GenAI4ELab/papercli-papers huggingface dataset:
FreeDance: Towards Harmonic Free-Number Group Dance Generation via a Unified Framework
Authors: Yiwen Zhao; Yang Wang; Liting Wen; Hengyuan Zhang; Xingqun Qi
Venue: ICCV 2025 (main)

Abstract: Generating harmonic and diverse human motions from music signals, especially for a group of dancers, is a practical yet challenging task in virtual avatar creation. Existing methods merely model a fixed number of dancers, lacking the flexibility for arbitrary individuals. To fulfill this goal, we propose a novel unified framework, FreeDance. Considering the plausibility of generating arbitrary dancers while preserving the diverse dynamics of multiple individuals, we build the framework upon collaborative masked token modeling in a 2D discrete space. In particular, we devise a Cross-modality Residual Alignment Module (CRAM) to diversify the movement of each individual and intensify its alignment with music. CRAM captures the spatial motion deformation of each dancer using residual learning and integrates it with rhythmic representation to reinforce the intrinsic connection. Moreover, recognizing the requirement of interactive coordination, we design a Temporal Interaction Module (TIM). Benefiting from masked 2D motion tokens, TIM effectively models the temporal correlation between current individuals w.r.t. neighboring dancers as interaction guidance to foster stronger inter-dancer dependencies. Extensive experiments demonstrate that our approach generates harmonic group dance with any number of individuals, outperforming state-of-the-art methods adapting number-fixed counterparts. Code is available at https://github.com/Tsukasane/FreeDance.
Beyond the Frame: Generating 360deg Panoramic Videos from Perspective Videos
Authors: Rundong Luo; Matthew Wallingford; Ali Fahardi; Noah Snavely; Wei-Chiu Ma
Venue: ICCV 2025 (main)

Abstract: 360deg videos have emerged as a promising medium to represent our dynamic visual world. Compared to the "tunnel vision" of standard cameras, their borderless field of view offers a more complete perspective of our surroundings. While existing video models excel at producing standard videos, their ability to generate full panoramic videos remains elusive. In this paper, we investigate the task of video-to-360deg generation: given a perspective video as input, our goal is to generate a full panoramic video that is consistent with the original video. Unlike conventional video generation tasks, the output's field of view is significantly larger, and the model is required to have a deep understanding of both the spatial layout of the scene and the dynamics of objects to maintain spatio-temporal consistency. To address these challenges, we first leverage the abundant 360deg videos available online and develop a high-quality data filtering pipeline to curate pairwise training data. We then carefully design a series of geometry- and motion-aware operations to facilitate the learning process and improve the quality of 360deg video generation. Experimental results demonstrate that our model can generate realistic and coherent 360deg videos from in-the-wild perspective video. In addition, we showcase its potential applications, including video stabilization, camera viewpoint control, and interactive visual question answering.
Multi-turn Consistent Image Editing
Authors: Zijun Zhou; Yingying Deng; Xiangyu He; Weiming Dong; Fan Tang
Venue: ICCV 2025 (main)

Abstract: Many real-world applications, such as interactive photo retouching, artistic content creation, and product design, require flexible and iterative image editing. However, existing image editing methods primarily focus on achieving the desired modifications in a single step, which often struggles with ambiguous user intent, complex transformations, or the need for progressive refinements. As a result, these methods frequently produce inconsistent outcomes or fail to meet user expectations. To address these challenges, we propose a multi-turn image editing framework that enables users to iteratively refine their edits, progressively achieving more satisfactory results. Our approach leverages flow matching for accurate image inversion and a dual-objective Linear Quadratic Regulators (LQR) for stable sampling, effectively mitigating error accumulation. Additionally, by analyzing the layer-wise roles of transformers, we introduce a adaptive attention highlighting method that enhances editability while preserving multi-turn coherence. Extensive experiments demonstrate that our framework significantly improves edit success rates and visual fidelity compared to existing methods. The code is available at: https://zhouzj-dl.github.io/Multi-turn_Consistent_Image_Editing/.
HUMOTO: A 4D Dataset of Mocap Human Object Interactions
Authors: Jiaxin Lu; Chun-Hao Paul Huang; Uttaran Bhattacharya; Qixing Huang; Yi Zhou
Venue: ICCV 2025 (main)

Abstract: We present Human Motions with Objects (HUMOTO), a high-fidelity dataset of human-object interactions for motion generation, computer vision, and robotics applications. Featuring 735 sequences (7,875 seconds at 30 fps), HUMOTO captures interactions with 63 precisely modeled objects and 72 articulated parts. Our innovations include a scene-driven LLM scripting pipeline creating complete, purposeful tasks with natural progression, and a mocap-and-camera recording setup to effectively handle occlusions. Spanning diverse activities from cooking to outdoor picnics, HUMOTO preserves both physical accuracy and logical task flow. Professional artists rigorously clean and verify each sequence, minimizing foot sliding and object penetrations. We also provide benchmarks compared to other datasets. HUMOTO's comprehensive full-body motion and simultaneous multi-object interactions address key data-capturing challenges and provide opportunities to advance realistic human-object interaction modeling across research domains with practical applications in animation, robotics, and embodied AI systems. Project Page: https://jiaxin-lu.github.io/humoto/.
Learning Visual Hierarchies in Hyperbolic Space for Image Retrieval
Authors: Ziwei Wang; Sameera Ramasinghe; Chenchen Xu; Julien Monteil; Loris Bazzani; Thalaiyasingam Ajanthan
Venue: ICCV 2025 (main)

Abstract: Structuring latent representations in a hierarchical manner enables models to learn patterns at multiple levels of abstraction. However, most prevalent image understanding models focus on visual similarity, and learning visual hierarchies is relatively unexplored. In this work, for the first time, we introduce a learning paradigm that can encode user-defined multi-level complex visual hierarchies in hyperbolic space without requiring explicit hierarchical labels. As a concrete example, first, we define a part-based image hierarchy using object-level annotations within and across images. Then, we introduce an approach to enforce the hierarchy using contrastive loss with pairwise entailment metrics. Finally, we discuss new evaluation metrics to effectively measure hierarchical image retrieval. Encoding these complex relationships ensures that the learned representations capture semantic and structural information that transcends mere visual similarity. Experiments in part-based image retrieval show significant improvements in hierarchical retrieval tasks, demonstrating the capability of our model in capturing visual hierarchies.
AutoPrompt: Automated Red-Teaming of Text-to-Image Models via LLM-Driven Adversarial Prompts
Authors: Yufan Liu; Wanqian Zhang; Huashan Chen; Lin Wang; Xiaojun Jia; Zheng Lin; Weiping Wang
Venue: ICCV 2025 (main)

Abstract: Despite rapid advancements in text-to-image (T2I) models, their safety mechanisms are vulnerable to adversarial prompts, which maliciously generate unsafe images. Current red-teaming methods for proactively assessing such vulnerabilities usually require white-box access to T2I models, and rely on inefficient per-prompt optimization, as well as inevitably generate semantically meaningless prompts easily blocked by filters. In this paper, we propose APT (AutoPrompT), a black-box framework that leverages large language models (LLMs) to automatically generate human-readable adversarial suffixes for benign prompts. We first introduce an alternating optimization-finetuning pipeline between adversarial suffix optimization and fine-tuning the LLM utilizing the optimized suffix. Furthermore, we integrate a dual-evasion strategy in the optimization phase, enabling the bypass of both perplexity-based filter and blacklist word filter: (1) we constrain the LLM generating human-readable prompts through an auxiliary LLM perplexity scoring, which starkly contrasts with prior token-level gibberish, and (2) we also introduce banned-token penalties to suppress the explicit generation of banned-tokens in the blacklist. Extensive experiments demonstrate the excellent red-teaming performance of our human-readable, filter-resistant adversarial prompts, as well as superior zero-shot transferability which enables instant adaptation to unseen prompts and exposes critical vulnerabilities even in commercial APIs (e.g., Leonardo.Ai.).
LLM-assisted Entropy-based Adaptive Distillation for Unsupervised Fine-grained Visual Representation Learning
Authors: Jianfeng Dong; Danfeng Luo; Daizong Liu; Jie Sun; Xiaoye Qu; Xun Yang; Dongsheng Liu; Xun Wang
Venue: ICCV 2025 (main)

Abstract: Unsupervised Fine-grained Visual Represent Learning (FVRL) aims to learn discriminative features to distinguish subtle differences among visually similar categories without using labeled fine-grained data. Existing works, which typically learn representation from target data, often struggle to capture subtle inter-class variations due to the limited prior fine-grained knowledge. To alleviate it, this paper proposes LLM-assisted Entropy-based Adaptive Distillation (LEAD), a novel unsupervised FVRL framework that selectively distills fine-grained knowledge from a powerful teacher model built upon pre-trained models. Specifically, we first harness the powerful reasoning capabilities of Large Language Models (LLMs) to generate contextual knowledge of fine-grained category-aware descriptions, enriching semantic priors in the teacher model. These descriptions are then used to form a prototype-driven fine-grained classifier, which acts as an assistant to generate rich knowledge with a frozen vision-language model. Besides, to achieve effective knowledge transfer, we further introduce an entropy-based adaptive mechanism, which dynamically adjusts the distillation strength based on the information entropy to identify and prioritize valuable knowledge. Extensive experimental results on three fine-grained datasets demonstrate the effectiveness and efficiency of our proposed LEAD for unsupervised FVRL. Our source code is available at https://anonymous.4open.science/r/EAD-FFAB.
Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency
Authors: Shiji Zhao; Ranjie Duan; Fengxiang Wang; Chi Chen; Caixin Kang; Shouwei Ruan; Jialing Tao; YueFeng Chen; Hui Xue; Xingxing Wei
Venue: ICCV 2025 (main)

Abstract: Multimodal Large Language Models (MLLMs) have achieved impressive performance and have been put into practical use in commercial applications, but they still have potential safety mechanism vulnerabilities. Jailbreak attacks are red teaming methods that aim to bypass safety mechanisms and discover MLLMs' potential risks. Existing MLLMs' jailbreak methods often bypass the model's safety mechanism through complex optimization methods or carefully designed image and text prompts. Despite achieving some progress, they have a low attack success rate on commercial closed-source MLLMs. Unlike previous research, we empirically find that there exists a Shuffle Inconsistency between MLLMs' comprehension ability and safety ability for the shuffled harmful instruction. That is, from the perspective of comprehension ability, MLLMs can understand the shuffled harmful text-image instructions well. However, they can be easily bypassed by the shuffled harmful instructions from the perspective of safety ability, leading to harmful responses. Then we innovatively propose a text-image jailbreak attack named SI-Attack. Specifically, to fully utilize the Shuffle Inconsistency and overcome the shuffle randomness, we apply a query-based black-box optimization method to select the most harmful shuffled inputs based on the feedback of the toxic judge model. A series of experiments show that SI-Attack can effectively improve the attack's performance on three benchmarks for both open-source and closed-source MLLMs.
UMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather Conditions
Authors: Siyuan Yao; Rui Zhu; Ziqi Wang; Wenqi Ren; Yanyang Yan; Xiaochun Cao
Venue: ICCV 2025 (main)

Abstract: Visual object tracking has gained promising progress in past decades. Most of the existing approaches focus on learning target representation in well-conditioned daytime data, while for the unconstrained real-world scenarios with adverse weather conditions, e.g. nighttime or foggy environment, the tremendous domain shift leads to significant performance degradation. In this paper, we propose UMDATrack, which is capable of maintaining high-quality target state prediction under various adverse weather conditions within a unified domain adaptation framework. Specifically, we first use a controllable scenario generator to synthesize a small amount of unlabeled videos (less than 2% frames in source daytime datasets) in multiple weather conditions under the guidance of different text prompts. Afterwards, we design a simple yet effective domain-customized adapter (DCA), allowing the target objects' representation to rapidly adapt to various weather conditions without redundant model updating. Furthermore, to enhance the localization consistency between source and target domains, we propose a target-aware confidence alignment module (TCA) following optimal transport theorem. Extensive experiments demonstrate that UMDATrack can surpass existing advanced visual trackers and lead new state-of-the-art performance by a significant margin.
Boosting MLLM Reasoning with Text-Debiased Hint-GRPO
Authors: Qihan Huang; Weilong Dai; Jinlong Liu; Wanggui He; Hao Jiang; Mingli Song; Jingyuan Chen; Chang Yao; Jie Song
Venue: ICCV 2025 (main)

Abstract: MLLM reasoning has drawn widespread research for its excellent problem-solving capability. Current reasoning methods fall into two types: PRM, which supervises the intermediate reasoning steps, and ORM, which supervises the final results. Recently, DeepSeek-R1 has challenged the traditional view that PRM outperforms ORM, which demonstrates strong generalization performance using an ORM method (i.e., GRPO). However, current MLLM's GRPO algorithms still struggle to handle challenging and complex multimodal reasoning tasks (e.g., mathematical reasoning). In this work, we reveal two problems that impede the performance of GRPO on the MLLM: Low data utilization and Text-bias. Low data utilization refers to that GRPO cannot acquire positive rewards to update the MLLM on difficult samples, and text-bias is a phenomenon that the MLLM bypasses image condition and solely relies on text condition for generation after GRPO training. To tackle these problems, this work proposes Hint-GRPO that improves data utilization by adaptively providing hints for samples of varying difficulty, and text-bias calibration that mitigates text-bias by calibrating the token prediction logits with image condition in test-time. Experiment results on three base MLLMs across eleven datasets demonstrate that our proposed methods advance the reasoning capability of original MLLM by a large margin, exhibiting superior performance to existing MLLM reasoning methods. Our code is available at https://github.com/hqhQAQ/Hint-GRPO.
Learning on the Go: A Meta-learning Object Navigation Model
Authors: Xiaorong Qin; Xinhang Song; Sixian Zhang; Xinyao Yu; Xinmiao Zhang; Shuqiang Jiang
Venue: ICCV 2025 (main)

Abstract: Object navigation tasks require an agent to locate a target object using visual observations in unseen environments, where unfamiliar layouts and novel object appearances can hinder navigation. Most existing methods lack the adaptability needed to handle these uncertainties, as their navigation models remain fixed during testing. In this paper, we address this challenge by examining object-conditioned trajectory distribution shifts in navigation caused by changes in environmental dynamics. We propose learning a central conditional distribution as a prior that approximates the specific distributions of diverse environments. To retain environment-specific information during navigation, we allow each environment-specific distribution to approximate this central distribution rather than relying on it directly. To implement this, we introduce a meta-learning mechanism that integrates with traditional navigation methods, offering tailored solutions for various types of navigation approaches. Our approach, Learning on the Go (LOG), enables agents to learn on the go, allowing for flexible, adaptive, real-time learning during navigation. Our theoretical analysis highlights the benefits of learning a central distribution for effective generalization across environments, and empirical results confirm the proposed method's effectiveness, demonstrating superior performance compared to existing approaches.
Visual Interestingness Decoded: How GPT-4o Mirrors Human Interests
Authors: Fitim Abdullahu; Helmut Grabner
Venue: ICCV 2025 (main)

Abstract: Our daily life is highly influenced by what we consume and see. Attracting and holding one's attention -- the definition of (visual) interestingness -- is essential. The rise of Large Multimodal Models (LMMs) trained on large-scale visual and textual data has demonstrated impressive capabilities. We explore these models' potential to understand to what extent the concepts of visual interestingness are captured and examine the alignment between human assessments and GPT-4o's, a leading LMM, predictions through comparative analysis. Our studies reveal partial alignment between humans and GPT-4o. It already captures the concept as best compared to state-of-the-art methods. Hence, this allows for the effective labeling of image pairs according to their (commonly) interestingness, which are used as training data to distill the knowledge into a learning-to-rank model. The insights pave the way for a deeper understanding of human interest.
PriOr-Flow: Enhancing Primitive Panoramic Optical Flow with Orthogonal View
Authors: Longliang Liu; Miaojie Feng; Junda Cheng; Jijun Xiang; Xuan Zhu; Xin Yang
Venue: ICCV 2025 (main)

Abstract: 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.
A Simple yet Mighty Hartley Diffusion Versatilist for Generalizable Dense Vision Tasks
Authors: Qi Bi; Jingjun Yi; Huimin Huang; Hao Zheng; Haolan Zhan; Wei Ji; Yawen Huang; Yuexiang Li; Yefeng Zheng
Venue: ICCV 2025 (main)

Abstract: Diffusion models have demonstrated powerful capability as a versatilist for dense vision tasks, yet the generalization ability to unseen domains remains rarely explored. This paper presents HarDiff, an efficient frequency learning scheme, so as to advance generalizable paradigms for diffusion based dense prediction. It draws inspiration from a fine-grained analysis of Discrete Hartley Transform, where some low-frequency features activate the broader content of an image, while some high-frequency features maintain sufficient details for dense pixels. Consequently, HarDiff consists of two key components. The low-frequency training process extracts structural priors from the source domain, to enhance understanding of task-related content. The high-frequency sampling process utilizes detail-oriented guidance from the unseen target domain, to infer precise dense predictions with target-related details. Extensive empirical evidence shows that HarDiff can be easily plugged into various dense vision tasks, e.g., semantic segmentation, depth estimation and haze removal, yielding improvements over the state-of-the-art methods in twelve public benchmarks.
Separation for Better Integration: Disentangling Edge and Motion in Event-based Deblurring
Authors: Yufei Zhu; Hao Chen; Yongjian Deng; Wei You
Venue: ICCV 2025 (main)

Abstract: Traditional motion deblurring methods struggle to effectively model motion information within the exposure time. Recently, event cameras have attracted significant research interest for its ability to model motion cues over the exposure duration. However, these methods directly fuse event features with image, overlooking the intrinsic heterogeneity of events. In this paper, we identify that the event modality contains two conflicting types of information: edge features and motion cues. Events accumulated over a short exposure period capture sharp edge details but lose motion information, while those accumulated over a long exposure period blur edge details due to motion. To address this issue, we propose a simple yet effective approach to disentangle these two cues from event features and employ an edge-aware sharpening module along with motion-driven scale-adaptive deblurring module to fully leverage both. Specifically, the first module aids in restoring sharp edges by leveraging the clear edge features provided by events, while the second module leverages motion cues to learn diverse blur kernels, adaptively adjusting the receptive field for optimal deblurring. Extensive experiments on synthetic and real-world datasets validate the effectiveness of our approach and yield a substantial improvement over state-of-the-art single-frame methods and surpasses most multi-frame-based methods. Code will be publicly available.
Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video
Authors: Xiao Li; Qi Chen; Xiulian Peng; Kai Yu; Xie Chen; Yan Lu
Venue: ICCV 2025 (main)

Abstract: We propose a novel and general framework to disentangle video data into its dynamic motion and static content components. Our proposed method is a self-supervised pipeline with less assumptions and inductive biases than previous works: it utilizes a transformer-based architecture to jointly generate flexible implicit features for frame-wise motion and clip-wise content, and incorporates a low-bitrate vector quantization as an information bottleneck to promote disentanglement and form a meaningful discrete motion space. The bitrate-controlled latent motion and content are used as conditional inputs to a denoising diffusion model to facilitate self-supervised representation learning. We validate our disentangled representation learning framework on real world talking head videos with motion transfer and auto-regressive motion generation tasks. Furthermore, we also show that our method can generalize to other type of video data, such as pixel sprites of 2D cartoon characters. Our work presents a new perspective on self-supervised learning of disentangled video representations, contributing to the broader field of video analysis and generation.