GenAI4ELab/papercli-papers huggingface dataset:
ISP2HRNet: Learning to Reconstruct High Resolution Image from Irregularly Sampled Pixels via Hierarchical Gradient Learning
Authors: Yuanlin Wang; Ruiqin Xiong; Rui Zhao; Jin Wang; Xiaopeng Fan; Tiejun Huang
Venue: ICCV 2025 (main)

Abstract: While image signals are typically defined on a regular 2D grid, there are scenarios where they are only available at irregular positions. In such cases, reconstructing a complete image on regular grid is essential. This paper introduces ISP2HRNet, an end-to-end network designed to reconstruct high resolution image from irregularly sampled pixels that do not fall on a regular grid. To handle the challenges brought by irregular sampling, we propose an architecture to extract gradient structure hierarchically and learn continuous image representation. Specifically, we derive image gradient for each irregularly sampled pixel and further learn higher order gradient structural features according to the geometric and photometric information at the vertices of neighboring triangles. To convert the features from irregular pixels to regular grid, we propose a dual branch content-dependent weight generator to adaptively fuse the information from neighboring irregular pixels. Subsequently, an encoder captures deep structural details on regular grid and forms latent codes. Implicit neural representation parameterized by multi-layer perceptron decodes the latent codes and coordinates to pixel values for generating high resolution image. Experimental results demonstrate that the proposed network effectively solves the problem of high resolution image reconstruction from irregularly sampled pixels and achieves promising results. The source code is available at https://github.com/yuanlinwang/ISP2HRNet.
Group Inertial Poser: Multi-Person Pose and Global Translation from Sparse Inertial Sensors and Ultra-Wideband Ranging
Authors: Ying Xue; Jiaxi Jiang; Rayan Armani; Dominik Hollidt; Yi-Chi Liao; Christian Holz
Venue: ICCV 2025 (main)

Abstract: Tracking human full-body motion using sparse wearable inertial measurement units (IMUs) overcomes the limitations of occlusion and instrumentation of the environment inherent in vision-based approaches. However, purely IMU-based tracking compromises translation estimates and accurate relative positioning between individual people, as inertial cues are inherently self-referential and provide no direct spatial reference about others. In this paper, we present a novel approach for robustly estimating body poses and global translation for multiple individuals by leveraging the distances between sparse wearable sensors - both on each individual and across different people. Our method Group Inertial Poser estimates these absolute distances between pairs of sensors from ultra-wideband ranging (UWB) and fuses them with inertial observations as input into structured state-space models to integrate temporal motion patterns for precise 3D pose estimation. Our novel two-step optimization further leverages the estimated distances for accurately tracking people's global trajectories through the world. We also introduce GIP-DB, the first IMU+UWB dataset for two-person tracking, which comprises 200 minutes of motion recordings from 14 participants. In our evaluation, Group Inertial Poser outperforms previous state-of-the-art methods in accuracy and robustness across synthetic and real-world captures, showing the promise of IMU+UWB-based multi-human motion capture in the wild.
NormalLoc: Visual Localization on Textureless 3D Models using Surface Normals
Authors: Jiro Abe; Gaku Nakano; Kazumine Ogura
Venue: ICCV 2025 (main)

Abstract: We propose NormalLoc, a novel visual localization method for estimating the 6-DoF pose of a camera using textureless 3D models. Existing methods often rely on color or texture information, limiting their applicability in scenarios where such information is unavailable. NormalLoc addresses this limitation by using rendered normal images generated from surface normals of 3D models to establish a training scheme for both global descriptor computation and matching. This approach enables robust visual localization even when geometric details are limited. Experimental results demonstrate that NormalLoc achieves state-of-the-art performance for visual localization on textureless 3D models, especially in scenarios with limited geometric detail.
Light-A-Video: Training-free Video Relighting via Progressive Light Fusion
Authors: Yujie Zhou; Jiazi Bu; Pengyang Ling; Pan Zhang; Tong Wu; Qidong Huang; Jinsong Li; Xiaoyi Dong; Yuhang Zang; Yuhang Cao; Anyi Rao; Jiaqi Wang; Li Niu
Venue: ICCV 2025 (main)

Abstract: Recent advancements in image relighting models, driven by large-scale datasets and pre-trained diffusion models, have enabled the imposition of consistent lighting. However, video relighting still lags, primarily due to the excessive training costs and the scarcity of diverse, high-quality video relighting datasets. A simple application of image relighting models on a frame-by-frame basis leads to several issues: lighting source inconsistency and relighted appearance inconsistency, resulting in flickers in the generated videos. In this work, we propose Light-A-Video, a training-free approach to achieve temporally smooth video relighting. Adapted from image relighting models, Light-A-Video introduces two key techniques to enhance lighting consistency. First, we design a Consistent Light Attention (CLA) module, which enhances cross-frame interactions within the self-attention layers of the image relight model to stabilize the generation of the background lighting source. Second, leveraging the physical principle of light transport independence, we apply linear blending between the source video's appearance and the relighted appearance, using a Progressive Light Fusion (PLF) strategy to ensure smooth temporal transitions in illumination. Experiments show that Light-A-Video improves the temporal consistency of relighted video while maintaining the relighted image quality, ensuring coherent lighting transitions across frames.
Decoupled Multi-Predictor Optimization for Inference-Efficient Model Tuning
Authors: Liwei Luo; Shuaitengyuan Li; Dongwei Ren; Qilong Wang; Pengfei Zhu; Qinghua Hu
Venue: ICCV 2025 (main)

Abstract: Recently, remarkable progress has been made in large-scale pre-trained model tuning, and inference efficiency is becoming more crucial for practical deployment. Early exiting in conjunction with multi-stage predictors, when cooperated with a parameter-efficient fine-tuning strategy, offers a straightforward way to achieve an inference-efficient model. However, a key challenge remains unresolved: How can early stages provide low-level fundamental features to deep stages while simultaneously supplying high-level discriminative features to early-stage predictors? To address this problem, we propose a Decoupled Multi-Predictor Optimization (DMPO) method to effectively decouple the low-level representative ability and high-level discriminative ability in early stages. First, in terms of architecture, we introduce a lightweight bypass module into multi-stage predictors for functional decomposition of shallow features from early stages, while a high-order statistics-based predictor is developed for early stages to effectively enhance their discriminative ability. To reasonably train our multi-predictor architecture, a decoupled optimization is proposed to allocate two-phase loss weights for multi-stage predictors during model tuning, where the initial training phase enables the model to prioritize the acquisition of discriminative ability of deep stages via emphasizing representative ability of early stages, and the latter training phase drives discriminative ability towards earlier stages as much as possible. As such, our DMPO can effectively decouple representative and discriminative abilities in early stages in terms of architecture design and model optimization. Experiments across various datasets and pre-trained backbones demonstrate that DMPO clearly outperforms its counterparts when reducing computational cost. Particularly, DMPO with 30% FLOPs is comparable with or even suppresses counterparts with 70% FLOPs.
Deterministic Object Pose Confidence Region Estimation
Authors: Jinghao Wang; Zhang Li; Zi Wang; Banglei Guan; Yang Shang; Qifeng Yu
Venue: ICCV 2025 (main)

Abstract: 6D pose confidence region estimation has emerged as a critical direction, aiming to perform uncertainty quantification for assessing the reliability of estimated poses. However, current sampling-based approach suffers from critical limitations that severely impede their practical deployment: 1) the sampling speed significantly decreases as the number of samples increases. 2) the derived confidence regions are often excessively large. To address these challenges, we propose a deterministic and efficient method for estimating pose confidence regions. Our approach uses inductive conformal prediction to calibrate the deterministically regressed Gaussian keypoint distributions into 2D keypoint confidence regions. We then leverage the implicit function theorem to propagate these keypoint confidence regions directly into 6D pose confidence regions. This method avoids the inefficiency and inflated region sizes associated with sampling and ensembling. It provides compact confidence regions that cover the ground-truth poses with a user-defined confidence level. Experimental results on the LineMOD Occlusion and SPEED datasets show that our method achieves higher pose estimation accuracy with reduced computational time. For the same coverage rate, our method yields significantly smaller confidence region volumes, reducing them by up to 99.9% for rotations and 99.8% for translations. The code will be available soon.
MedVSR: Medical Video Super-Resolution with Cross State-Space Propagation
Authors: Xinyu Liu; Guolei Sun; Cheng Wang; Yixuan Yuan; Ender Konukoglu
Venue: ICCV 2025 (main)

Abstract: High-resolution (HR) medical videos are vital for accurate diagnosis, yet are hard to acquire due to hardware limitations and physiological constraints. Clinically, the collected low-resolution (LR) medical videos present unique challenges for video super-resolution (VSR) models, including camera shake, noise, and abrupt frame transitions, which result in significant optical flow errors and alignment difficulties. Additionally, tissues and organs exhibit continuous and nuanced structures, but current VSR models are prone to introducing artifacts and distorted features that can mislead doctors. To this end, we propose MedVSR, a tailored framework for medical VSR. It first employs Cross State-Space Propagation (CSSP) to address the imprecise alignment by projecting distant frames as control matrices within state-space models, enabling the selective propagation of consistent and informative features to neighboring frames for effective alignment. Moreover, we design an Inner State-Space Reconstruction (ISSR) module that enhances tissue structures and reduces artifacts with joint long-range spatial feature learning and large-kernel short-range information aggregation. Experiments across four datasets in diverse medical scenarios, including endoscopy and cataract surgeries, show that MedVSR significantly outperforms existing VSR models in reconstruction performance and efficiency. Code released at github.com/CUHK-AIM-Group/MedVSR.
MPBR: Multimodal Progressive Bidirectional Reasoning for Open-Set Fine-Grained Recognition
Authors: Junfu Tan; Peiguang Jing; Yu Zhu; Yu Liu
Venue: ICCV 2025 (main)

Abstract: Open-set fine-grained recognition (OSFGR) is the core exploration of building open-world intelligent systems. The challenge lies in the gradual semantic drift during the transition from coarse-grained to fine-grained categories. However, although existing methods leverage hierarchical representations to assist progressive reasoning, they neglect semantic consistency across hierarchies. To address this, we propose a multimodal progressive bidirectional reasoning framework: (1) In forward reasoning, the model progressively refines visual features to capture hierarchical structural representations, while (2) in backward reasoning, variational inference integrates multimodal information to constrain consistency in category-aware latent spaces. This mechanism mitigates semantic drift through bidirectional information flow and cross-hierarchical feature consistency constraints. Extensive experiments on the iNat2021-OSR dataset demonstrate that our proposed method achieves superior performance.
Domain-aware Category-level Geometry Learning Segmentation for 3D Point Clouds
Authors: Pei He; Lingling Li; Licheng Jiao; Ronghua Shang; Fang Liu; Shuang Wang; Xu Liu; Wenping Ma
Venue: ICCV 2025 (main)

Abstract: Domain generalization in 3D segmentation is a critical challenge in deploying models to unseen environments. Current methods mitigate the domain shift by augmenting the data distribution of point clouds. However, the model learns global geometric patterns in point clouds while ignoring the category-level distribution and alignment. In this paper, a category-level geometry learning framework is proposed to explore the domain-invariant geometric features for domain generalized 3D semantic segmentation. Specifically, Category-level Geometry Embedding (CGE) is proposed to perceive the fine-grained geometric properties of point cloud features, which constructs the geometric properties of each class and couples geometric embedding to semantic learning. Secondly, Geometric Consistent Learning (GCL) is proposed to simulate the latent 3D distribution and align the category-level geometric embeddings, allowing the model to focus on the geometric invariant information to improve generalization. Experimental results verify the effectiveness of the proposed method, which has very competitive segmentation accuracy compared with the state-of-the-art domain generalized point cloud methods. The code will be available at https://github.com/ChicalH/DCGL.
Di[M]O: Distilling Masked Diffusion Models into One-step Generator
Authors: Yuanzhi Zhu; Xi Wang; Stéphane Lathuilière; Vicky Kalogeiton
Venue: ICCV 2025 (main)

Abstract: Masked Diffusion Models (MDMs) have emerged as a powerful generative modeling technique. Despite their remarkable results, they typically suffer from slow inference with several steps. In this paper, we propose Di\mathtt [M] O, a novel approach that distills masked diffusion models into a one-step generator.Di\mathtt [M] O addresses two key challenges: (1) the intractability of using intermediate-step information for one-step generation, which we solve through token-level distribution matching that optimizes model output logits by an `on-policy framework' with the help of an auxiliary model; and (2) the lack of entropy in the initial distribution, which we address through a token initialization strategy that injects randomness while maintaining similarity to teacher training distribution. We show Di\mathtt [M] O's effectiveness on both class-conditional and text-conditional image generation, impressively achieving performance competitive to multi-step teacher outputs while drastically reducing inference time. To our knowledge, we are the first to successfully achieve one-step distillation of masked diffusion models and the first to apply discrete distillation to text-to-image generation, opening new paths for efficient generative modeling.
TerraMind: Large-Scale Generative Multimodality for Earth Observation
Authors: Johannes Jakubik; Felix Yang; Benedikt Blumenstiel; Erik Scheurer; Rocco Sedona; Stefano Maurogiovanni; Jente Bosmans; Nikolaos Dionelis; Valerio Marsocci; Niklas Kopp; Rahul Ramachandran; Paolo Fraccaro; Thomas Brunschwiler; Gabriele Cavallaro; Juan Bernabe-Moreno; Nicolas Longépé
Venue: ICCV 2025 (main)

Abstract: We present TerraMind, the first any-to-any generative, multi-modal foundation model for Earth observation (EO). Unlike other multimodal models, TerraMind is pretrained on dual-scale representations combining both token-level and pixel-level data across modalities. On a token level, TerraMind encodes high-level contextual information to learn cross-modal relationships, while on a pixel level, TerraMind leverages fine-grained representations to capture critical spatial nuances. We pretrained TerraMind on nine geospatial modalities of a global, large-scale dataset. In this paper, we demonstrate that (i) TerraMind's dual-scale early fusion approach unlocks a range of zero-shot and few-shot applications for Earth observation, (ii) TerraMind introduces "thinking in modalities" (TiM)--the capability of generating additional artificial data during finetuning and inference to improve the model output--and (iii) TerraMind achieves beyond state-of-the-art performance in community-standard benchmarks for EO like PANGAEA. All models and code have been open-sourced under a permissive license at https://huggingface.co/ibm-esa-geospatial and https://github.com/ibm/terramind.
An Efficient Post-hoc Framework for Reducing Task Discrepancy of Text Encoders for Composed Image Retrieval
Authors: Jaeseok Byun; Seokhyeon Jeong; Wonjae Kim; Sanghyuk Chun; Taesup Moon
Venue: ICCV 2025 (main)

Abstract: Composed Image Retrieval (CIR) aims to retrieve a target image based on a reference image and conditioning text, enabling controllable image searches. The mainstream Zero-Shot (ZS) CIR methods bypass the need for expensive training CIR triplets by projecting image embeddings into the text token embedding space, forming a composed query for retrieval. However, we highlight an inherent limitation in these projection-based CIR: a task discrepancy of text encoders between the original pre-training task of the encoders (text \leftrightarrow image) and the target CIR task (image + text \leftrightarrow image), which potentially negatively impacts CIR performance. To reduce such a discrepancy, a naive solution would be to train both image and text encoders with CIR triplets in a supervised manner. Instead, we introduce Reducing Task Discrepancy of Text encoders (RTD), an efficient text-only post-hoc framework that complements projection-based CIR methods. We devise a novel frozen-target text contrastive learning designed to enhance the capability of the text encoder for CIR. We also propose two key enhancements: (1) a hard negative-based refined batch sampling strategy and (2) a refined concatenation scheme to further mitigate training-inference discrepancy. Integrating RTD into state-of-the-art projection-based methods achieves performance comparable to, or even surpassing, resource-intensive state-of-the-art synthetic CIR triplet-based approaches, only with 23 minutes of additional training on 4 A100 GPUs. Our code is available in https://github.com/jaeseokbyun/RTD.
Q-Norm: Robust Representation Learning via Quality-Adaptive Normalization
Authors: Lanning Zhang; Ying Zhou; Fei Gao; Ziyun Li; Maoying Qiao; Jinlan Xu; Nannan Wang
Venue: ICCV 2025 (main)

Abstract: Although deep neural networks have achieved remarkable success in various computer vision tasks, they face significant challenges in degraded image understanding due to domain shifts caused by quality variations. Drawing biological inspiration from the human visual system (HVS), which dynamically adjusts perception strategies through contrast gain control and selective attention to salient regions, we propose Quality-Adaptive Normalization (Q-Norm) - a novel normalization method that learns adaptive parameters guided by image quality features. Our approach addresses two critical limitations of conventional normalization techniques: 1) Domain Covariance Shift: Existing methods fail to align feature distributions across different quality domains. Q-Norm implicitly achieves cross-domain alignment through quality-aware parameter adaptation without explicit loss functions. 2) Biological Plausibility: By mimicking HVS's contrast normalization mechanisms and attention-based feature selection, Q-Norm dynamically adjusts the mean and variance parameters using a pre-trained quality assessment model, ensuring robustness to image degradation. Extensive experiments across multiple tasks (image classification, semantic segmentation, object detection) demonstrate that Q-Norm consistently outperforms baseline methods on low-quality images. Code has been released at https://github.com/IIP-Lab-XDU/Q-Norm.
VoiceCraft-Dub: Automated Video Dubbing with Neural Codec Language Models
Authors: Kim Sung-Bin; Jeongsoo Choi; Puyuan Peng; Joon Son Chung; Tae-Hyun Oh; David Harwath
Venue: ICCV 2025 (main)

Abstract: We present VoiceCraft-Dub, a novel approach for automated video dubbing that synthesizes high-quality speech from text and facial cues. This task has broad applications in filmmaking, multimedia creation, and assisting voice-impaired individuals. Building on the success of Neural Codec Language Models (NCLMs) for speech synthesis, our method extends their capabilities by incorporating video features, ensuring that synthesized speech is time-synchronized and expressively aligned with facial movements while preserving natural prosody. To inject visual cues, we design adapters to align facial features with the NCLM token space and introduce audio-visual fusion layers to merge audio-visual information within the NCLM framework. Additionally, we curate CelebV-Dub, a new dataset of expressive, real-world videos specifically designed for automated video dubbing. Extensive experiments show that our model achieves high-quality, intelligible, and natural speech synthesis with accurate lip synchronization, outperforming existing methods in human perception and performing favorably in objective evaluations. We also adapt VoiceCraft-Dub for the video-to-speech task, demonstrating its versatility for various applications.
SUV: Suppressing Undesired Video Content via Semantic Modulation Based on Text Embeddings
Authors: Xiang Lv; Mingwen Shao; Lingzhuang Meng; Chang Liu; Yecong Wan; Xinyuan Chen
Venue: ICCV 2025 (main)

Abstract: Recently, text-driven diffusion models have significantly promoted the development of video editing. However, there still remain two practical challenges: (1) existing text-to-video editing methods struggle to understand negative text prompt, resulting in ineffective suppression of undesirable content in edited video; (2) these methods are difficult to maintain the temporal consistency of the edited video, leading to inter-frame flickering. To address the above challenges, we propose SUV, a novel semantic modulation method based on text embeddings to suppress undesired content in the edited video. Specifically, on the one hand, we discover that the end embeddings (EE) contain substantial coupled positive and negative embeddings, which is the primary reason for the appearance of undesirable content in the edited video. Based on this discovery, we advocate decoupling the negative embeddings from the EE by employing singular value decomposition and propose an exponential suppression operator to decrease the singular values of negative embeddings, thereby restraining the effect of negative embeddings on the edited video content. Subsequently, two constraints are designed to further suppress negative content while keep positive content unchanged via pushing negative embeddings apart and pulling positive embeddings closer. On the other hand, to boost the temporal consistency of edited video, we devise a fuzzy feature selection strategy to fuse similar features in different frame for avoiding inter-frame flickering. Benefiting from the above elaborate designs, our method not only effectively suppresses undesired content of video, but also maintains inter-frame consistency. Extensive experiments demonstrate that our SUV significantly improves edit accuracy and temporal consistency of edited videos compared to existing methods.