GenAI4ELab/papercli-papers huggingface dataset:
StableCodec: Taming One-Step Diffusion for Extreme Image Compression
Authors: Tianyu Zhang; Xin Luo; Li Li; Dong Liu
Venue: ICCV 2025 (main)

Abstract: Diffusion-based image compression has shown remarkable potential for achieving ultra-low bitrate coding (less than 0.05 bits per pixel) with high realism, by leveraging the generative priors of large pre-trained text-to-image diffusion models. However, current approaches require a large number of denoising steps at the decoder to generate realistic results under extreme bitrate constraints, limiting their application in real-time compression scenarios. Additionally, these methods often sacrifice reconstruction fidelity, as diffusion models typically fail to guarantee pixel-level consistency. To address these challenges, we introduce StableCodec, which enables one-step diffusion for high-fidelity and high-realism extreme image compression with improved coding efficiency. To achieve ultra-low bitrates, we first develop an efficient Deep Compression Latent Codec to transmit a noisy latent representation for a single-step denoising process. We then propose a Dual-Branch Coding Structure, consisting of a pair of auxiliary encoder and decoder, to enhance reconstruction fidelity. Furthermore, we adopt end-to-end optimization with joint bitrate and pixel-level constraints. Extensive experiments on the CLIC 2020, DIV2K, and Kodak datasets demonstrate that StableCodec outperforms existing methods in terms of FID, KID and DISTS by a significant margin, even at bitrates as low as 0.005 bits per pixel, while maintaining strong fidelity. Additionally, StableCodec achieves inference speeds comparable to mainstream transform coding schemes. Code is available at our project repository.
Seal Your Backdoor with Variational Defense
Authors: Ivan Sabolić; Matej Grcić; Siniša Šegvić
Venue: ICCV 2025 (main)

Abstract: We propose VIBE, a model-agnostic framework that trains classifiers resilient to backdoor attacks. The key concept behind our approach is to treat malicious inputs and corrupted labels from the training dataset as observed random variables, while the actual clean labels are latent. VIBE then recovers the corresponding latent clean label posterior through variational inference. The resulting training procedure follows the expectation-maximization (EM) algorithm. The E-step infers the clean pseudolabels by solving an entropy-regularized optimal transport problem, while the M-step updates the classifier parameters via gradient descent. Being modular, VIBE can seamlessly integrate with recent advancements in self-supervised representation learning, which enhance its ability to resist backdoor attacks. We experimentally validate the method effectiveness against contemporary backdoor attacks on standard datasets, a large-scale setup with 1k classes, and a dataset poisoned with multiple attacks. VIBE consistently outperforms previous defenses across all tested scenarios.
From Abyssal Darkness to Blinding Glare: A Benchmark on Extreme Exposure Correction in Real World
Authors: Bo Wang; Huiyuan Fu; Zhiye Huang; Siru Zhang; Xin Wang; Huadong Ma
Venue: ICCV 2025 (main)

Abstract: Exposure correction aims to restore over/under-exposed images to well-exposed ones using a single network. However, existing methods mainly handle non-extreme exposure conditions and struggle with the severe luminance and texture loss caused by extreme exposure. Through a thorough investigation, we find that the lack of high-quality benchmark datasets significantly limits progress in extreme exposure correction.To address this issue, we introduce the first Real-world Extreme Exposure Dataset, REED. By leveraging the burst shooting mode of cameras, we capture image sequences covering a luminance range from extremely dark to extremely bright. To prevent misalignment caused by camera motion and scene changes, we apply cropping and an improved SIFT algorithm to ensure precise alignment.We also propose a novel Context-Guided Luminance-Normalized Iterative Exposure Refinement Network. We employ Contrastive Loss and Luminance Normalizer to disentangle the coupled distribution of over/under-exposed images. In certain cases, luminance alone is insufficient for determining over/under-exposure, so we integrate semantic guidance into the Semantic-aware Exposure Diffusion Model to further enhance luminance and texture restoration. Inspired by the effectiveness of iterative correction in improving color and texture, we introduce the CLIP-Guided Iterative Refinement Strategy. Extensive experiments validate the superiority of our dataset and method. Our dataset is available at https://github.com/juvenoia/REED.
MVTrajecter: Multi-View Pedestrian Tracking with Trajectory Motion Cost and Trajectory Appearance Cost
Authors: Taiga Yamane; Ryo Masumura; Satoshi Suzuki; Shota Orihashi
Venue: ICCV 2025 (main)

Abstract: Multi-View Pedestrian Tracking (MVPT) aims to track pedestrians in the form of a bird's eye view occupancy map from multi-view videos. End-to-end methods that detect and associate pedestrians within one model have shown great progress in MVPT. The motion and appearance information of pedestrians is important for the association, but previous end-to-end MVPT methods rely only on the current and its single adjacent past timestamp, discarding the past trajectories before that. This paper proposes a novel end-to-end MVPT method called Multi-View Trajectory Tracker (MVTrajecter) that utilizes information from multiple timestamps in past trajectories for robust association. MVTrajecter introduces trajectory motion cost and trajectory appearance cost to effectively incorporate motion and appearance information, respectively. These costs calculate which pedestrians at the current and each past timestamp are likely identical based on the information between those timestamps. Even if a current pedestrian could be associated with a false pedestrian at some past timestamp, these costs enable the model to associate that current pedestrian with the correct past trajectory based on other past timestamps. In addition, MVTrajecter effectively captures the relationships between multiple timestamps leveraging the attention mechanism. Extensive experiments demonstrate the effectiveness of each component in MVTrajecter and show that it outperforms the previous state-of-the-art methods.
GeoDiffusion: A Training-Free Framework for Accurate 3D Geometric Conditioning in Image Generation
Authors: Phillip Mueller; Talip Uenlue; Sebastian Schmidt; Marcel Kollovieh; Jiajie Fan; Stephan Günnemann; Lars Mikelsons
Venue: ICCV 2025 (main)

Abstract: Precise geometric control in image generation is essential for fields like engineering & product design and creative industries to control 3D object features accurately in 2D image space. Traditional 3D editing approaches are time-consuming and demand specialized skills, while current image-based generative methods lack accuracy in geometric conditioning. To address these challenges, we propose GeoDiffusion, a training-free framework for accurate and efficient geometric conditioning of 3D features in image generation. GeoDiffusion employs a class-specific 3D object as a geometric prior to define keypoints and parametric correlations in 3D space. We ensure viewpoint consistency through a rendered image of a reference 3D object, followed by style transfer to meet user-defined appearance specifications. At the core of our framework is GeoDrag, improving accuracy and speed of drag-based image editing on geometry guidance tasks and general instructions on DragBench. Our results demonstrate that GeoDiffusion enables precise geometric modifications across various iterative design workflows.
VALLR: Visual ASR Language Model for Lip Reading
Authors: Marshall Thomas; Edward Fish; Richard Bowden
Venue: ICCV 2025 (main)

Abstract: Lip Reading, or Visual Automatic Speech Recognition (V-ASR), is a complex task requiring the interpretation of spoken language exclusively from visual cues, primarily lip movements and facial expressions. This task is especially challenging due to the absence of auditory information and the inherent ambiguity when visually distinguishing phonemes that have overlapping visemes, where different phonemes appear identical on the lips. Current methods typically attempt to predict words or characters directly from these visual cues, but this approach frequently encounters high error rates due to coarticulation effects and viseme ambiguity. We propose a novel two-stage, phoneme-centric framework for Visual Automatic Speech Recognition (V-ASR) that addresses these longstanding challenges. First, our model predicts a compact sequence of phonemes from visual inputs using a Video Transformer with a CTC head, thereby reducing the task complexity and achieving robust speaker invariance. This phoneme output then serves as the input to a fine-tuned Large Language Model (LLM), which reconstructs coherent words and sentences by leveraging broader linguistic context. Unlike existing methods that either predict words directly or rely on large-scale multimodal pre-training, our approach explicitly encodes intermediate linguistic structure while remaining highly data efficient. We demonstrate state-of-the-art performance on two challenging datasets, LRS2 and LRS3, where our method achieves significant reductions in Word Error Rate (WER) achieving a SOTA WER of 18.7 on LRS3 despite using 99.4% less labelled video data than the next best approach
Learning Efficient and Generalizable Human Representation with Human Gaussian Model
Authors: Yifan Liu; Shengjun Zhang; Chensheng Dai; Yang Chen; Hao Liu; Chen Li; Yueqi Duan
Venue: ICCV 2025 (main)

Abstract: Modeling animatable human avatars from videos is a long-standing and challenging problem. While conventional methods require per-instance optimization, recent feed-forward methods have been proposed to generate 3D Gaussians with a learnable network.However, these methods predict independent Gaussians for each frame prediction without fully capturing the relations of Gaussians from different frames, which are hard to be animated by novel poses. To address this, we propose Human Gaussian Graph (HGG) to generate generalizable and animatable Gaussian representations. Specifically, we construct a dual-layer graph to model the relations between predicted Gaussians from multiple frames and SMPL mesh. We design an intra-node operation to aggregate various Gaussian information at different timesteps to benefit from video inputs. Furthermore, we propose an inter-node operation to support message passing between SMPL vertices. In this manner, we leverage the human structure prior to recover generalizable and animatable Gaussian representations.Experimental results on novel view synthesis and novel pose animation demonstrate the efficiency and generalization of our method.
DexH2R: A Benchmark for Dynamic Dexterous Grasping in Human-to-Robot Handover
Authors: Youzhuo Wang; Jiayi Ye; Chuyang Xiao; Yiming Zhong; Heng Tao; Hang Yu; Yumeng Liu; Jingyi Yu; Yuexin Ma
Venue: ICCV 2025 (main)

Abstract: Handover between a human and a dexterous robotic hand is a fundamental yet challenging task in human-robot collaboration. It requires handling dynamic environments and a wide variety of objects and demands robust and adaptive grasping strategies. However, progress in developing effective dynamic dexterous grasping methods is limited by the absence of high-quality, real-world human-to-robot handover datasets. Existing datasets primarily focus on grasping static objects or rely on synthesized handover motions, which differ significantly from real-world robot motion patterns, creating a substantial gap in applicability. In this paper, we introduce DexH2R, a comprehensive real-world dataset for human-to-robot handovers, built on a dexterous robotic hand. Our dataset captures a diverse range of interactive objects, dynamic motion patterns, rich visual sensor data, and detailed annotations. Additionally, to ensure natural and human-like dexterous motions, we utilize teleoperation for data collection, enabling the robot's movements to align with human behaviors and habits, which is a crucial characteristic for intelligent humanoid robots. Furthermore, we propose an effective solution, DynamicGrasp, for human-to-robot handover and evaluate various state-of-the-art approaches, including auto-regressive models and diffusion policy methods, providing a thorough comparison and analysis. We believe our benchmark will drive advancements in human-to-robot handover research by offering a high-quality dataset, effective solutions, and comprehensive evaluation metrics. Project is at https://dexh2r.github.io.
Mobile Video Diffusion
Authors: Haitam Ben Yahia; Denis Korzhenkov; Ioannis Lelekas; Amir Ghodrati; Amirhossein Habibian
Venue: ICCV 2025 (main)

Abstract: Video diffusion models have achieved impressive realism and controllability but are limited by high computational demands, restricting their use on mobile devices. This paper introduces the first mobile-optimized image-to-video diffusion model. Starting from a spatio-temporal UNet from Stable Video Diffusion (SVD), we reduce the computational cost by reducing the frame resolution, incorporating multi-scale temporal representations, and introducing two novel pruning schemas to reduce the number of channels and temporal blocks. Furthermore, we employ adversarial finetuning to reduce the denoising to a single step. Our model, coined as MobileVD, can generate latents for a 14x512x256 px clip in 1.7 seconds on a Xiaomi-14 Pro, with negligible quality loss. Our results are available at https://qualcomm-ai-research.github.io/mobile-video-diffusion
Importance-Based Token Merging for Efficient Image and Video Generation
Authors: Haoyu Wu; Jingyi Xu; Hieu Le; Dimitris Samaras
Venue: ICCV 2025 (main)

Abstract: Token merging can effectively accelerate various vision systems by processing groups of similar tokens only once and sharing the results across them. However, existing token grouping methods are often ad hoc and random, disregarding the actual content of the samples. We show that preserving high-information tokens during merging--those essential for semantic fidelity and structural details--significantly improves sample quality, producing finer details and more coherent, realistic generations. To do so, we propose an importance-based token merging method that prioritizes the most critical tokens in computational resource allocation, leveraging readily available importance scores, such as those from classifier-free guidance in diffusion models. Experiments show that our approach significantly outperforms baseline methods across multiple applications, including text-to-image synthesis, multi-view image generation, and video generation with various model architectures such as Stable Diffusion, Zero123++, AnimateDiff, or PixArt-\alpha.
Semi-ViM: Bidirectional State Space Model for Mitigating Label Imbalance in Semi-Supervised Learning
Authors: Hongyang He; Hongyang Xie; Haochen You; Victor Sanchez
Venue: ICCV 2025 (main)

Abstract: Semi-supervised learning (SSL) is often hindered by learning biases when imbalanced datasets are used for training, which limits its effectiveness in real-world applications. In this paper, we propose Semi-ViM, a novel SSL framework based on Vision Mamba, a bidirectional state space model (SSM) that serves as a superior alternative to Transformer-based architectures for visual representation learning. Semi-ViM effectively deals with label imbalance and improves model stability through two key innovations: LyapEMA, a stability-aware parameter update mechanism inspired by Lyapunov theory, and SSMixup, a novel mixup strategy applied at the hidden state level of bidirectional SSMs. Experimental results on ImageNet-1K and ImageNet-LT demonstrate that Semi-ViM significantly outperforms state-of-the-art SSL models, achieving 85.40% accuracy with only 10% of the labeled data, surpassing Transformer-based methods such as Semi-ViT.
Diffusion Transformer meets Multi-level Wavelet Spectrum for Single Image Super-Resolution
Authors: Peng Du; Hui Li; Han Xu; Paul Barom Jeon; Dongwook Lee; Daehyun Ji; Ran Yang; Feng Zhu
Venue: ICCV 2025 (main)

Abstract: Discrete Wavelet Transform (DWT) has been widely explored to enhance the performance of image super-resolution (SR). Despite some DWT-based methods improving SR by capturing fine-grained frequency signals, most existing approaches neglect the interrelations among multi-scale frequency sub-bands, resulting in inconsistencies and unnatural artifacts in the reconstructed images. To address this challenge, we propose a Diffusion Transformer model based on image Wavelet spectra for SR (DTWSR). DTWSR incorporates the superiority of diffusion models and transformers to capture the interrelations among multi-scale frequency sub-bands, leading to a more consistence and realistic SR image. Specifically, we use a Multi-level Discrete Wavelet Transform (MDWT) to decompose images into wavelet spectra. A pyramid tokenization method is proposed which embeds the spectra into a sequence of tokens for transformer model, facilitating to capture features from both spatial and frequency domain. A dual-decoder is designed elaborately to handle the distinct variances in low-frequency (LF) and high-frequency (HF) sub-bands, without omitting their alignment in image generation. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our method, with high performance on both perception quality and fidelity.
Towards Human-like Virtual Beings: Simulating Human Behavior in 3D Scenes
Authors: Chen Liang; Wenguan Wang; Yi Yang
Venue: ICCV 2025 (main)

Abstract: Building autonomous agents that can replicate human behavior in the realistic 3D world is a key step toward artificial general intelligence. This requires agents to be holistic goal achievers and to naturally adapt to environmental dynamics. In this work, we introduce ACTOR, an agent capable of performing high-level, long-horizon, abstract goals in 3D households, guided by its internal value similar to those of humans. ACTOR operates in a perceive-plan-act cycle, extending the ungrounded, scene-agnostic LLM controller with deliberate goal decomposition and decision-making through actively searching the behavior space, generating activity choices based on a hierarchical prior, and evaluating these choices using customizable value functions to determine the subsequent steps. Furthermore, we introduce BehaviorHub, a large-scale human behavior simulation dataset in scene-aware, complicated tasks. Considering the unaffordable acquisition of human-authored 3D human behavior data, we construct BehaviorHub by exploring the commonsense knowledge of LLMs learned from large corpora, and automatically aligning motion resources with 3D scene for knowledgeable generation. Extensive experiments on our established benchmark demonstrate that the proposed architecture leads to effective behavior planning and simulation. BehaviorHub also proves beneficial for downstream task development.
HiGarment: Cross-modal Harmony Based Diffusion Model for Flat Sketch to Realistic Garment Image
Authors: Junyi Guo; Jingxuan Zhang; Fangyu Wu; Huanda Lu; Qiufeng Wang; Wenmian Yang; Eng Gee Lim; Dongming Lu
Venue: ICCV 2025 (main)

Abstract: Diffusion-based garment synthesis tasks primarily focus on the design phase in the fashion domain, while the garment production process remains largely underexplored. To bridge this gap, we introduce a new task: Flat Sketch to Realistic Garment Image (FS2RG), which generates realistic garment images by integrating flat sketches and textual guidance. FS2RG presents two key challenges: 1) fabric characteristics are solely guided by textual prompts, providing insufficient visual supervision for diffusion-based models, which limits their ability to capture fine-grained fabric details; 2) flat sketches and textual guidance may provide conflicting information, requiring the model to selectively preserve or modify garment attributes while maintaining structural coherence. To tackle this task, we propose HiGarment, a novel framework that comprises two core components: i) a multi-modal semantic enhancement mechanism that enhances fabric representation across textual and visual modalities, and ii) a harmonized cross-attention mechanism that dynamically balances information from flat sketches and text prompts, allowing controllable synthesis by generating either sketch-aligned (image-biased) or text-guided (text-biased) outputs. Furthermore, we collect Multi-modal Detailed Garment, the largest open-source dataset for garment generation. Experimental results and user studies demonstrate the effectiveness of HiGarment in garment synthesis. The code and dataset are available at https://github.com/Maple498/HiGarment.
Robust Unfolding Network for HDR Imaging with Modulo Cameras
Authors: Zhile Chen; Hui Ji
Venue: ICCV 2025 (main)

Abstract: High Dynamic Range (HDR) imaging with modulo cameras involves solving a challenging inverse problem, where degradation occurs due to the modulo operation applied to the target HDR image. Existing methods operate directly in the image domain, overlooking the underlying properties of the modulo operation. Motivated by Itoh's continuity condition in optics, we reformulate modulo HDR reconstruction in image gradient domain, leveraging the inherent properties of modulo-wrapped gradients to simplify the problem. Furthermore, to address possible ambiguities on large image gradients, we introduce an auxiliary variable with a learnable sparsity prior in an optimization formulation to absorb the related residuals. This is implemented within an unfolding network, where sparsity is enforced through a spiking neuron-based module. Experiments show that our method outperforms existing approaches while being among the lightest models of existing works.
Player-Centric Multimodal Prompt Generation for Large Language Model Based Identity-Aware Basketball Video Captioning
Authors: Zeyu Xi; Haoying Sun; Yaofei Wu; Junchi Yan; Haoran Zhang; Lifang Wu; Liang Wang; Changwen Chen
Venue: ICCV 2025 (main)

Abstract: Existing sports video captioning methods often focus on the action yet overlook player identities, limiting their applicability. Although some methods integrate extra information to generate identity-aware descriptions, the player identities are sometimes incorrect because the extra information is independent of the video content. This paper proposes a player-centric multimodal prompt generation network for identity-aware sports video captioning (LLM-IAVC), which focuses on recognizing player identities from a visual perspective. Specifically, an identity-related information extraction module (IRIEM) is designed to extract player-related multimodal embeddings. IRIEM includes a player identification network (PIN) for extracting visual features and player names, and a bidirectional semantic interaction module (BSIM) to link player features with video content for mutual enhancement. Additionally, a visual context learning module (VCLM) is designed to capture the key video context information. Finally, by integrating the outputs of the above modules as the multimodal prompt for the large language model (LLM), it facilitates the generation of descriptions with player identities. To support this work, we construct a new benchmark called NBA-Identity, a large identity-aware basketball video captioning dataset with 9,726 videos covering 9 major event types. The experimental results on NBA-Identity and VC-NBA-2022 demonstrate that our proposed model achieves advanced performance. Code and dataset are publicly available at https://github.com/Zeyu1226-mt/LLM-IAVC.