GenAI4ELab/papercli-papers huggingface dataset:
VideoVAE+: Large Motion Video Autoencoding with Cross-modal Video VAE
Authors: Yazhou Xing; Yang Fei; Yingqing He; Jingye Chen; Jiaxin Xie; Xiaowei Chi; Qifeng Chen
Venue: ICCV 2025 (main)

Abstract: Learning a robust video Variational Autoencoder (VAE) is essential for reducing video redundancy and facilitating efficient video generation. Directly applying image VAEs to individual frames in isolation results in temporal inconsistencies and fails to compress temporal redundancy effectively. Existing works on Video VAEs compress temporal redundancy but struggle to handle videos with large motion effectively. They suffer from issues such as severe image blur and loss of detail in scenarios with large motion. In this paper, we present a powerful video VAE named VideoVAE+ that effectively reconstructs videos with large motion. First, we investigate two architecture choices and propose our simple yet effective architecture with better spatiotemporal joint modeling performance. Second, we propose to leverage the textual information in existing text-to-video datasets and incorporate text guidance during training. The textural guidance is optional during inference. We find that this design enhances the reconstruction quality and preservation of detail. Finally, our models achieve strong performance compared with various baseline approaches in both general videos and large motion videos, demonstrating its effectiveness on the challenging large motion scenarios.
Learning 3D Object Spatial Relationships from Pre-trained 2D Diffusion Models
Authors: Sangwon Baik; Hyeonwoo Kim; Hanbyul Joo
Venue: ICCV 2025 (main)

Abstract: We present a method for learning 3D spatial relationships between object pairs, referred to as object-object spatial relationships (OOR), by leveraging synthetically generated 3D samples from pre-trained 2D diffusion models. We hypothesize that images synthesized by 2D diffusion models inherently capture realistic OOR cues, enabling efficient collection of a 3D dataset to learn OOR for various unbounded object categories. Our approach synthesizes diverse images that capture plausible OOR cues, which we then uplift into 3D samples. Leveraging our diverse collection of 3D samples for the object pairs, we train a score-based OOR diffusion model to learn the distribution of their relative spatial relationships. Additionally, we extend our pairwise OORtomulti-object OOR by enforcing consistency across pairwise relations and preventing object collisions. Extensive experiments demonstrate the robustness of our method across various object-object spatial relationships, along with its applicability to 3D scene arrangement tasks and human motion synthesis using our OOR diffusion model.
Event-guided Unified Framework for Low-light Video Enhancement, Frame Interpolation, and Deblurring
Authors: Taewoo Kim; Kuk-Jin Yoon
Venue: ICCV 2025 (main)

Abstract: In low-light environments, longer exposure times are commonly used to enhance image visibility; however, this inevitably leads to motion blur. Even with a long exposure time, videos captured in low-light environments still suffer from issues such as low visibility, low contrast, and color distortion. Additionally, the long exposure time results in videos with a low frame rate. Therefore, videos captured in low-light exhibit low visibility and motion blur, as well as low frame rates. To overcome these limitations, we propose a novel problem aimed at transforming motion-blurred, low-frame-rate videos with poor visibility in low-light environments into high-frame-rate videos while simultaneously enhancing their visibility. To tackle this challenge, we leverage the unique advantages of event cameras, which capture scene changes asynchronously, providing superior temporal resolution and a wider dynamic range compared to conventional frame-based cameras. These properties make event cameras particularly effective in reducing motion blur, compensating for low frame rates, and enhancing visibility in low-light conditions. To this end, we developed a hybrid camera system that integrates two RGB cameras and an event camera, capturing a dedicated dataset for this task and proposing novel network architectures to effectively address this problem. The project pages are available at https://sites.google.com/view/eledi-iccv.
FE-CLIP: Frequency Enhanced CLIP Model for Zero-Shot Anomaly Detection and Segmentation
Authors: Tao Gong; Qi Chu; Bin Liu; Wei Zhou; Nenghai Yu
Venue: ICCV 2025 (main)

Abstract: Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is challenging since the models need to generalize to anomalies across different domains. Recently, CLIP-based anomaly detection methods, such as WinCLIP and AnomalyCLIP, have demonstrated superior performance in the ZSAD task, due to the strong zero-shot recognition of the CLIP model. However, they overlook the utilization of frequency information of images. In this paper, we find that frequency information could benefit the ZSAD task, since some properties of the anomaly area, such as appearance defects, can also be reflected based on its frequency information. To this end, We propose Frequency Enhanced CLIP (FE-CLIP), taking advantage of two different but complementary frequency-aware clues, (1) Frequency-aware Feature Extraction adapter, and (2) Local Frequency Statistics adapter, in the visual encoder of CLIP, to deeply mine frequency information for the ZSAD task. We apply DCT as the frequency-domain transformation. Through comprehensive experiments, we show that the proposed FE-CLIP has good generalization across different domains and achieves superior zero-shot performance of detecting and segmenting anomalies in 10 datasets of highly diverse class semantics from various defect inspections and medical domains. Besides, the proposed FE-CLIP also achieves superior performance under the few-normal-shot anomaly detection settings.
MotionDiff: Training-free Zero-shot Interactive Motion Editing via Flow-assisted Multi-view Diffusion
Authors: Yikun Ma; Yiqing Li; Jiawei Wu; Xing Luo; Zhi Jin
Venue: ICCV 2025 (main)

Abstract: Generative models have made remarkable advancements and are capable of producing high-quality content. However, performing controllable editing with generative models remains challenging, due to their inherent uncertainty in outputs. This challenge is particularly pronounced in motion editing, which involves the processing of spatial information. While some physics-based generative methods have attempted to implement motion editing, they typically operate on single-view images with simple motions, such as translation and dragging. These methods struggle to handle complex rotation and stretching motions and ensure multi-view consistency, often necessitating resource-intensive retraining. To address these challenges, we propose a training-free zero-shot diffusion method that leverages optical flow for complex multi-view motion editing. Specifically, given a static scene, users can interactively select objects of interest to add motion priors. The proposed Point Kinematic Model then estimates corresponding multi-view optical flows during the Multi-view Flow Estimation Stage. Subsequently, these optical flows are utilized to generate multi-view motion results through decoupled motion representation in the Multi-view Motion Diffusion Stage. Extensive experiments demonstrate that our method outperforms other physics-based generative motion editing methods in achieving high-quality multi-view consistent motion results. Notably, our method does not require retraining, enabling users to conveniently adapt it for various down-stream tasks.
Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximation
Authors: Shuchang Ye; Usman Naseem; Mingyuan Meng; Jinman Kim
Venue: ICCV 2025 (main)

Abstract: Medical language-guided segmentation, integrating textual clinical reports as auxiliary guidance to enhance image segmentation, has demonstrated significant improvements over unimodal approaches. However, its inherent reliance on paired image-text input, which we refer to as "textual reliance", presents two fundamental limitations: 1) many medical segmentation datasets lack paired reports, leaving a substantial portion of image-only data underutilized for training; and 2) inference is limited to retrospective analysis of cases with paired reports, limiting its applicability in most clinical scenarios where segmentation typically precedes reporting. To address these limitations, we propose ProLearn, the first Prototype-driven Learning framework for language-guided segmentation that fundamentally alleviates textual reliance. At its core, we introduce a novel Prototype-driven Semantic Approximation (PSA) module to enable approximation of semantic guidance from textual input. PSA initializes a discrete and compact prototype space by distilling segmentation-relevant semantics from textual reports. Once initialized, it supports a query-and-respond mechanism which approximates semantic guidance for images without textual input, thereby alleviating textual reliance. Extensive experiments on QaTa-COV19, MosMedData+ and Kvasir-SEG demonstrate that ProLearn outperforms state-of-the-art language-guided methods when limited text is available.
CoTMR: Chain-of-Thought Multi-Scale Reasoning for Training-Free Zero-Shot Composed Image Retrieval
Authors: Zelong Sun; Dong Jing; Zhiwu Lu
Venue: ICCV 2025 (main)

Abstract: Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images by integrating information from a composed query (reference image and modification text) without training samples. Existing methods primarily combine caption models and Large Language Models (LLMs) to generate target captions from composed queries but face various issues such as incompatibility, visual information loss, and insufficient reasoning. In this work, we propose CoTMR, a training-free framework with novel Chain-of-thought (CoT) and Multi-scale Reasoning. Instead of relying on caption models for modality transformation, CoTMR directly employs the Large Vision-Language Model (LVLM) to achieve unified understanding and reasoning of composed queries. To enhance reasoning reliability, we devise CIRCoT, which guides the LVLM to perform step-by-step reasoning by following predefined subtasks. Additionally, while most existing approaches focus solely on global-level reasoning, CoTMR introduces fine-grained predictions about the presence or absence of key elements at the object scale for more comprehensive reasoning. Furthermore, we design a Multi-Grained Scoring (MGS) mechanism, which integrates CLIP similarity scores of the above reasoning outputs with candidate images to realize precise retrieval. Extensive experiments demonstrate that our CoTMR not only drastically outperforms previous methods across four prominent benchmarks but also offers appealing interpretability.
PASTA: Part-Aware Sketch-to-3D Shape Generation with Text-Aligned Prior
Authors: Seunggwan Lee; Hwanhee Jung; Byoungsoo Koh; Qixing Huang; Sang Ho Yoon; Sangpil Kim
Venue: ICCV 2025 (main)

Abstract: A fundamental challenge in conditional 3D shape generation is to minimize the information loss and maximize the intention of user input. Existing approaches have predominantly focused on two types of isolated conditional signals, i.e., user sketches and text descriptions, each of which does not offer flexible control of the generated shape. In this paper, we introduce PASTA, the flexible approach that seamlessly integrates a user sketch and a text description for 3D shape generation. The key idea is to use text embeddings from a vision-language model to enrich the semantic representation of sketches. Specifically, these text-derived priors specify the part components of the object, compensating for missing visual cues from ambiguous sketches. In addition, we introduce ISG-Net which employs two types of graph convolutional networks: IndivGCN, which processes fine-grained details, and PartGCN, which aggregates these details into parts and refines the structure of objects. Extensive experiments demonstrate that PASTA outperforms existing methods in part-level editing and achieves state-of-the-art results in sketch-to-3D shape generation.
BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models
Authors: Jianting Tang; Yubo Wang; Haoyu Cao; Linli Xu
Venue: ICCV 2025 (main)

Abstract: Mainstream Multimodal Large Language Models (MLLMs) achieve visual understanding by using a vision projector to bridge well-pretrained vision encoders and large language models (LLMs). The inherent gap between visual and textual modalities makes the embeddings from the vision projector critical for visual comprehension. However, current alignment approaches treat visual embeddings as contextual cues and merely apply auto-regressive supervision to textual outputs, neglecting the necessity of introducing equivalent direct visual supervision, which hinders the potential finer alignment of visual embeddings. In this paper, based on our analysis of the refinement process of visual embeddings in the LLM's shallow layers, we propose BASIC, a method that utilizes refined visual embeddings within the LLM as supervision to directly guide the projector in generating initial visual embeddings. Specifically, the guidance is conducted from two perspectives: (i) optimizing embedding directions by reducing angles between initial and supervisory embeddings in semantic space; (ii) improving semantic matching by minimizing disparities between the logit distributions of both visual embeddings. Without additional supervisory models or artificial annotations, BASIC significantly improves the performance of MLLMs across a wide range of benchmarks, demonstrating the effectiveness of our introduced direct visual supervision.
Long-term Traffic Simulation with Interleaved Autoregressive Motion and Scenario Generation
Authors: Xiuyu Yang; Shuhan Tan; Philipp Krähenbühl
Venue: ICCV 2025 (main)

Abstract: An ideal traffic simulator replicates the realistic long-term point-to-point trip that a self-driving system experiences during deployment. Prior models and benchmarks focus on closed-loop motion simulation for initial agents in a scene. This is problematic for long-term simulation. Agents enter and exit the scene as the ego vehicle enters new regions. We propose InfGen, a unified next-token prediction model that performs interleaved closed-loop motion simulation and scene generation. InfGen automatically switches between closed-loop motion simulation and scene generation mode. It enables stable long-term rollout simulation. InfGen performs at the state-of-the-art in short-term (9s) traffic simulation, and significantly outperforms all other methods in long-term (30s) simulation. The code and model of InfGen will be released at https://orangesodahub.github.io/InfGen.
VMBench: A Benchmark for Perception-Aligned Video Motion Generation
Authors: Xinran Ling; Chen Zhu; Meiqi Wu; Hangyu Li; Xiaokun Feng; Cundian Yang; Aiming Hao; Jiashu Zhu; Jiahong Wu; Xiangxiang Chu
Venue: ICCV 2025 (main)

Abstract: Video generation has advanced rapidly, improving evaluation methods, yet assessing video's motion remains a major challenge. Specifically, there are two key issues: 1) current motion metrics do not fully align with human perceptions; 2) the existing motion prompts are limited. Based these findings, we introduce VMBench--a comprehensive Video Motion Benchmark that has perception-aligned motion metrics and features the most diverse types of motion. VMBench has several appealing properties: (1) Perception-Driven Motion Evaluation Metrics, we identify five dimensions based on human perception in motion video assessment and develop fine-grained evaluation metrics, providing deeper insights into models' strengths and weaknesses in motion quality. (2) Meta-Guided Motion Prompt Generation, a structured method that extracts meta-information, generates diverse motion prompts with LLMs, and refines them through human-AI validation, resulting in a multi-level prompt library covering six key dynamic scene dimensions. (3) Human-Aligned Validation Mechanism, we provide human preference annotations to validate our benchmarks, with our metrics achieving an average 35.3% improvement in Spearman's correlation over baseline methods. This is the first time that the quality of motion in videos has been evaluated from the perspective of human perception alignment. Additionally, we release VMBench at https://github.com/AMAP-ML/VMBench, setting a new standard for evaluating and advancing motion generation models.
Physics Context Builders: A Modular Framework for Physical Reasoning in Vision-Language Models
Authors: Vahid Balazadeh; Mohammadmehdi Ataei; Hyunmin Cheong; Amir Hosein Khasahmadi; Rahul G. Krishnan
Venue: ICCV 2025 (main)

Abstract: Physical reasoning remains a significant challenge for Vision-Language Models (VLMs). This limitation arises from an inability to translate learned knowledge into predictions about physical behavior. Although continual fine-tuning can mitigate this issue, it is expensive for large models and impractical to perform repeatedly for every task. This necessitates the creation of modular and scalable ways to teach VLMs about physical reasoning. To that end, we introduce Physics Context Builders (PCBs), a modular framework where specialized smaller VLMs are fine-tuned to generate detailed physical scene descriptions. These can be used as physical contexts to enhance the reasoning capabilities of larger VLMs. PCBs enable the separation of visual perception from reasoning, allowing us to analyze their relative contributions to physical understanding. We perform experiments on CLEVRER and on Falling Tower, a stability detection dataset with both simulated and real-world scenes, to demonstrate that PCBs provide substantial performance improvements, increasing average accuracy by up to 13.8% on complex physical reasoning tasks. Notably, PCBs also show strong Sim2Real transfer, successfully generalizing from simulated training data to real-world scenes.
RhythmGuassian: Repurposing Generalizable Gaussian Model For Remote Physiological Measurement
Authors: Hao Lu; Yuting Zhang; Jiaqi Tang; Bowen Fu; Wenhang Ge; Wei Wei; Kaishun Wu; Yingcong Chen
Venue: ICCV 2025 (main)

Abstract: Remote Photoplethysmography (rPPG) enables non-contact extraction of physiological signals, providing significant advantages in medical monitoring, emotion recognition, and face anti-spoofing. However, the extraction of reliable rPPG signals is hindered by motion variations in real-world environments, leading to entanglement issue. To address the challenge, we employ the Generalizable Gaussian Model (GGM) to disentangle geometry and chroma components with 4D Gaussian representations. Employing the GGM for robust rPPG estimation is non-trivial. Firstly, there are no camera parameters in the dataset, resulting in the inability to render video from 4D Gaussian. The "4D virtual camera" is proposed to construct extra Gaussian parameters to describe view and motion changes, giving the ability to render video with the fixed virtual camera parameters. Further, the chroma component is still not explicitly decoupled in 4D Gaussian representation. Explicit motion modeling (EMM) is designed to decouple the motion variation in an unsupervised manner. Explicit chroma modeling (ECM) is tailored to decouple specular, physiological, and noise signals, respectively. To validate our approach, we expand existing rPPG datasets to include various motion and illumination interference scenarios, demonstrating the effectiveness of our method in real-world settings. The code is available at https://github.com/LuPaoPao/RhythmGuassian.
Bridging the Sky and Ground: Towards View-Invariant Feature Learning for Aerial-Ground Person Re-Identification
Authors: Wajahat Khalid; Bin Liu; Xulin Li; Muhammad Waqas; Muhammad Sher Afgan
Venue: ICCV 2025 (main)

Abstract: Aerial-Ground Person Re-Identification (AG-ReID) is a practical yet challenging task that involves cross-platform matching between aerial and ground cameras. Existing person Re-Identification (Re-ID) methods are primarily designed for homogeneous camera settings, such as ground-to-ground or aerial-to-aerial matching. Therefore, these conventional Re-ID approaches underperform due to the significant viewpoint discrepancies introduced by cross-platform cameras in the AG-ReID task. To address this limitation, we propose a novel and efficient approach, termed View-Invariant Feature Learning for Aerial-Ground Person Re-Identification (VIF-AGReID), which explores view-invariant features without leveraging any auxiliary information. Our approach introduces two key components: (1) Patch-Level RotateMix (PLRM), an augmentation strategy that enhances rotational diversity within local regions of training samples, enabling the model to capture fine-grained view-invariant features, and (2) View-Invariant Angular Loss (VIAL), which mitigates the impact of perspective variations by imposing angular constraints that exponentially penalize large angular deviations, optimizing the similarity of positive pairs while enhancing dissimilarity for hard negatives. These components interact synergistically to drive view-invariant feature learning, enhancing robustness across diverse viewpoints. Extensive experiments on the CARGO, AG-ReIDv1, and AG-ReIDv2 benchmarks demonstrate the effectiveness of our method in addressing the AG-ReID task.
Prototype Guided Backdoor Defense via Activation Space Manipulation
Authors: Venkat Adithya Amula; Sunayana Samavedam; Saurabh Saini; Avani Gupta; P J Narayanan
Venue: ICCV 2025 (main)

Abstract: Deep learning models are susceptible to   backdoor attacks  involving malicious perturbation of some training data with a   trigger  to force misclassification to a target class. Various triggers have been used including semantic triggers that are easily realizable. We present Prototype Guided Backdoor Defense (PGBD), a robust post-hoc defense that scales across different trigger types, including previously unsolved semantic triggers. PPGBD exploits displacements in the geometric spaces of activations to penalize movements towards the trigger. This is done using a novel   sanitization loss  of a post-hoc fine-tuning step. This approach scales to all types of attacks and triggers, and achieves better performance across settings. We also present the first defense against semantic attacks on a new celebrity face images dataset. Activation spaces can provide rich clues to enhance DL models in different ways.
EvolvingGrasp: Evolutionary Grasp Generation via Efficient Preference Alignment
Authors: Yufei Zhu; Yiming Zhong; Zemin Yang; Peishan Cong; Jingyi Yu; Xinge Zhu; Yuexin Ma
Venue: ICCV 2025 (main)

Abstract: Dexterous robotic hands often struggle to generalize effectively in complex environments due to models trained on low-diversity data. However, the real world presents an inherently unbounded range of scenarios. A natural solution is to enable robots learning from experience in complex environments--an approach akin to evolution, where systems improve through learning from both failures and successes. Motivated by this, we propose EvolvingGrasp, an evolutionary grasp generation method that continuously enhances grasping performance through efficient preference alignment. Specifically, we introduce Handpose-wise Preference Optimization (HPO), which allows the model to continuously align with preferences from both positive and negative feedback while progressively refining its grasping strategies. To further enhance efficiency and reliability during online adjustments, we incorporate a Physics-aware Consistency Model within HPO, which accelerates inference, reduces the number of timesteps needed for preference fine-tuning, and ensures physical plausibility throughout the process. Our results validate that EvolvingGrasp enables evolutionary grasp generation, ensuring robust, physically feasible, and preference-aligned grasping in both simulation and real scenarios.