benchmark tasks capturing a range of model selection scenarios.CODA outperforms existing methods for active model selection significantly, reducing the annotation effort required to discover the best model by upwards of 70% compared to the previous state-of-the-art. We will make our code and data public. Code and data are available at https://github.com/justinkay/coda.
Learning an Implicit Physics Model for Image-based Fluid Simulation
Authors: Emily Yue-Ting Jia; Jiageng Mao; Zhiyuan Gao; Yajie Zhao; Yue Wang
Venue: ICCV 2025 (main)

Abstract: Humans possess an exceptional ability to imagine 4D scenes, encompassing both motion and 3D geometry, from a single still image. This ability is rooted in our accumulated observations of similar scenes and an intuitive understanding of physics. In this paper, we aim to replicate this capacity in neural networks, specifically focusing on natural fluid imagery. Existing methods for this task typically employ simplistic 2D motion estimators to animate the image, leading to motion predictions that often defy physical principles, resulting in unrealistic animations. Our approach introduces a novel method for generating 4D scenes with physics-consistent animation from a single image. We propose the use of a physics-informed neural network that predicts motion for each surface point, guided by a loss term derived from fundamental physical principles, including the Navier-Stokes equations. To capture appearance, we predict feature-based 3D Gaussians from the input image and its estimated depth, which are then animated using the predicted motions and rendered from any desired camera perspective. Experimental results highlight the effectiveness of our method in producing physically plausible animations, showcasing significant performance improvements over existing methods. Our project page is \href https://physfluid.github.io/  https://physfluid.github.io/ .
Task-Decoupled Bezier Surface Constraint for Uneven Low-Light Image Enhancement
Authors: Xingxiang Zhou; Xiangdong Su; Haoran Zhang; Wei Chen; Guanglai Gao
Venue: ICCV 2025 (main)

Abstract: Low-light image enhancement (LLIE) is a fundamental task in computer vision. Its goal is to extract more useful information from dark regions. Many existing methods have made excellent strides in improving image brightness and enhancing texture details. However, these approaches often lead to overexposure in certain regions when dealing with unevenly illuminated images, resulting in the loss of original information within the images. To address this issue, we propose a Bezier surface constraint (BSCNet) method based on task decoupling to enhance low-light images with uneven brightness. Specifically, we design a diffusion model with a branch structure that separates the enhancement process into brightness adjustment and color restoration, enabling independent control over brightness uniformity. Additionally, we use Bezier surfaces as a learning target to impose smoothness constraints on the image, thereby addressing the issue of uneven brightness in the enhanced image. To counteract potential detail loss introduced by Bezier surfaces, we introduce a spatial-frequency reconstruction module based on the Fourier transform to enhance fine-grained texture information. Experimental comparisons of six generalized LLIE datasets show that our proposed method has demonstrated outstanding effectiveness.
PRISM: Reducing Spurious Implicit Biases in Vision-Language Models with LLM-Guided Embedding Projection
Authors: Mahdiyar Molahasani; Azadeh Motamedi; Michael Greenspan; Il-Min Kim; Ali Etemad
Venue: ICCV 2025 (main)

Abstract: We introduce Projection-based Reduction of Implicit Spurious bias in vision-language Models (PRISM), a new data-free and task-agnostic solution for bias mitigation in VLMs like CLIP. VLMs often inherit and amplify biases in their training data, leading to skewed predictions.PRISM is designed to debias VLMs without relying on predefined bias categories or additional external data. It operates in two stages: first, an LLM is prompted with simple class prompts to generate scene descriptions that contain spurious correlations. Next, PRISM uses our novel contrastive-style debiasing loss to learn a projection that maps the embeddings onto a latent space that minimizes spurious correlations while preserving the alignment between image and text embeddings. Extensive experiments demonstrate that PRISM outperforms current debiasing methods on the commonly used Waterbirds and CelebA datasets We make our code public at: https://github.com/MahdiyarMM/PRISM.
Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance
Authors: Li Hu; Guangyuan Wang; Zhen Shen; Xin Gao; Dechao Meng; Lian Zhuo; Peng Zhang; Bang Zhang; Liefeng Bo
Venue: ICCV 2025 (main)

Abstract: Recent character image animation methods based on diffusion models, such as Animate Anyone, have made significant progress in generating consistent and generalizable character animations. However, these approaches fail to produce reasonable associations between characters and their environments. To address this limitation, we introduce Animate Anyone 2, aiming to animate characters with environment affordance. Beyond extracting motion signals from source video, we additionally capture environmental representations as conditional inputs. The environment is formulated as the region with the exclusion of characters and our model generates characters to populate these regions while maintaining coherence with the environmental context. We propose a shape-agnostic mask strategy that more effectively characterizes the relationship between character and environment. Furthermore, to enhance the fidelity of object interactions, we leverage an object guider to extract features of interacting objects and employ spatial blending for feature injection. We also introduce a pose modulation strategy that enables the model to handle more diverse motion patterns. Experimental results demonstrate the superior performance of the proposed method.
DATA: Domain-And-Time Alignment for High-Quality Feature Fusion in Collaborative Perception
Authors: Chengchang Tian; Jianwei Ma; Yan Huang; Zhanye Chen; Honghao Wei; Hui Zhang; Wei Hong
Venue: ICCV 2025 (main)

Abstract: Feature-level fusion shows promise in collaborative perception (CP) through balanced performance and communication bandwidth trade-off. However, its effectiveness critically relies on input feature quality. The acquisition of high-quality features faces domain gaps from hardware diversity and deployment conditions, alongside temporal misalignment from transmission delays. These challenges degrade feature quality with cumulative effects throughout the collaborative network. In this paper, we present the Domain-And-Time Alignment (DATA) network, designed to systematically align features while maximizing their semantic representations for fusion. Specifically, we propose a Consistency-preserving Domain Alignment Module (CDAM) that reduces domain gaps through proximal-region hierarchical downsampling and observability-constrained discriminator. We further propose a Progressive Temporal Alignment Module (PTAM) to handle transmission delays via multi-scale motion modeling and two-stage compensation. Building upon the aligned features, an Instance-focused Feature Aggregation Module (IFAM) is developed to enhance semantic representations. Extensive experiments demonstrate that DATA achieves state-of-the-art performance on three typical datasets, maintaining robustness with severe communication delays and pose errors.
LongAnimation: Long Animation Generation with Dynamic Global-Local Memory
Authors: Nan Chen; Mengqi Huang; Yihao Meng; Zhendong Mao
Venue: ICCV 2025 (main)

Abstract: Animation colorization is a crucial part of real animation industry production. Long animation colorization has high labor costs. Therefore, automated long animation colorization based on the video generation model has significant research value. Existing studies are limited to short-term colorization. These studies adopt a local paradigm, fusing overlapping features to achieve smooth transitions between local segments. However, the local paradigm neglects global information, failing to maintain long-term color consistency. In this study, we argue that ideal long-term color consistency can be achieved through a dynamic global-local paradigm, i.e., dynamically extracting global color consistent features relevant to the current generation. Specifically, we propose LongAnimation, a novel framework, which mainly includes a SketchDiT, a Dynamic Global-Local Memory (DGLM), and a Color Consistency Reward. The SketchDiT captures hybrid reference features to support the DGLM module. The DGLM module employs a long video understanding model to dynamically compress global historical features and adaptively fuse them with the current generation features. To refine the color consistency, we introduce a Color Consistency Reward. During inference, we propose a color consistency fusion to smooth the video segment transition. Extensive experiments on both short-term (14 frames) and long-term (average 500 frames) animations show the effectiveness of LongAnimation in maintaining short-term and long-term color consistency for open-domain animation colorization task.
Is Tracking Really More Challenging in First Person Egocentric Vision?
Authors: Matteo Dunnhofer; Zaira Manigrasso; Christian Micheloni
Venue: ICCV 2025 (main)

Abstract: Visual object tracking and segmentation are becoming fundamental tasks for understanding human activities in egocentric vision. Recent research has benchmarked state-of-the-art methods and concluded that first person egocentric vision presents challenges compared to previously studied domains. However, these claims are based on evaluations conducted across significantly different scenarios. Many of the challenging characteristics attributed to egocentric vision are also present in third person videos of human-object activities. This raises a critical question: how much of the observed performance drop stems from the unique first person viewpoint inherent to egocentric vision versus the domain of human-object activities? To address this question, we introduce a new benchmark study designed to disentangle such factors. Our evaluation strategy enables a more precise separation of challenges related to the first person perspective from those linked to the broader domain of human-object activity understanding. By doing so, we provide deeper insights into the true sources of difficulty in egocentric tracking and segmentation, facilitating more targeted advancements on this task.
Bringing RNNs Back to Efficient Open-Ended Video Understanding
Authors: Weili Xu; Enxin Song; Wenhao Chai; Xuexiang Wen; Tian Ye; Gaoang Wang
Venue: ICCV 2025 (main)

Abstract: The challenge of long video understanding lies in its high computational complexity and prohibitive memory cost, since the memory and computation required by transformer-based LLMs scale quadratically with input sequence length. We propose AuroraLong to address this challenge by replacing the LLM component in MLLMs with a linear RNN language model that handles input sequence of arbitrary length with constant-size hidden states. To further increase throughput and efficiency, we combine visual token merge with linear RNN models by reordering the visual tokens by their sizes in ascending order. Despite having only 2B parameters and being trained exclusively on public data, AuroraLong achieves performance comparable to Transformer-based models of similar size trained on private datasets across multiple video benchmarks. This demonstrates the potential of efficient, linear RNNs to democratize long video understanding by lowering its computational entry barrier. To our best knowledge, we are the first to use a linear RNN based LLM backbone in a LLaVA-like model for open-ended video understanding.