GenAI4ELab/papercli-papers huggingface dataset:
A Hidden Stumbling Block in Generalized Category Discovery: Distracted Attention
Authors: Qiyu Xu; Zhanxuan Hu; Yu Duan; Ercheng Pei; Yonghang Tai
Venue: ICCV 2025 (main)

Abstract: Generalized Category Discovery (GCD) aims to classify unlabeled data from both known and unknown categories by leveraging knowledge from labeled known categories. While existing methods have made notable progress, they often overlook a hidden stumbling block in GCD: distracted attention. Specifically, when processing unlabeled data, models tend to focus not only on key objects in the image but also on task-irrelevant background regions, leading to suboptimal feature extraction. To remove this stumbling block, we propose Attention Focusing (AF), an adaptive mechanism designed to sharpen the model's focus by pruning non-informative tokens. AF consists of two simple yet effective components: Token Importance Measurement (TIME) and Token Adaptive Pruning (TAP), working in a cascade. TIME quantifies token importance across multiple scales, while TAP prunes non-informative tokens by utilizing the multi-scale importance scores provided by TIME. AF is a lightweight, plug-and-play module that integrates seamlessly into existing GCD methods with minimal computational overhead. When incorporated into one prominent GCD method, SimGCD, AF achieves up to 15.4% performance improvement over the baseline with minimal computational overhead. The implementation code is provided in https://github.com/Afleve/AFGCD.
High-Precision 3D Measurement of Complex Textured Surfaces Using Multiple Filtering Approach
Authors: Yuchong Chen; Jian Yu; Shaoyan Gai; Zeyu Cai; Feipeng Da
Venue: ICCV 2025 (main)

Abstract: In structured light systems, measurement accuracy tends to decline significantly when evaluating complex textured surfaces, particularly at boundaries between different colors. To address this issue, this paper conducts a detailed analysis to develop an error model that illustrates the relationship between phase error and image characteristics, specifically the blur level, grayscale value, and grayscale gradient. Based on this model, a high-precision approach for measuring complex textured targets is introduced, employing a multiple filtering approach. This approach first applies a sequence of filters to vary the blur level of the captured patterns, allowing calculation of phase differences under different blur conditions. Then, these phase differences are used in the constructed error model to identify the critical parameter causing phase errors. Finally, phase recovery is performed using the calibrated parameter, effectively reducing errors caused by complex textures. Experimental comparisons exhibit that this method reduces the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 40.31% and 40.78%, respectively. In multiple experiments, its performance generally surpassed that of existing methods, demonstrating improved accuracy and robustness.
Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers
Authors: Lukas Kuhn; Sari Sadiya; Jörg Schlötterer; Florian Buettner; Christin Seifert; Gemma Roig
Venue: ICCV 2025 (main)

Abstract: Shortcut learning, i.e., a model's reliance on undesired features not directly relevant to the task, is a major challenge that severely limits the applications of machine learning algorithms, particularly when deploying them to assist in making sensitive decisions, such as in medical diagnostics. In this work, we leverage recent advancements in machine learning to create an unsupervised framework that is capable of both detecting and mitigating shortcut learning in transformers. We validate our method on multiple datasets. Results demonstrate that our framework significantly improves both worst-group accuracy (samples misclassified due to shortcuts) and average accuracy, while minimizing human annotation effort. Moreover, we demonstrate that the detected shortcuts are meaningful and informative to human experts, and that our framework is computationally efficient, allowing it to be run on consumer hardware.
Adaptive Caching for Faster Video Generation with Diffusion Transformers
Authors: Kumara Kahatapitiya; Haozhe Liu; Sen He; Ding Liu; Menglin Jia; Chenyang Zhang; Michael S. Ryoo; Tian Xie
Venue: ICCV 2025 (main)

Abstract: Generating temporally-consistent high-fidelity videos can be computationally expensive, especially over longer temporal spans. More-recent Diffusion Transformers (DiTs)--- despite making significant headway in this context--- have only heightened such challenges as they rely on larger models and heavier attention mechanisms, resulting in slower inference speeds. In this paper, we introduce a method to accelerate video DiTs, termed Adaptive Caching (AdaCache), which is motivated by the fact that 'not all videos are created equal': meaning, some videos require fewer denoising steps to attain a reasonable quality than others. Building on this, we not only cache computations through the diffusion process, but also devise a caching schedule tailored to each video generation, maximizing the quality-latency trade-off. We further introduce a Motion Regularization (MoReg) scheme to utilize video information within AdaCache, essentially controlling the compute allocation based on motion content. Altogether, our plug-and-play contributions grant significant inference speedups (e.g. up to 4.7x on Open-Sora 720p - 2s video generation) without sacrificing the generation quality, across multiple video DiT baselines.
RGE-GS: Reward-Guided Expansive Driving Scene Reconstruction via Diffusion Priors
Authors: Sicong Du; Jiarun Liu; Qifeng Chen; Hao-Xiang Chen; Tai-Jiang Mu; Sheng Yang
Venue: ICCV 2025 (main)

Abstract: A single-pass driving clip frequently results in incomplete scanning of the road structure, making reconstructed scene expanding a critical requirement for sensor simulators to effectively regress driving actions. Although contemporary 3D Gaussian Splatting (3DGS) techniques achieve remarkable reconstruction quality, their direct extension through the integration of diffusion priors often introduces cumulative physical inconsistencies and compromises training efficiency. To address these limitations, we present RGE-GS, a novel expansive reconstruction framework that synergizes diffusion-based generation with reward-guided Gaussian integration. The RGE-GS framework incorporates two key innovations: First, we propose a reward network that learns to identify and prioritize consistently generated patterns prior to reconstruction phases, thereby enabling selective retention of diffusion outputs for spatial stability. Second, during the reconstruction process, we devise a differentiated training strategy that automatically adjust Gaussian optimization progress according to scene converge metrics, which achieving better convergence than baseline methods. Extensive evaluations of publicly available datasets demonstrate that RGE-GS achieves state-of-the-art performance in reconstruction quality.
EgoMusic-driven Human Dance Motion Estimation with Skeleton Mamba
Authors: Quang Nguyen; Nhat Le; Baoru Huang; Minh Nhat Vu; Chengcheng Tang; Van Nguyen; Ngan Le; Thieu Vo; Anh Nguyen
Venue: ICCV 2025 (main)

Abstract: Estimating human dance motion is a challenging task with various industrial applications. Recently, many efforts have focused on predicting human dance motion using either egocentric video or music as input. However, the task of jointly estimating human motion from both egocentric video and music remains largely unexplored. In this paper, we aim to develop a new method that predicts human dance motion from both egocentric video and music. In practice, the egocentric view often obscures much of the body, making accurate full-pose estimation challenging. Additionally, incorporating music requires the generated head and body movements to align well with both visual and musical inputs. We first introduce EgoAIST++, a new large-scale dataset that combines both egocentric views and music with more than 36 hours of dancing motion. Drawing on the success of diffusion models and Mamba on modeling sequences, we develop an EgoMusic Motion Network with a core Skeleton Mamba that explicitly captures the skeleton structure of the human body. We illustrate that our approach is theoretically supportive. Intensive experiments show that our method clearly outperforms state-of-the-art approaches and generalizes effectively to real-world data.
OmniCache: A Trajectory-Oriented Global Perspective on Training-Free Cache Reuse for Diffusion Transformer Models
Authors: Huanpeng Chu; Wei Wu; Guanyu Feng; Yutao Zhang
Venue: ICCV 2025 (main)

Abstract: Diffusion models have emerged as a powerful paradigm for generative tasks such as image synthesis and video generation, with Transformer architectures further enhancing performance. However, the high computational cost of diffusion Transformers--stemming from a large number of sampling steps and complex per-step computations--presents significant challenges for real-time deployment. In this paper, we introduce OmniCache, a training-free acceleration method that exploits the global redundancy inherent in the denoising process. Unlike existing methods that determine caching strategies based on inter-step similarities and tend to prioritize reusing later sampling steps, our approach originates from the sampling perspective of DIT models. We systematically analyze the model's sampling trajectories and strategically distribute cache reuse across the entire sampling process. This global perspective enables more effective utilization of cached computations throughout the diffusion trajectory, rather than concentrating reuse within limited segments of the sampling procedure. In addition, during cache reuse, we dynamically estimate the corresponding noise and filter it out to reduce its impact on the sampling direction. Extensive experiments demonstrate that our approach accelerates the sampling process while maintaining competitive generative quality, offering a promising and practical solution for efficient deployment of diffusion-based generative models.
VIPerson: Flexibly Generating Virtual Identity for Person Re-Identification
Authors: Xiao-Wen Zhang; Delong Zhang; Yi-Xing Peng; Zhi Ouyang; Jingke Meng; Wei-Shi Zheng
Venue: ICCV 2025 (main)

Abstract: Person re-identification (ReID) is to match the person images under different camera views. Training ReID models necessitates a substantial amount of labeled real-world data, leading to high labeling costs and privacy issues. Although several ReID data synthetic methods are proposed to address these issues, they fail to generate images with new identities or real-world camera styles. In this paper, we propose a novel pedestrian generation pipeline, VIPerson, to generate camera-realistic pedestrian images with flexible Virtual Identities for the Person ReID task. VIPerson focuses on three key factors in data synthesis: (I) Virtual identity diversity: Enhancing the latent diffusion model with our proposed dropout text embedding, we flexibly generate random and hard identities. (II) Scalable cross-camera variations: VIPerson introduces scalable variations of scenes and poses within each identity. (III) Camera-realistic style: Adopting an identity-agnostic approach to transfer realistic styles, we avoid privacy exposure of real identities. Extensive experimental results across a broad range of downstream ReID tasks demonstrate the superiority of our generated dataset over existing methods. In addition, VIPerson can be adapted to the identity expansion scenario, which widens the application of our pipeline. The dataset and code of VIPerson are available at https://isee-laboratory.github.io/VIPerson/.
St4RTrack: Simultaneous 4D Reconstruction and Tracking in the World
Authors: Haiwen Feng; Junyi Zhang; Qianqian Wang; Yufei Ye; Pengcheng Yu; Michael J. Black; Trevor Darrell; Angjoo Kanazawa
Venue: ICCV 2025 (main)

Abstract: Dynamic 3D reconstruction and point tracking in videos are typically treated as separate tasks, despite their deep connection. We propose St4RTrack, a feed-forward frame- work that simultaneously reconstructs and tracks dynamic video content in a world coordinate frame from RGB in- puts. This is achieved by predicting two appropriately de- fined pointmaps for a pair of frames captured at different moments. Specifically, we predict both pointmaps at the same moment, in the same world, capturing both static and dynamic scene geometry while maintaining 3D correspon- dences. Chaining these predictions through the video sequence with respect to a reference frame naturally computes long-range correspondences, effectively combining 3D reconstruction with 3D tracking. Unlike prior methods that rely heavily on 4D ground truth supervision, we employ a novel adaptation scheme based on a reprojection loss. We establish a new extensive benchmark for world-frame re- construction and tracking, demonstrating the effectiveness and efficiency of our unified, data-driven framework. Our code, model, and benchmark will be released.
PRM: Photometric Stereo based Large Reconstruction Model
Authors: Wenhang Ge; Jiantao Lin; Guibao Shen; Jiawei Feng; Tao Hu; Xinli Xu; Ying-Cong Chen
Venue: ICCV 2025 (main)

Abstract: We propose PRM, a novel photometric stereo based large reconstruction model to reconstruct high-quality meshes with fine-grained details. Previous large reconstruction models typically prepare training images under fixed and simple lighting, offering minimal photometric cues for precise reconstruction. Furthermore, images containing specular surfaces are treated as out-of-distribution samples, resulting in degraded reconstruction quality. To handle these challenges, PRM renders images by varying materials and lighting, which not only improves the local details by providing rich photometric cues but also increases the model's robustness to variations in the appearance of input images. To offer enhanced flexibility, we incorporate a real-time physically-based rendering (PBR) method and mesh rasterization for ground-truth rendering. By using an explicit mesh as 3D representation, PRM ensures the application of differentiable PBR for predicted rendering. This approach models specular color more accurately for images with varying materials and illumination than previous neural rendering methods and supports multiple supervisions for geometry optimization. Extensive experiments demonstrate that PRM significantly outperforms other models.
Exploiting Frequency Dynamics for Enhanced Multimodal Event-based Action Recognition
Authors: Meiqi Cao; Xiangbo Shu; Xin Jiang; Rui Yan; Yazhou Yao; Jinhui Tang
Venue: ICCV 2025 (main)

Abstract: While event cameras excel in capturing microsecond temporal dynamics, they suffer from sparse spatial representations compared to traditional RGB data. Thus, multi-modal event-based action recognition approaches aim to synergize complementary strengths by independently extracting and integrating paired RGB-Event features. However, this paradigm inevitably introduces additional data acquisition costs, while eroding the inherent privacy advantages of event-based sensing. Drawing inspiration from event-to-image reconstruction, texture-enriched visual representation directly reconstructed from asynchronous event streams is a promising solution. In response, we propose an Enhanced Multimodal Perceptual (EMP) framework that hierarchically explores multimodal cues (e.g., edges and textures) from raw event streams through two synergistic innovations spanning representation to feature levels. Specifically, we introduce Cross-Modal Frequency Enhancer (CFE) that leverages complementary frequency characteristics between reconstructed frames and stacked frames to refine event representations. Furthermore, to achieve unified feature encoding across modalities, we develop High-Frequency Guided Selector (HGS) for semantic consistency token selection guided by dynamic edge features while suppressing redundant multimodal information interference adaptively. Extensive experiments on four benchmark datasets demonstrate the superior effectiveness of our proposed framework.
AerialVG: A Challenging Benchmark for Aerial Visual Grounding by Exploring Positional Relations
Authors: Junli Liu; Qizhi Chen; Zhigang Wang; Yiwen Tang; Yiting Zhang; Chi Yan; Dong Wang; Xuelong Li; Bin Zhao
Venue: ICCV 2025 (main)

Abstract: Visual grounding (VG) aims to localize target objects in an image based on natural language descriptions. In this paper, we propose AerialVG, a new task focusing on visual grounding from aerial views. Compared to traditional VG, AerialVG poses new challenges, e.g., appearance-based grounding is insufficient to distinguish among multiple visually similar objects, and positional relations should be emphasized. Besides, existing VG models struggle when applied to aerial imagery, where high-resolution images cause significant difficulties. To address these challenges, we introduce the first AerialVG dataset, consisting of 5K real-world aerial images, 50K manually annotated descriptions, and 103K objects. Particularly, each annotation in AerialVG dataset contains multiple target objects annotated with relative spatial relations, requiring models to perform comprehensive spatial reasoning.Furthermore, we propose an innovative model especially for the AerialVG task, where a Hierarchical Cross-Attention is devised to focus on target regions, and a Relation-Aware Grounding module is designed to infer positional relations. Experimental results validate the effectiveness of our dataset and method, highlighting the importance of spatial reasoning in aerial visual grounding. The code will be released at https://github.com/Ideal-ljl/AerialVG.
DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving
Authors: Xuemeng Yang; Licheng Wen; Tiantian Wei; Yukai Ma; Jianbiao Mei; Xin Li; Wenjie Lei; Daocheng Fu; Pinlong Cai; Min Dou; Liang He; Yong Liu; Botian Shi; Yu Qiao
Venue: ICCV 2025 (main)

Abstract: This paper introduces DriveArena, the first high-fidelity closed-loop simulation system designed for driving agents navigating real-world scenarios. DriveArena comprises two core components: Traffic Manager, a traffic simulator capable of generating realistic traffic flow on any global street map, and World Dreamer, a high-fidelity conditional generative model with infinite auto-regression. DriveArena supports closed-loop simulation using road networks from cities worldwide, enabling the generation of diverse traffic scenarios with varying styles. This powerful synergy empowers any driving agent capable of processing real-world images to navigate in DriveArena 's simulated environment. Furthermore, DriveArena features a flexible, modular architecture, allowing for multiple implementations of its core components and driving agents. Serving as a highly realistic arena for these players, our work provides a valuable platform for developing and evaluating driving agents across diverse and challenging scenarios. DriveArena takes a significant leap forward in leveraging generative models for driving simulation platforms, opening new avenues for closed-loop evaluation of autonomous driving systems.
InvRGB+L: Inverse Rendering of Complex Scenes with Unified Color and LiDAR Reflectance Modeling
Authors: Xiaoxue Chen; Bhargav Chandaka; Chih-Hao Lin; Ya-Qin Zhang; David Forsyth; Hao Zhao; Shenlong Wang
Venue: ICCV 2025 (main)

Abstract: We present InvRGB+L, a novel inverse rendering model that reconstructs large, relightable, and dynamic scenes from a single RGB+LiDAR sequence. Conventional inverse graphics methods rely primarily on RGB observations and use LiDAR mainly for geometric information, often resulting in suboptimal material estimates due to visible light interference. We find that LiDAR's intensity values--captured with active illumination in a different spectral range--offer complementary cues for robust material estimation under variable lighting. Inspired by this, InvRGB+L leverages LiDAR intensity cues to overcome challenges inherent in RGB-centric inverse graphics through two key innovations: (1) a novel physics-based LiDAR shading model and (2) RGB-LiDAR material consistency losses. The model produces novel-view RGB and LiDAR renderings of urban and indoor scenes and supports relighting, night simulations, and dynamic object insertions--achieving results that surpass current state-of-the-art methods in both scene-level urban inverse rendering and LiDAR simulation.
DreamFuse: Adaptive Image Fusion with Diffusion Transformer
Authors: Junjia Huang; Pengxiang Yan; Jiyang Liu; Jie Wu; Zhao Wang; Yitong Wang; Liang Lin; Guanbin Li
Venue: ICCV 2025 (main)

Abstract: Image fusion seeks to seamlessly integrate foreground objects with background scenes, producing realistic and harmonious fused images. Unlike existing methods that directly insert objects into the background, adaptive and interactive fusion remains a challenging yet appealing task. It requires the foreground to adjust or interact with the background context, enabling more coherent integration. To address this, we propose an iterative human-in-the-loop data generation pipeline, which leverages limited initial data with diverse textual prompts to generate fusion datasets across various scenarios and interactions, including placement, holding, wearing, and style transfer. Building on this, we introduce DreamFuse, a novel approach based on the Diffusion Transformer (DiT) model, to generate consistent and harmonious fused images with both foreground and background information. DreamFuse employs a Positional Affine mechanism to inject the size and position of the foreground into the background, enabling effective foreground-background interaction through shared attention. Furthermore, we apply Localized Direct Preference Optimization guided by human feedback to refine DreamFuse, enhancing background consistency and foreground harmony. DreamFuse achieves harmonious fusion while generalizing to text-driven attribute editing of the fused results.Experimental results demonstrate that our method outperforms state-of-the-art approaches across multiple metrics.
DALIP: Distribution Alignment-based Language-Image Pre-Training for Domain-Specific Data
Authors: Junjie Wu; Jiangtao Xie; Zhaolin Zhang; Qilong Wang; Qinghua Hu; Peihua Li; Sen Xu
Venue: ICCV 2025 (main)

Abstract: Recently, Contrastive Language-Image Pre-training (CLIP) has shown promising performance in domain-specific data (e.g., biology), and has attracted increasing research attention. Existing works generally focus on collecting extensive domain-specific data and directly tuning the original CLIP models. Intuitively, such a paradigm takes no full consideration of the characteristics lying in domain-specific data (e.g., fine-grained nature of biological data) and so limits model capability, while mostly losing the original ability of CLIP in the general domain. In this paper, we propose a Distribution Alignment-based Language-Image Pre-Training (DALIP) method for biological data. Specifically, DALIP optimizes CLIP models by matching the similarity between feature distribution of image-text pairs instead of the original [cls] token, which can capture rich yet effective information inherent in image-text pairs as powerful representations, and so better cope with fine-grained nature of biological data. Particularly, our DALIP efficiently approximates feature distribution via its first- and second-order statistics, while presenting a Multi-head Brownian Distance Covariance (MBDC) module to acquire second-order statistics of token features efficiently. Furthermore, we collect a new dataset for plant domain (e.g., specific data in biological domain) comprising 10M plant data with 3M general-domain data (namely PlantMix-13M) according to data mixing laws. Extensive experiments show that DALIP clearly outperforms existing CLIP counterparts in biological domain, while well generalizing to remote sensing and medical imaging domains. Besides, our PlantMix-13M dataset further boosts performance of DALIP in plant domain, while preserving model ability in general domain.