GenAI4ELab/papercli-papers huggingface dataset:
Met2Net: A Decoupled Two-Stage Spatio-Temporal Forecasting Model for Complex Meteorological Systems
Authors: Shaohan Li; Hao Yang; Min Chen; Xiaolin Qin
Venue: ICCV 2025 (main)

Abstract: The increasing frequency of extreme weather events due to global climate change urges accurate weather prediction. Recently, great advances are made by the end-to-end methods, thanks to deep learning techniques, but they face limitations of representation inconsistency in multivariable integration and struggle to effectively capture the dependency between variables, which is required in complex weather systems. Treating different variables as distinct modalities and applying a two-stage training approach from multimodal models can partially alleviate this issue, but due to the inconformity in training tasks between the two stages, the results are often suboptimal. To address these challenges, we propose an implicit two-stage training method, configuring separate encoders and decoders for each variable. In detailed, in the first stage, the Translator is frozen while the Encoders and Decoders learn a shared latent space, in the second stage, the Encoders and Decoders are frozen, and the Translator captures inter-variable interactions for prediction. Besides, by introducing a self-attention mechanism for multivariable fusion in the latent space, the performance achieves further improvements. Empirically, extensive experiments shows state-of-the-art performance of our method. In specific, it reduces the MSE for near-surface air temperature and relative humidity predictions by 28.82% and 23.39%, respectively.
SVG-Head: Hybrid Surface-Volumetric Gaussians for High-Fidelity Head Reconstruction and Real-Time Editing
Authors: Heyi Sun; Cong Wang; Tian-Xing Xu; Jingwei Huang; Di Kang; Chunchao Guo; Song-Hai Zhang
Venue: ICCV 2025 (main)

Abstract: Creating high-fidelity and editable head avatars is a pivotal challenge in computer vision and graphics, boosting many AR/VR applications. While recent advancements have achieved photorealistic renderings and plausible animation, head editing, especially real-time appearance editing, remains challenging due to the implicit representation and entangled modeling of the geometry and global appearance. To address this, we propose Surface-Volumetric Gaussian Head Avatar (SVG-Head), a novel hybrid representation that explicitly models the geometry with 3D Gaussians bound on a FLAME mesh and leverages disentangled texture images to capture the global appearance. Technically, it contains two types of Gaussians, in which surface Gaussians explicitly model the appearance of head avatars using learnable texture images, facilitating real-time texture editing, while volumetric Gaussians enhance the reconstruction quality of non-Lambertian regions (e.g., lips and hair). To model the correspondence between 3D world and texture space, we provide a mesh-aware Gaussian UV mapping method, which leverages UV coordinates given by the FLAME mesh to obtain sharp texture images and real-time rendering speed. A hierarchical optimization strategy is further designed to pursue the optimal performance in both reconstruction quality and editing flexibility. Experiments on the NeRSemble dataset show that SVG-Head not only generates high-fidelity rendering results, but also is the first method to obtain explicit texture images for Gaussian head avatars and support real-time appearance editing.
Dynamic Dictionary Learning for Remote Sensing Image Segmentation
Authors: Xuechao Zou; Yue Li; Shun Zhang; Kai Li; Shiying Wang; Pin Tao; Junliang Xing; Congyan Lang
Venue: ICCV 2025 (main)

Abstract: Remote sensing image segmentation faces persistent challenges in distinguishing morphologically similar categories and adapting to diverse scene variations. While existing methods rely on implicit representation learning paradigms, they often fail to dynamically adjust semantic embeddings according to contextual cues, leading to suboptimal performance in fine-grained scenarios such as cloud thickness differentiation. This work introduces a dynamic dictionary learning framework that explicitly models class ID embeddings through iterative refinement. The core contribution lies in a novel dictionary construction mechanism, where class-aware semantic embeddings are progressively updated via multi-stage alternating cross-attention querying between image features and dictionary embeddings. This process enables adaptive representation learning tailored to input-specific characteristics, effectively resolving ambiguities in intra-class heterogeneity and inter-class homogeneity. To further enhance discriminability, a contrastive constraint is applied to the dictionary space, ensuring compact intra-class distributions while maximizing inter-class separability. Extensive experiments across both coarse- and fine-grained datasets demonstrate consistent improvements over state-of-the-art methods, particularly in two online test benchmarks (LoveDA and UAVid). Code is available at https://github.com/XavierJiezou/D2LS.
Video Motion Graphs
Authors: Haiyang Liu; Zhan Xu; Fa-Ting Hong; Hsin-Ping Huang; Yi Zhou; Yang Zhou
Venue: ICCV 2025 (main)

Abstract: We present Video Motion Graphs, a system designed to generate realistic human motion videos. Using a reference video and conditional signals such as music or motion tags, the system synthesizes new videos by first retrieving video clips with gestures matching the conditions and then generating interpolation frames to seamlessly connect clip boundaries. The core of our approach is HMInterp, a robust Video Frame Interpolation (VFI) model that enables seamless interpolation of discontinuous frames, even for complex motion scenarios like dancing. HMInterp i) employs a dual-branch interpolation approach, combining a Motion Diffusion Model for human skeleton motion interpolation with a diffusion-based video frame interpolation model for final frame generation. ii) adopts condition progressive training to effectively leverage identity strong and weak conditions, such as images and pose. These designs ensure both high video texture quality and accurate motion trajectory. Our Video Motion Graphs outperforms existing generative- and retrieval-based methods for human motion video generation. Our codes and pretrained models are public available.
Ask and Remember: A Questions-Only Replay Strategy for Continual Visual Question Answering
Authors: Imad Eddine Marouf; Enzo Tartaglione; Stéphane Lathuilière; Joost Van De Weijer
Venue: ICCV 2025 (main)

Abstract: Continual Learning in Visual Question Answering (VQACL) requires models to acquire new visual-linguistic skills (plasticity) while preserving previously learned knowledge (stability). The inherent multimodality of VQACL exacerbates this challenge, as models must balance stability across visual and textual domains while adapting to novel objects and reasoning tasks. Existing methods, primarily designed for unimodal settings, often fall short in addressing this dual requirement. In this work, we present QUestion-only replay with Attention Distillation (QUAD), a novel approach for VQACL that leverages only past task questions for regularization. By eliminating the need to store visual data, QUAD not only reduces memory overhead, but also alleviates privacy concerns. Our method introduces a Question-only Replay mechanism that selectively reuses prior task questions to counteract overfitting to the answer space of the current task, addressing the problem out of answer set. Complementing this, we propose Attention Consistency Distillation to enforce both intra-modal and inter-modal attention consistency across tasks, preserving essential visual-linguistic associations. Extensive experiments on VQAv2 and NExT-QA demonstrate that QUAD significantly outperforms state-of-the-art methods, achieving robust performance in continual VQA.
Dense Policy: Bidirectional Autoregressive Learning of Actions
Authors: Yue Su; Xinyu Zhan; Hongjie Fang; Han Xue; Hao-Shu Fang; Yong-Lu Li; Cewu Lu; Lixin Yang
Venue: ICCV 2025 (main)

Abstract: Mainstream visuomotor policies predominantly rely on generative models for holistic action prediction, while current autoregressive policies, predicting the next token or chunk, have shown suboptimal results. This motivates a search for more effective learning methods to unleash the potential of autoregressive policies for robotic manipulation. This paper introduces a bidirectionally expanded learning approach, termed Dense Policy, to establish a new paradigm for autoregressive policies in action prediction. It employs a lightweight encoder-only architecture to iteratively unfold the action sequence from an initial single frame into the target sequence in a coarse-to-fine manner with logarithmic-time inference. Extensive experiments validate that our dense policy has superior autoregressive learning capabilities and can surpass existing holistic generative policies. Our policy, example data, and training code will be publicly available upon publication.
Conditional Visual Autoregressive Modeling for Pathological Image Restoration
Authors: Ziyi Liu; Zhe Xu; Jiabo Ma; Wenqiang Li; Ruixuan Wang; Bo Du; Hao Chen
Venue: ICCV 2025 (main)

Abstract: Pathological image has been recognized as the gold standard for cancer diagnosis for more than a century. However, some internal regions of pathological images may inevitably exhibit various degradation issues, including low resolution, image blurring, and image noising, which will affect disease diagnosis, staging, and risk stratification.Existing pathological image restoration methods were mainly based on generative adversarial networks (GANs) to improve image quality, which are limited by the inherent instability and loss of structural details, often resulting in artifacts in the restored images.Large scale of whole slide images (WSIs) also makes it hard for efficient processing and restoration. To address these limitations, we propose a conditional visual autoregressive model (CVARPath) for next-scale token prediction, guided by the degraded tokens from the current scale. We introduce a novel framework that employs quantified encoders specifically designed for pathological image generation, which learns consistent sparse vocabulary tokens through self-supervised contrastive learning. Furthermore, our method efficiently compresses image patches into compact degraded sparse tokens at smaller scales and reconstructs high-quality large-scale whole slide images (WSIs). This is achieved using only an 8x8 vocabulary index for 256x256 images while maintaining minimal reconstruction loss. Experimental results demonstrate that our approach significantly enhances image quality, achieving an approximately 30% improvement in mean Frechet inception distance (FID) compared to popular conditional GANs and diffusion models across various degradation scenarios in pathological images.
BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration
Authors: Hanyuan Liu; Chengze Li; Minshan Xie; Zhenni Wang; Jiawen Liang; Chi-Sing Leung; Tien-Tsin Wong
Venue: ICCV 2025 (main)

Abstract: While digitally acquired photographs have been dominating since around 2000, there remains a huge amount of legacy photographs being acquired by optical cameras and are stored in the form of film negatives. In this paper, we address the unique challenge of channel-heterogeneous deterioration in film negatives and introduce BlueNeg, the first high-quality 35mm negative film dataset specifically designed for restoration in this context. Our work aims to spotlight this underexplored area of image restoration on channel-heterogeneous deterioration. A key challenge in evaluating restoration performance is the absence of ground truth, as many negatives are already contaminated. To overcome this, we utilize printed photographs from the same negatives as proxies for quantitative assessment. These prints, while affected by spatially invariant color fading, are less affected from channel-heterogeneous degradation. We propose a reverse-printing process to estimate pseudo-ground truth from the prints and introduce a dedicated evaluation protocol. Our empirical analysis shows that existing restoration methods struggle with this dataset, highlighting the need for specialized techniques. We hope that BlueNeg and our evaluation framework will inspire further research in legacy photograph restoration, contributing to the digital preservation of historical moments for archival use. Our dataset is publicly available at https://huggingface.co/datasets/ttgroup/blueneg-release and https://blueneg.github.io
egoPPG: Heart Rate Estimation from Eye-Tracking Cameras in Egocentric Systems to Benefit Downstream Vision Tasks
Authors: Björn Braun; Rayan Armani; Manuel Meier; Max Moebus; Christian Holz
Venue: ICCV 2025 (main)

Abstract: Egocentric vision systems aim to understand the spatial surroundings and the wearer's behavior inside it, including motions, activities, and interactions. We argue that egocentric systems must additionally detect physiological states to capture a person's attention and situational responses, which are critical for context-aware behavior modeling. In this paper, we propose egoPPG, a novel vision task for egocentric systems to recover a person's cardiac activity to aid downstream vision tasks. We introduce PulseFormer, a method to extract heart rate as a key indicator of physiological state from the eye tracking cameras on unmodified egocentric vision systems. PulseFormer continuously estimates the photoplethysmogram (PPG) from areas around the eyes and fuses motion cues from the headset's inertial measurement unit to track HR values. We demonstrate egoPPG's downstream benefit for a key task on EgoExo4D, an existing egocentric dataset for which we find PulseFormer's estimates of HR to improve proficiency estimation by 14%. To train and validate PulseFormer, we collected a dataset of 13+ hours of eye tracking videos from Project Aria and contact-based PPG signals as well as an electrocardiogram (ECG) for ground-truth HR values. Similar to EgoExo4D, 25 participants performed diverse everyday activities such as office work, cooking, dancing, and exercising, which induced significant natural motion and HR variation (44-164 bpm). Our model robustly estimates HR (MAE=7.67 bpm) and captures patterns (r=0.85). Our results show how egocentric systems may unify environmental and physiological tracking to better understand users and that egoPPG as a complementary task provides meaningful augmentations for existing datasets and tasks. We release our code, dataset, and HR augmentations for EgoExo4D to inspire research on physiology-aware egocentric tasks.
Beyond Pixel Uncertainty: Bounding the OoD Objects in Road Scenes
Authors: Huachao Zhu; Zelong Liu; Zhichao Sun; Yuda Zou; Gui-Song Xia; Yongchao Xu
Venue: ICCV 2025 (main)

Abstract: Recognizing out-of-distribution (OoD) objects on roads is crucial for safe driving. Most existing methods rely on segmentation models' uncertainty as anomaly scores, often resulting in false positives - especially at ambiguous regions like boundaries, where segmentation models inherently exhibit high uncertainty. Additionally, it is challenging to define a suitable threshold to generate anomaly masks, especially with the inconsistencies in predictions across consecutive frames. We propose DetSeg, a novel paradigm that helps incorporate object-level understanding. DetSeg first detects all objects in the open world and then suppresses in-distribution (ID) bounding boxes, leaving only OoD proposals. These proposals can either help previous methods eliminate false positives (DetSeg-\mathcal R ), or generate binary anomaly masks without complex threshold search when combined with a box-prompted segmentation module (DetSeg-\mathcal S ). Additionally, we introduce vanishing point guided Hungarian matching (VPHM) to smooth the prediction results within a video clip, mitigating abrupt variations of predictions between consecutive frames. Comprehensive experiments on various benchmarks demonstrate that DetSeg significantly improves performance, reducing the FPR\it _ 95   of previous methods by up to 37.45%, offering a more robust and practical solution. Code: https://github.com/huachao0124/DetSeg-official.
Gain-MLP: Improving HDR Gain Map Encoding via a Lightweight MLP
Authors: Trevor D. Canham; SaiKiran Tedla; Michael J. Murdoch; Michael S. Brown
Venue: ICCV 2025 (main)

Abstract: While most images shared on the web and social media platforms are encoded in standard dynamic range (SDR), many displays now can accommodate high dynamic range (HDR) content. Additionally, modern cameras can capture images in an HDR format but convert them to SDR to ensure maximum compatibility with existing workflows and legacy displays. To support both SDR and HDR, new encoding formats are emerging that store additional metadata in SDR images in the form of a gain map. When applied to the SDR image, the gain map recovers the HDR version of the image as needed. These gain maps, however, are typically down-sampled and encoded using standard image compression, such as JPEG and HEIC, which can result in unwanted artifacts. In this paper, we propose to use a lightweight multi-layer perceptron (MLP) network to encode the gain map. The MLP is optimized using the SDR image information as input and provides superior performance in terms of HDR reconstruction. Moreover, the MLP-based approach uses a fixed memory footprint (10 KB) and requires no additional adjustments to accommodate different image sizes or encoding parameters. We conduct extensive experiments on various MLP based HDR embedding strategies and demonstrate that our approach outperforms the current state-of-the-art.
MaterialMVP: Illumination-Invariant Material Generation via Multi-view PBR Diffusion
Authors: Zebin He; Mingxin Yang; Shuhui Yang; Yixuan Tang; Tao Wang; Kaihao Zhang; Guanying Chen; Yuhong Liu; Jie Jiang; Chunchao Guo; Wenhan Luo
Venue: ICCV 2025 (main)

Abstract: Physically-based rendering (PBR) has become a cornerstone in modern computer graphics, enabling realistic material representation and lighting interactions in 3D scenes. In this paper, we present MaterialMVP, a novel end-to-end model for generating PBR textures from 3D meshes and image prompts, addressing key challenges in multi-view material synthesis. Our approach leverages Reference Attention to extract and encode informative latent from the input reference images, enabling intuitive and controllable texture generation. We also introduce a Consistency-Regularized Training strategy to enforce stability across varying viewpoints and illumination conditions, ensuring illumination-invariant and geometrically consistent results. Additionally, we propose Dual-Channel Material Generation, which separately optimizes albedo and metallic-roughness (MR) textures while maintaining precise spatial alignment with the input images through Multi-Channel Aligned Attention. Learnable material embeddings are further integrated to capture the distinct properties of albedo and MR. Experimental results demonstrate that our model generates PBR textures with realistic behavior across diverse lighting scenarios, outperforming existing methods in both consistency and quality for scalable 3D asset creation.
Learning to See in the Extremely Dark
Authors: Hai Jiang; Binhao Guan; Zhen Liu; Xiaohong Liu; Jian Yu; Zheng Liu; Songchen Han; Shuaicheng Liu
Venue: ICCV 2025 (main)

Abstract: Learning-based methods have made promising advances in low-light RAW image enhancement, while their capability to extremely dark scenes where the environmental illuminance drops as low as 0.0001 lux remains to be explored due to the lack of corresponding datasets. To this end, we propose a paired-to-paired data synthesis pipeline capable of generating well-calibrated extremely low-light RAW images at three precise illuminance ranges of 0.01-0.1 lux, 0.001-0.01 lux, and 0.0001-0.001 lux, together with high-quality sRGB references to comprise a large-scale paired dataset named See-in-the-Extremely-Dark (SIED) to benchmark low-light RAW image enhancement approaches. Furthermore, we propose a diffusion-based framework that leverages the generative ability and intrinsic denoising property of diffusion models to restore visually pleasing results from extremely low-SNR RAW inputs, in which an Adaptive Illumination Correction Module (AICM) and a color consistency loss are introduced to ensure accurate exposure correction and color restoration. Extensive experiments on the proposed SIED and publicly available benchmarks demonstrate the effectiveness of our method. The code and dataset are available at https://github.com/JianghaiSCU/SIED.
StyleMotif: Multi-Modal Motion Stylization using Style-Content Cross Fusion
Authors: Ziyu Guo; Young Yoon Lee; Joseph Liu; Yizhak Ben-Shabat; Victor Zordan; Mubbasir Kapadia
Venue: ICCV 2025 (main)

Abstract: We present StyleMotif, a novel Stylized Motion Latent Diffusion model, generating motion conditioned on both content and style from multiple modalities. Unlike existing approaches that either focus on generating diverse motion content or transferring style from sequences, StyleMotif seamlessly synthesizes motion across a wide range of content while incorporating stylistic cues from multi-modal inputs, including motion, text, image, video, and audio. To achieve this, we introduce a style-content cross fusion mechanism and align a style encoder with a pre-trained multi-modal model, ensuring that the generated motion accurately captures the reference style while preserving realism. Extensive experiments demonstrate that our framework surpasses existing methods in stylized motion generation and exhibits emergent capabilities for multi-modal motion stylization, enabling more nuanced motion synthesis. Project Page: https://stylemotif.github.io.
SpecGuard: Spectral Projection-based Advanced Invisible Watermarking
Authors: Inzamamul Alam; Md Tanvir Islam; Simon S. Woo; Khan Muhammad
Venue: ICCV 2025 (main)

Abstract: Watermarking embeds imperceptible patterns into images for authenticity verification. However, existing methods often lack robustness against various transformations primarily including distortions, image regeneration, and adversarial perturbation, creating real-world challenges. In this work, we introduce SpecGuard, a novel watermarking approach for robust and invisible image watermarking. Unlike prior approaches, we embed the message inside hidden convolution layers by converting from the spatial domain to the frequency domain using spectral projection of a higher frequency band that is decomposed by wavelet projection. Spectral projection employs Fast Fourier Transform approximation to transform spatial data into the frequency domain efficiently. In the encoding phase, a strength factor enhances resilience against diverse attacks, including adversarial, geometric, and regeneration-based distortions, ensuring the preservation of copyrighted information. Meanwhile, the decoder leverages Parseval's theorem to effectively learn and extract the watermark pattern, enabling accurate retrieval under challenging transformations. We evaluate the proposed SpecGuard based on the embedded watermark's invisibility, capacity, and robustness. Comprehensive experiments demonstrate the proposed SpecGuard outperforms the state-of-the-art models. To ensure reproducibility, the full code is released on \href https://github.com/inzamamulDU/SpecGuard_ICCV_2025  GitHub .