GenAI4ELab/papercli-papers huggingface dataset:
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather
Authors: Yuran Wang; Yingping Liang; Yutao Hu; Ying Fu
Venue: ICCV 2025 (main)

Abstract: Learning-based stereo matching models struggle in adverse weather conditions due to the scarcity of corresponding training data and the challenges in extracting discriminative features from degraded images. These limitations significantly hinder zero-shot generalization to out-of-distribution weather conditions. In this paper, we propose **RobuSTereo** a novel framework that enhances the zero-shot generalization of stereo matching models under adverse weather by addressing both data scarcity and feature extraction challenges. First, we introduce a diffusion-based simulation pipeline with a stereo consistency module, which generates high-quality stereo data tailored for adverse conditions. By training stereo matching models on our synthetic datasets, we reduce the domain gap between clean and degraded images, significantly improving the models' robustness to unseen weather conditions. The stereo consistency module ensures structural alignment across synthesized image pairs, preserving geometric integrity and enhancing depth estimation accuracy. Second, we design a robust feature encoder that combines a specialized ConvNet with a denoising transformer to extract stable and reliable features from degraded images. The ConvNet captures fine-grained local structures, while the denoising transformer refines global representations, effectively mitigating the impact of noise, low visibility, and weather-induced distortions. This enables more accurate disparity estimation even under challenging visual conditions. Extensive experiments demonstrate that **RobuSTereo** significantly improves the robustness and generalization of stereo matching models across diverse adverse weather scenarios.
SemGes: Semantics-aware Co-Speech Gesture Generation using Semantic Coherence and Relevance Learning
Authors: Lanmiao Liu; Esam Ghaleb; Asli Ozyurek; Zerrin Yumak
Venue: ICCV 2025 (main)

Abstract: Creating a virtual avatar with semantically coherent gestures that are aligned with speech is a challenging task. Existing gesture generation research mainly focused on generating rhythmic beat gestures, neglecting the semantic context of the gestures. In this paper, we propose a novel approach for semantic grounding in co-speech gesture generation that integrates semantic information at both fine-grained and global levels. Our approach starts with learning the motion prior through a vector-quantized variational autoencoder. Built on this model, a second-stage module is applied to automatically generate gestures from speech, text-based semantics and speaker identity that ensures consistency between the semantic relevance of generated gestures and co-occurring speech semantics through semantic coherence and relevance modules. Experimental results demonstrate that our approach enhances the realism and coherence of semantic gestures. Extensive experiments and user studies show that our method outperforms state-of-the-art approaches across two benchmarks in co-speech gesture generation in both objective and subjective metrics. The qualitative results of our model can be viewed at https://semgesture.github.io. Our code, dataset and pre-trained models will be shared upon acceptance.
From Sharp to Blur: Unsupervised Domain Adaptation for 2D Human Pose Estimation Under Extreme Motion Blur Using Event Cameras
Authors: Youngho Kim; Hoonhee Cho; Kuk-Jin Yoon
Venue: ICCV 2025 (main)

Abstract: Human pose estimation is critical for applications such as rehabilitation, sports analytics, and AR/VR systems. However, rapid motion and low-light conditions often introduce motion blur, significantly degrading pose estimation due to the domain gap between sharp and blurred images. Most datasets assume stable conditions, making models trained on sharp images struggle in blurred environments. To address this, we introduce a novel domain adaptation approach that leverages event cameras, which capture high temporal resolution motion data and are inherently robust to motion blur. Using event-based augmentation, we generate motion-aware blurred images, effectively bridging the domain gap between sharp and blurred domains without requiring paired annotations. Additionally, we develop a student-teacher framework that iteratively refines pseudo-labels, leveraging mutual uncertainty masking to eliminate incorrect labels and enable more effective learning. Experimental results demonstrate that our approach outperforms conventional domain-adaptive human pose estimation methods, achieving robust pose estimation under motion blur without requiring annotations in the target domain. Our findings highlight the potential of event cameras as a scalable and effective solution for domain adaptation in real-world motion blur environments. Our project codes are available at https://github.com/kmax2001/EvSharp2Blur.
HyPiDecoder: Hybrid Pixel Decoder for Efficient Segmentation and Detection
Authors: Fengzhe Zhou; Humphrey Shi
Venue: ICCV 2025 (main)

Abstract: Recently, Mask2Former has achieved significant success as a universal image segmentation framework, with its Multi-Scale Deformable Attention (MSDeformAttn) Pixel Decoder becoming a widely adopted component in current segmentation models. However, the inefficiency of MSDeformAttn has become a performance bottleneck for segmenters. To address this, we propose the Hyper Pixel Decoder (HyPiDecoder), an improved Pixel Decoder design that replaces parts of the MSDeformAttn layers with convolution-based FPN layers, introducing explicit locality information and significantly boosting inference speed. Experimental results show that HyPiDecoder can be applied to both universal segmentation models and unified segmentation and detection models, achieving improvements in both speed and accuracy across object detection, semantic, instance, and panoptic segmentation tasks. The Mask DINO model integrated with HyPiDecoder achieves a new SOTA of 58.8 PQ on COCO panoptic segmentation with SwinL-scale backbone and no extra training data, with a 127% increase in inference speed compared to the original model. Code is available at \href https://github.com/HyPiDecoder/HybridPixelDecoder  https://github.com/HyPiDecoder/HybridPixelDecoder .
DDB: Diffusion Driven Balancing to Address Spurious Correlations
Authors: Aryan Yazdan Parast; Basim Azam; Naveed Akhtar
Venue: ICCV 2025 (main)

Abstract: Deep neural networks trained with Empirical Risk Minimization (ERM) perform well when both training and test data come from the same domain, but they often fail to generalize to out-of-distribution samples. In image classification, these models may rely on spurious correlations that often exist between labels and irrelevant features of images, making predictions unreliable when those features do not exist. We propose a Diffusion Driven Balancing (DDB) technique to generate training samples with text-to-image diffusion models for addressing the spurious correlation problem. First, we compute the best describing token for the visual features pertaining to the causal components of samples by a textual inversion mechanism. Then, leveraging a language segmentation method and a diffusion model, we generate new samples by combining the causal component with the elements from other classes. We also meticulously prune the generated samples based on the prediction probabilities and attribution scores of the ERM model to ensure their correct composition for our objective. Finally, we retrain the ERM model on our augmented dataset. This process reduces the model's reliance on spurious correlations by learning from carefully crafted samples in which this correlation does not exist. Our experiments show that across different benchmarks, our technique achieves better worst-group accuracy than the existing state-of-the-art methods. Our code is available at https://github.com/ArianYp/DDB.
Feature Purification Matters: Suppressing Outlier Propagation for Training-Free Open-Vocabulary Semantic Segmentation
Authors: Shuo Jin; Siyue Yu; Bingfeng Zhang; Mingjie Sun; Yi Dong; Jimin Xiao
Venue: ICCV 2025 (main)

Abstract: Training-free open-vocabulary semantic segmentation has advanced with vision-language models like CLIP, which exhibit strong zero-shot abilities. However, CLIP's attention mechanism often wrongly emphasises specific image tokens, namely outliers, which results in irrelevant over-activation. Existing approaches struggle with these outliers that arise in intermediate layers and propagate through the model, ultimately degrading spatial perception. In this paper, we propose a Self-adaptive Feature Purifier framework (SFP) to suppress propagated outliers and enhance semantic representations for open-vocabulary semantic segmentation. Specifically, based on an in-depth analysis of attention responses between image and class tokens, we design a self-adaptive outlier mitigator to detect and mitigate outliers at each layer for propagated feature purification. In addition, we introduce a semantic-aware attention enhancer to augment attention intensity in semantically relevant regions, which strengthens the purified feature to focus on objects. Further, we introduce a hierarchical attention integrator to aggregate multi-layer attention maps to refine spatially coherent feature representations for final segmentation. Our proposed SFP enables robust outlier suppression and object-centric feature representation, leading to a more precise segmentation. Extensive experiments show that our method achieves state-of-the-art performance and surpasses existing methods by an average of 4.6% mIoU on eight segmentation benchmarks. The code will be released.
Motal: Unsupervised 3D Object Detection by Modality and Task-specific Knowledge Transfer
Authors: Hai Wu; Hongwei Lin; Xusheng Guo; Xin Li; Mingming Wang; Cheng Wang; Chenglu Wen
Venue: ICCV 2025 (main)

Abstract: The performance of unsupervised 3D object classification and bounding box regression relies heavily on the quality of initial pseudo-labels. Traditionally, the labels of classification and regression are represented by a single set of candidate boxes generated by motion or geometry heuristics. However, due to the similarity of many objects to the background in shape or lack of motion, the labels often fail to achieve high accuracy in two tasks simultaneously. Using these labels to directly train the network results in decreased detection performance. To address this challenge, we introduce Motal that performs unsupervised 3D object detection by Modality and task-specific knowledge transfer. Motal decouples the pseudo-labels into two sets of candidates, from which Motal discovers classification knowledge by motion and image appearance prior, and discovers box regression knowledge by geometry prior, respectively. Motal finally transfers all knowledge to a single student network by a TMT (Task-specific Masked Training) scheme, attaining high performance in both classification and regression. Motal can greatly enhance various unsupervised methods by about 2x mAP. For example, on the WOD test set, Motal improves the state-of-the-art CPD by 21.56% mAP L1 (from 20.54% to 42.10%) and 19.90% mAP L2 (from 18.18% to 38.08%). These achievements highlight the significance of our method.
MMAIF: Multi-task and Multi-degradation All-in-One for Image Fusion with Language Guidance
Authors: Zihan Cao; Yu Zhong; Ziqi Wang; Liang-Jian Deng
Venue: ICCV 2025 (main)

Abstract: Image fusion, a fundamental low-level vision task, aims to integrate multiple image sequences into a single output while preserving as much information as possible from the input. However, existing methods face several significant limitations: 1) requiring task- or dataset-specific models; 2) neglecting real-world image degradations (e.g., noise), which causes failure when processing degraded inputs; 3) operating in pixel space, where attention mechanisms are computationally expensive; and 4) lacking user interaction capabilities.To address these challenges, we propose a unified framework for multi-task, multi-degradation, and language-guided image fusion. Our framework includes two key components: 1) a practical degradation pipeline that simulates real-world image degradations and generates interactive prompts to guide the model; 2) an all-in-one Diffusion Transformer (DiT) operating in latent space, which fuses a clean image conditioned on both the degraded inputs and the generated prompts. Furthermore, we introduce principled modifications to the original DiT architecture to better suit the fusion task. Based on this framework, we develop two versions of the model: Regression-based and Flow Matching-based variants.Extensive qualitative and quantitative experiments demonstrate that our approach effectively addresses the aforementioned limitations and outperforms previous restoration+fusion and all-in-one pipelines.
SparseLaneSTP: Leveraging Spatio-Temporal Priors with Sparse Transformers for 3D Lane Detection
Authors: Maximilian Pittner; Joel Janai; Mario Faigle; Alexandru Paul Condurache
Venue: ICCV 2025 (main)

Abstract: 3D lane detection has emerged as a critical challenge in autonomous driving, encompassing identification and localization of lane markings and the 3D road surface. Conventional 3D methods detect lanes from dense birds-eye-viewed (BEV) features, though erroneous transformations often result in a poor feature representation misaligned with the true 3D road surface. While recent sparse lane detectors have surpassed dense BEV approaches, they completely disregard valuable lane-specific priors. Furthermore, existing methods fail to utilize historic lane observations, which yield the potential to resolve ambiguities in situations of poor visibility. To address these challenges, we present SparseLaneSTP, a novel method that integrates both geometric properties of the lane structure and temporal information into a sparse lane transformer. It introduces a new lane-specific spatio-temporal attention mechanism, a continuous lane representation tailored for sparse architectures as well as temporal regularization. Identifying weaknesses of existing 3D lane datasets, we also introduce a precise and consistent 3D lane dataset using a simple yet effective auto-labeling strategy. Our experimental section proves the benefits of our contributions and demonstrates state-of-the-art performance across all detection and error metrics on existing 3D lane detection benchmarks as well as on our novel dataset.
MonSTeR: a Unified Model for Motion, Scene, Text Retrieval
Authors: Luca Collorone; Matteo Gioia; Massimiliano Pappa; Paolo Leoni; Giovanni Ficarra; Or Litany; Indro Spinelli; Fabio Galasso
Venue: ICCV 2025 (main)

Abstract: Intention drives human movement in complex environments, but such movement can only happen if the surrounding context supports it. Despite the intuitive nature of this mechanism, existing research has not yet provided tools to evaluate the alignment between skeletal movement (motion), intention (text), and the surrounding context (scene). In this work, we introduce MonSTeR, the first MOtioN-Scene-TExt Retrieval model. Inspired by the modeling of higher-order relations, MonSTeR constructs a unified latent space by leveraging unimodal and cross-modal representations. This allows MonSTeR to capture the intricate dependencies between modalities, enabling flexible but robust retrieval across various tasks. Our results show that MonSTeR outperforms trimodal models that rely solely on unimodal representations. Furthermore, we validate the alignment of our retrieval scores with human preferences through a dedicated user study. We demonstrate the versatility of MonSTeR's latent space on zero-shot in-Scene Object Placement and Motion Captioning. Code and pre-trained models are available at github.com/colloroneluca/MonSTeR.
Semantic Causality-Aware Vision-Based 3D Occupancy Prediction
Authors: Dubing Chen; Huan Zheng; Yucheng Zhou; Xianfei Li; Wenlong Liao; Tao He; Pai Peng; Jianbing Shen
Venue: ICCV 2025 (main)

Abstract: Vision-based 3D semantic occupancy prediction is a critical task in 3D vision that integrates volumetric 3D reconstruction with semantic understanding. Existing methods, however, often rely on modular pipelines. These modules are typically optimized independently or use pre-configured inputs, leading to cascading errors. In this paper, we address this limitation by designing a novel causal loss that enables holistic, end-to-end supervision of the modular 2D-to-3D transformation pipeline. Grounded in the principle of 2D-to-3D semantic causality, this loss regulates the gradient flow from 3D voxel representations back to the 2D features. Consequently, it renders the entire pipeline differentiable, unifying the learning process and making previously non-trainable components fully learnable. Building on this principle, we propose the Semantic Causality-Aware 2D-to-3D Transformation, which comprises three components guided by our causal loss: Channel-Grouped Lifting for adaptive semantic mapping, Learnable Camera Offsets for enhanced robustness against camera perturbations, and Normalized Convolution for effective feature propagation. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the Occ3D benchmark, demonstrating significant robustness to camera perturbations and improved 2D-to-3D semantic consistency.
OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentation
Authors: Ding Zhong; Xu Zheng; Chenfei Liao; Yuanhuiyi Lyu; Jialei Chen; Shengyang Wu; Linfeng Zhang; Xuming Hu
Venue: ICCV 2025 (main)

Abstract: Segment Anything Model 2 (SAM2) has emerged as a strong base model in various pinhole imaging segmentation tasks. However, when applying it to 360^\circ domain, the significant field-of-view (FoV) gap between pinhole (70^\circ x70^\circ) and panoramic images (180^\circ x360^\circ) poses unique challenges. Two major concerns for this application includes 1) inevitable distortion and object deformation brought by the large FoV disparity between domains; 2) the lack of pixel-level semantic understanding that the original SAM2 cannot provide. To address these issues, we propose a novel OmniSAM framework, which makes the first attempt to apply SAM2 for panoramic semantic segmentation. Specifically, to bridge the first gap, OmniSAM first divides the panorama into sequences of patches. These patches are then treated as image sequences in similar manners as in video segmentation tasks. We then leverage the SAM2's memory mechanism to extract cross-patch correspondences that embeds the cross-FoV dependencies, improving feature continuity and the prediction consistency along mask boundaries. For the second gap, OmniSAM fine-tunes the pretrained image encoder and reutilize the mask decoder for semantic prediction. An FoV-based prototypical adaptation module with dynamic pseudo label update mechanism is also introduced to facilitate the alignment of memory and backbone features, thereby improving model generalization ability across different sizes of source models. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods by large margins, e.g., 79.06% (10.22%\uparrow) on SPin8-to-SPan8, 62.46% (6.58%\uparrow) on CS13-to-DP13.
Event-Driven Storytelling with Multiple Lifelike Humans in a 3D Scene
Authors: Donggeun Lim; Jinseok Bae; Inwoo Hwang; Seungmin Lee; Hwanhee Lee; Young Min Kim
Venue: ICCV 2025 (main)

Abstract: In this work, we propose a framework that creates a lively virtual dynamic scene with contextual motions of multiple humans. Generating multi-human contextual motion requires holistic reasoning over dynamic relationships among human-human and human-scene interactions. We adapt the power of a large language model (LLM) to digest the contextual complexity within textual input and convert the task into tangible subproblems such that we can generate multi-agent behavior beyond the scale that was not considered before. Specifically, our event generator formulates the temporal progression of a dynamic scene into a sequence of small events. Each event calls for a well-defined motion involving relevant characters and objects. Next, we synthesize the motions of characters at positions sampled based on spatial guidance. We employ a high-level module to deliver scalable yet comprehensive context, translating events into relative descriptions that enable the retrieval of precise coordinates. As the first to address this problem at scale and with diversity, we offer a benchmark to assess diverse aspects of contextual reasoning. Benchmark results and user studies show that our framework effectively captures scene context with high scalability.
Probabilistic Inertial Poser (ProbIP): Uncertainty-aware Human Motion Modeling from Sparse Inertial Sensors
Authors: Min Kim; Younho Jeon; Sungho Jo
Venue: ICCV 2025 (main)

Abstract: Wearable Inertial Measurement Units (IMUs) allow non-intrusive motion tracking, but limited sensor placements can introduce uncertainty in capturing detailed full-body movements. Existing methods mitigate this issue by selecting more physically plausible motion patterns but do not directly address inherent uncertainties in the data. We introduce the Probabilistic Inertial Poser (ProbIP), a novel probabilistic model that transforms sparse IMU data into human motion predictions without physical constraints. ProbIP utilizes RU-Mamba blocks to predict a matrix Fisher distribution over rotations, effectively estimating both rotation matrices and associated uncertainties. To refine motion distribution through layers, our Progressive Distribution Narrowing (PDN) technique enables stable learning across a diverse range of motions. Experimental results demonstrate that ProbIP achieves state-of-the-art performance on multiple public datasets with six and fewer IMU sensors. Our contributions include the development of ProbIP with RU-Mamba blocks for probabilistic motion estimation, applying Progressive Distribution Narrowing (PDN) for uncertainty reduction, and evidence of superior results with six and reduced sensor configurations.
Bi-Level Optimization for Self-Supervised AI-Generated Face Detection
Authors: Mian Zou; Nan Zhong; Baosheng Yu; Yibing Zhan; Kede Ma
Venue: ICCV 2025 (main)

Abstract: AI-generated face detectors trained via supervised learning typically rely on synthesized images from specific generators, limiting their generalization to emerging generative techniques. To overcome this limitation, we introduce a self-supervised method based on bi-level optimization. In the inner loop, we pretrain a vision encoder only on photographic face images using a set of linearly weighted pretext tasks: classification of categorical exchangeable image file format (EXIF) tags, ranking of ordinal EXIF tags, and detection of artificial face manipulations. The outer loop then optimizes the relative weights of these pretext tasks to enhance the coarse-grained detection of manipulated faces, serving as a proxy task for identifying AI-generated faces. In doing so, it aligns self-supervised learning more closely with the ultimate goal of AI-generated face detection. Once pretrained, the encoder remains fixed, and AI-generated faces are detected either as anomalies under a Gaussian mixture model fitted to photographic face features or by a lightweight two-layer perceptron serving as a binary classifier. Extensive experiments demonstrate that our detectors significantly outperform existing approaches in both one-class and binary classification settings, exhibiting strong generalization to unseen generators.