GenAI4ELab/papercli-papers huggingface dataset:
Recognizing Actions from Robotic View for Natural Human-Robot Interaction
Authors: Ziyi Wang; Peiming Li; Hong Liu; Zhichao Deng; Can Wang; Jun Liu; Junsong Yuan; Mengyuan Liu
Venue: ICCV 2025 (main)

Abstract: Natural Human-Robot Interaction (N-HRI) requires robots to recognize human actions at varying distances and states, regardless of whether the robot itself is in motion or stationary. This setup is more flexible and practical than conventional human action recognition tasks. However, existing benchmarks designed for traditional action recognition fail to address the unique complexities in N-HRI due to limited data, modalities, task categories, and diversity of subjects and environments. To address these challenges, we introduce ACTIVE (Action from Robotic View), a large-scale dataset tailored specifically for perception-centric robotic views prevalent in mobile service robots. ACTIVE comprises 30 composite action categories, 80 participants, and 46,868 annotated video instances, covering both RGB and point cloud modalities. Participants performed various human actions in diverse environments at distances ranging from 3m to 50m, while the camera platform was also mobile, simulating real-world scenarios of robot perception with varying camera heights due to uneven ground. This comprehensive and challenging benchmark aims to advance action and attribute recognition research in N-HRI. Furthermore, we propose ACTIVE-PC, a method that accurately perceives human actions at long distances using Multilevel Neighborhood Sampling, Layered Recognizers, Elastic Ellipse Query, and precise decoupling of kinematic interference from human actions. Experimental results demonstrate the effectiveness of ACTIVE-PC. Our code is available at: https://github.com/wangzy01/ACTIVE-Action-from-Robotic-View.
WIPES: Wavelet-based Visual Primitives
Authors: Wenhao Zhang; Hao Zhu; Delong Wu; Di Kang; Linchao Bao; Xun Cao; Zhan Ma
Venue: ICCV 2025 (main)

Abstract: Pursuing a continuous visual representation that offers flexible frequency modulation and fast rendering speed has recently garnered increasing attention in the fields of 3D vision and graphics. However, existing representations often rely on frequency guidance or complex neural network decoding, leading to spectrum loss or slow rendering. To address these limitations, we propose **WIPES**, a universal **W**avelet-based v**I**sual **P**rimitiv**ES** for representing multi-dimensional visual signals. Building on the spatial-frequency localization advantages of wavelets, WIPES effectively captures both the low-frequency "forest" and the high-frequency "trees." Additionally, we develop a wavelet-based differentiable rasterizer to achieve fast visual rendering. Experimental results on various visual tasks, including 2D image representation, 5D static and 6D dynamic novel view synthesis, demonstrate that WIPES, as a visual primitive, offers higher rendering quality and faster inference than INR-based methods, and outperforms Gaussian-based representations in rendering quality.
Frequency Domain-Based Diffusion Model for Unpaired Image Dehazing
Authors: Chengxu Liu; Lu Qi; Jinshan Pan; Xueming Qian; Ming-Hsuan Yang
Venue: ICCV 2025 (main)

Abstract: Unpaired image dehazing has attracted increasing attention due to its flexible data requirements during model training. Dominant methods based on contrastive learning not only introduce haze-unrelated content information, but also ignore haze-specific properties in the frequency domain (i.e., haze-related degradation is mainly manifested in the amplitude spectrum). To address these issues, we propose a novel frequency domain-based diffusion model, named FrDiff, for fully exploiting the beneficial knowledge in unpaired clear data. In particular, inspired by the strong generative ability shown by Diffusion Models (DMs), we tackle the dehazing task from the perspective of frequency domain reconstruction and perform the DMs to yield the amplitude spectrum consistent with the distribution of clear images. To implement it, we propose an Amplitude Residual Encoder (ARE) to extract the amplitude residuals, which effectively compensates for the amplitude gap from the hazy to clear domains, as well as provide supervision for the DMs training. In addition, we propose a Phase Correction Module (PCM) to eliminate artifacts by further refining the phase spectrum during dehazing with a simple attention mechanism. Experimental results demonstrate that our FrDiff outperforms other state-of-the-art methods on both synthetic and real-world datasets.
Unsupervised Histopathological Image Semantic Segmentation with Overlapping Patches Consistency Constraint
Authors: Wentian Cai; Weizhao Weng; Zihao Huang; Yandan Chen; Siquan Huang; Ping Gao; Victor C. M. Leung; Ying Gao
Venue: ICCV 2025 (main)

Abstract: Massive requirement for pixel-wise annotations in histopathological image segmentation poses a significant challenge, leading to increasing interest in Unsupervised Semantic Segmentation (USS) as a viable alternative. Pre-trained model-based methods have been widely used in USS, achieving promising segmentation performance. However, these methods are less capable for medical image USS tasks due to their limited ability in encoding task-specific contextual information. In this paper, we propose a context-based Overlapping Patches Consistency Constraint (OPCC), which employs the consistency constraint between the local overlapping region's similarity and global context similarity, achieving consistent class representation in similar environments. Additionally, we introduce an Inter-Layer Self-Attention Fusion (ILSAF) module that employs a multi-head self-attention mechanism along with Inter-Layer Importance-Weighting to generate context-aware and semantically discriminative pixel representations, improving pixel clustering accuracy. Extensive experiments on two public histopathological image segmentation datasets demonstrate that our approach significantly outperforms state-of-the-art methods by a large margin, with mIoU surpassing previous leading work by 5.74 and 8.38 percentage points on the two datasets, respectively.
SAGI: Semantically Aligned and Uncertainty Guided AI Image Inpainting
Authors: Paschalis Giakoumoglou; Dimitrios Karageorgiou; Symeon Papadopoulos; Panagiotis C. Petrantonakis
Venue: ICCV 2025 (main)

Abstract: Recent advancements in generative AI have made text-guided image inpainting--adding, removing, or altering image regions using textual prompts--widely accessible. However, generating semantically correct photorealistic imagery, typically requires carefully-crafted prompts and iterative refinement by evaluating the realism of the generated content - tasks commonly performed by humans. To automate the generative process, we propose Semantically Aligned and Uncertainty Guided AI Image Inpainting (SAGI), a model-agnostic pipeline, to sample prompts from a distribution that closely aligns with human perception and to evaluate the generated content and discard instances that deviate from such a distribution, which we approximate using pretrained large language models and vision-language models. By applying this pipeline on multiple state-of-the-art inpainting models, we create the SAGI Dataset SAGI-D, currently the largest and most diverse dataset of AI-generated inpaintings, comprising over 95k inpainted images and a human-evaluated subset. Our experiments show that semantic alignment significantly improves image quality and aesthetics, while uncertainty guidance effectively identifies realistic manipulations -- human ability to distinguish inpainted images from real ones drops from 74% to 35% in terms of accuracy, after applying our pipeline. Moreover, using SAGI-D for training several image forensic approaches increases in-domain detection performance on average by 37.4% and out-of-domain generalization by 26.1% in terms of IoU, also demonstrating its utility in countering malicious exploitation of generative AI. Code and dataset are available at https://mever-team.github.io/SAGI/
Adversarial Purification via Super-Resolution and Diffusion
Authors: Mincheol Park; Cheonjun Park; Seungseop Lim; Mijin Koo; Hyunwuk Lee; Won Woo Ro; Suhyun Kim
Venue: ICCV 2025 (main)

Abstract: Deep neural networks are widely used in various computer vision tasks, but their vulnerability to adversarial perturbations remains a significant challenge for reliable decision-making. Adversarial purification, a test-time defense strategy, has shown potential in countering these threats by removing noise through diffusion models. This plug-and-play method, using off-the-shelf models, appears highly effective. However, the purified data from diffusion often deviates more from the original data than the adversarial examples, leading to missing critical information and causing misclassification. In this study, we propose that upsampling with Super-Resolution (SR), followed by downsampling, can also aid in eliminating adversarial noise, similar to the noise addition and removal process in diffusion models. While SR alone is not as effective as the diffusion process, it better restores the original features typically associated with the early layers of networks. By combining SR, which initially mitigates damage to early-layer information from adversarial attacks, with diffusion, we observe a synergistic effect, leading to enhanced performance over diffusion models alone. Our comprehensive evaluations demonstrate that this combined approach, PuriFlow, significantly improves accuracy and robustness, working synergistically with state-of-the-art methods.
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation
Authors: Yuhan Li; Xianfeng Tan; Wenxiang Shang; Yubo Wu; Jian Wang; Xuanhong Chen; Yi Zhang; Hangcheng Zhu; Bingbing Ni
Venue: ICCV 2025 (main)

Abstract: Standard clothing asset generation involves restoring forward-facing flat-lay garment images displayed on a clear background by extracting clothing information from diverse real-world contexts, which presents significant challenges due to highly standardized structure sampling distributions and clothing semantic absence in complex scenarios. Existing models have limited spatial perception, often exhibiting structural hallucinations and texture distortion in this high-specification generative task. To address this issue, we propose a novel Retrieval-Augmented Generation (RAG) framework, termed RAGDiffusion, to enhance structure determinacy and mitigate hallucinations by assimilating knowledge from language models and external databases. RAGDiffusion consists of two processes: (1) Retrieval-based structure aggregation, which employs contrastive learning and a Structure Locally Linear Embedding (SLLE) to derive global structure and spatial landmarks, providing both soft and hard guidance to counteract structural ambiguities; and (2) Omni-level faithful garment generation, which introduces a coarse-to-fine texture alignment that ensures fidelity in pattern and detail components within the diffusing. Extensive experiments on challenging real-world datasets demonstrate that RAGDiffusion synthesizes structurally and texture-faithful clothing assets with significant performance improvements, representing a pioneering effort in high-specification faithful generation with RAG to confront intrinsic hallucinations and enhance fidelity.
Diff2I2P: Differentiable Image-to-Point Cloud Registration with Diffusion Prior
Authors: Juncheng Mu; Chengwei Ren; Weixiang Zhang; Liang Pan; Xiao-Ping Zhang; Yue Gao
Venue: ICCV 2025 (main)

Abstract: Learning cross-modal correspondences is essential for image-to-point cloud (I2P) registration. Existing methods achieve this mostly by utilizing metric learning to enforce feature alignment across modalities, disregarding the inherent modality gap between image and point data. Consequently, this paradigm struggles to ensure accurate cross-modal correspondences. To this end, inspired by the cross-modal generation success of recent large diffusion models, we propose **Diff^2I2P**, a fully **Diff**erentiable **I2P** registration framework, leveraging a novel and effective **Diff**usion prior for bridging the modality gap. Specifically, we propose a Control-Side Score Distillation (CSD) technique to distill knowledge from a depth-conditioned diffusion model to directly optimize the predicted transformation. However, the gradients on the transformation fail to backpropagate onto the cross-modal features due to the non-differentiability of correspondence retrieval and PnP solver. To this end, we further propose a Deformable Correspondence Tuning (DCT) module to estimate the correspondences in a differentiable way, followed by the transformation estimation using a differentiable PnP solver. With these two designs, the Diffusion model serves as a strong prior to guide the cross-modal feature learning of image and point cloud for forming robust correspondences, which significantly improves the registration. Extensive experimental results demonstrate that **Diff^2I2P** consistently outperforms state-of-the-art I2P registration methods, achieving over 7 % improvement in registration recall on the 7-Scenes benchmark. Moreover, **Diff^2I2P** exhibits robust and superior scene-agnostic registration performance.
Dynamic Typography: Bringing Text to Life via Video Diffusion Prior
Authors: Zichen Liu; Yihao Meng; Hao Ouyang; Yue Yu; Bolin Zhao; Daniel Cohen-Or; Huamin Qu
Venue: ICCV 2025 (main)

Abstract: Text animation serves as an expressive medium, transforming static communication into dynamic experiences by infusing words with motion to evoke emotions, emphasize meanings, and construct compelling narratives. Crafting animations that are semantically aware poses significant challenges, demanding expertise in graphic design and animation. We present an automated text animation scheme, termed "Dynamic Typography", which deforms letters to convey semantic meaning and infuses them with vibrant movements based on user prompts. The animation is represented by a canonical field that aggregates the semantic content in a canonical shape and a deformation field that applies per-frame motion to deform the canonical shape. Two fields are jointly optimized by the priors from a large pretrained text-to-video diffusion model using score-distillation loss with designed regularization, encouraging the video coherence with the intended textual concept while maintaining legibility and structural integrity throughout the animation process. We demonstrate the generalizability of our approach across various text-to-video models and highlight the superiority of our methodology over baselines. Through quantitative and qualitative evaluations, we demonstrate the effectiveness of our framework in generating coherent text animations that faithfully interpret user prompts while maintaining readability.
LightSwitch: Multi-view Relighting with Material-guided Diffusion
Authors: Yehonathan Litman; Fernando De la Torre; Shubham Tulsiani
Venue: ICCV 2025 (main)

Abstract: Recent approaches for 3D relighting have shown promise in integrating 2D image relighting generative priors to alter the appearance of a 3D representation while preserving the underlying structure. Nevertheless, generative priors used for 2D relighting that directly relight from an input image do not take advantage of intrinsic properties of the subject that can be inferred or cannot consider multi-view data at scale, leading to subpar relighting. In this paper, we propose Lightswitch, a novel finetuned material-relighting diffusion framework that efficiently relights an arbitrary number of input images to a target lighting condition while incorporating cues from inferred intrinsic properties. By using multi-view and material information cues together with a scalable denoising scheme, our method consistently and efficiently relights dense multi-view data of objects with diverse material compositions. We show that our 2D relighting prediction quality exceeds previous state-of-the-art relighting priors that directly relight from images. We further demonstrate that LightSwitch matches or outperforms state-of-the-art diffusion inverse rendering methods in relighting synthetic and real objects in as little as 2 minutes.
CanFields: Consolidating Diffeomorphic Flows for Non-Rigid 4D Interpolation from Arbitrary-Length Sequences
Authors: Miaowei Wang; Changjian Li; Amir Vaxman
Venue: ICCV 2025 (main)

Abstract: We introduce Canonical Consolidation Fields (CanFields). This novel method interpolates arbitrary-length sequences of independently sampled 3D point clouds into a unified, continuous, and coherent deforming shape. Unlike prior methods that oversmooth geometry or produce topological and geometric artifacts, CanFields optimizes fine-detailed geometry and deformation jointly in an unsupervised fitting with two novel bespoke modules. First, we introduce a dynamic consolidator module that adjusts the input and assigns confidence scores, balancing the optimization of the canonical shape and its motion. Second, we represent the motion as a diffeomorphic flow parameterized by a smooth velocity field. We have validated our robustness and accuracy on more than 50 diverse sequences, demonstrating its superior performance even with missing regions, noisy raw scans, and sparse data. Our project page is at: https://wangmiaowei.github.io/CanFields.github.io/.
Occlusion-robust Stylization for Drawing-based 3D Animation
Authors: Sunjae Yoon; Gwanhyeong Koo; Younghwan Lee; Ji Woo Hong; Chang D. Yoo
Venue: ICCV 2025 (main)

Abstract: 3D animation aims to generate a 3D animated video from an input image and a target 3D motion sequence. Recent advances in image-to-3D models enable the creation of animations directly from user-hand drawings. Distinguished from conventional 3D animation, drawing-based 3D animation is crucial to preserve artist's unique style properties, such as rough contours and distinct stroke patterns. However, recent methods still exhibit quality deterioration in style properties, especially under occlusions caused by overlapping body parts, leading to contour flickering and stroke blurring. This occurs due to a `stylization pose gap' between training and inference in stylization networks designed to preserve drawing styles in drawing-based 3D animation systems. The stylization pose gap denotes that input target poses used to train the stylization network are always in occlusion-free poses, while target poses encountered in an inference include diverse occlusions under dynamic motions. To this end, we propose Occlusion-robust Stylization Framework (OSF) for drawing-based 3D animation. We found that while employing object's edge can be effective input prior for guiding stylization, it becomes notably inaccurate when occlusions occur at inference. Thus, our proposed OSF provides occlusion-robust edge guidance for stylization network using optical flow, ensuring a consistent stylization even under occlusions. Furthermore, OSF operates in a single run instead of the previous two-stage method, achieving 2.4x faster inference and 2.1x less memory. Project is available at: github.io/Drawing-based-3D-Animation-page.
Towards Foundational Models for Single-Chip Radar
Authors: Tianshu Huang; Akarsh Prabhakara; Chuhan Chen; Jay Karhade; Deva Ramanan; Matthew O'toole; Anthony Rowe
Venue: ICCV 2025 (main)

Abstract: mmWave radars are compact, inexpensive, and durable sensors that are robust to occlusions and work regardless of environmental conditions, such as weather and darkness. However, this comes at the cost of poor angular resolution, especially for inexpensive single-chip radars, which are typically used in automotive and indoor sensing applications. Although many have proposed learning-based methods to mitigate this weakness, no standardized foundational models or large datasets for the mmWave radar have emerged, and practitioners have largely trained task-specific models from scratch using relatively small datasets.In this paper, we collect (to our knowledge) the largest available raw radar dataset with 1M samples (29 hours) and train a foundational model for 4D single-chip radar, which can predict 3D occupancy and semantic segmentation with quality that is typically only possible with much higher resolution sensors. We demonstrate that our Generalizable Radar Transformer (GRT) generalizes across diverse settings, can be fine-tuned for different tasks, and shows logarithmic data scaling of 20% per 10xdata. We also run extensive ablations on common design decisions, and find that using raw radar data significantly outperforms widely-used lossy representations, equivalent to a 10xincrease in training data. Finally, we estimate a total data requirement of ~100M samples (3000 hours) to fully exploit the potential of GRT.
Augmented and Softened Matching for Unsupervised Visible-Infrared Person Re-Identification
Authors: Zhiqi Pang; Chunyu Wang; Lingling Zhao; Junjie Wang
Venue: ICCV 2025 (main)

Abstract: Color variations, a key challenge in the unsupervised visible-infrared person re-identification (UVI-ReID) task, have garnered significant attention. While existing UVI-ReID methods have made substantial efforts during the optimization phase to enhance the model's robustness to color variations, they often overlook the impact of color variations on the acquisition of pseudo-labels. To address this, in this paper, we focus on improving the robustness of pseudo-labels to color variations through data augmentation and propose an augmented and softened matching (ASM) method. Specifically, we first develop the cross-modality augmented matching (CAM) module, which performs channel augmentation on visible images to generate augmented images. Then, based on the fusion of the visible-infrared and augmented-infrared centroid similarity matrices, CAM establishes cross-modality correspondences that are robust to color variations. To increase training stability, we design a soft-labels momentum update (SMU) strategy, which converts traditional one-hot labels into soft-labels through momentum updates, thus adapting to CAM. During the optimization phase, we introduce the cross-modality soft contrastive loss and cross-modality hard contrastive loss to promote modality-invariant learning from the perspectives of shared and diversified features, respectively. Extensive experimental results validate the effectiveness of the proposed method, showing that ASM not only outperforms state-of-the-art unsupervised methods but also competes with some supervised methods.
Spatial Alignment and Temporal Matching Adapter for Video-Radar Remote Physiological Measurement
Authors: Qian Liang; Ruixu Geng; Jinbo Chen; Haoyu Wang; Yan Chen; Yang Hu
Venue: ICCV 2025 (main)

Abstract: Remote physiological measurement (RPM) based on video and radar has made significant progress in recent years. However, unimodal methods based solely on video or radar sensor have notable limitations due to their measurement principles, and multimodal RPM that combines these modalities has emerged as a promising direction. Despite its potential, the lack of large-scale multimodal data and the significant modality gap between video and radar pose substantial challenges in building robust video-radar RPM models. To handle these problems, we suggest leveraging unimodal pre-training and present the Spatial alignment and Temporal Matching (SATM) Adapter to effectively fine-tune pre-trained unimodal backbones into a multimodal RPM model. Given the distinct measurement principles of video- and radar-based methods, we propose Spatial Alignment to align the spatial distribution of their features. Furthermore, Temporal Matching is applied to mitigate waveform discrepancies between video and radar signals. By integrating these two modules into adapters, the unimodal backbones could retain their modality-specific knowledge while effectively extracting complementary features from each other. Extensive experiments across various challenging scenarios, including low light conditions and head motions, demonstrate that our approach significantly surpasses the state-of-the-art methods.