GenAI4ELab/papercli-papers huggingface dataset:
Text-guided Visual Prompt DINO for Generic Segmentation
Authors: Yuchen Guan; Chong Sun; Canmiao Fu; Zhipeng Huang; Chun Yuan; Chen Li
Venue: ICCV 2025 (main)

Abstract: Recent advancements in multimodal vision models have highlighted limitations in late-stage feature fusion and suboptimal query selection for hybrid prompts open-world segmentation, alongside constraints from caption-derived vocabularies. To address these challenges, we propose Prompt-DINO, a text-guided visual Prompt DINO framework featuring three key innovations. First, we introduce an early fusion mechanism that unifies text/visual prompts and backbone features at the initial encoding stage, enabling deeper cross-modal interactions to resolve semantic ambiguities. Second, we design order-aligned query selection for DETR-based architectures, explicitly optimizing the structural alignment between text and visual queries during decoding to enhance semantic-spatial consistency. Third, we develop a generative data engine powered by the Recognize Anything via Prompting (RAP) model, which synthesizes 0.5B diverse training instances through a dual-path cross-verification pipeline, reducing label noise by 80.5% compared to conventional approaches. Extensive experiments demonstrate that Prompt-DINO achieves state-of-the-art performance on open-world detection benchmarks while significantly expanding semantic coverage beyond fixed-vocabulary constraints. Our work establishes a new paradigm for scalable multimodal detection and data generation in open-world scenarios. Data&Code are available at https://github.com/WeChatCV/WeVisionOne.
Open-set Cross Modal Generalization via Multimodal Unified Representation
Authors: Hai Huang; Yan Xia; Shulei Wang; Hanting Wang; Minghui Fang; Shengpeng Ji; Sashuai Zhou; Tao Jin; Zhou Zhao
Venue: ICCV 2025 (main)

Abstract: This paper extends Cross Modal Generalization (CMG) to open-set environments by proposing the more challenging Open-set Cross Modal Generalization (OSCMG) task. This task evaluates multimodal unified representations in open-set conditions, addressing the limitations of prior closed-set cross-modal evaluations. OSCMG requires not only cross-modal knowledge transfer but also robust generalization to unseen classes within new modalities, a scenario frequently encountered in real-world applications. Existing multimodal unified representation work lacks consideration for open-set environments. To tackle this, we propose MICU, comprising two key components: Fine-Coarse Masked multimodal InfoNCE (FCMI) and Cross modal Unified Jigsaw Puzzles (CUJP). FCMI enhances multimodal alignment by applying contrastive learning at both holistic semantic and temporal levels, incorporating masking to enhance generalization. CUJP enhances feature diversity and model uncertainty by integrating modality-agnostic feature selection with self-supervised learning, thereby strengthening the model's ability to handle unknown categories in open-set tasks. Extensive experiments on CMG and the newly proposed OSCMG validate the effectiveness of our approach. The code is available at https://github.com/haihuangcode/CMG.
PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference Time by Leveraging Sparsity
Authors: Kwanyoung Kim; Byeongsu Sim
Venue: ICCV 2025 (main)

Abstract: Diffusion models have shown impressive results in generating high-quality conditional samples using guidance techniques such as Classifier-Free Guidance (CFG). However, existing methods often require additional training or neural function evaluations (NFEs), making them incompatible with guidance-distilled models. Also, they rely on heuristic approaches that need identifying target layers. In this work, we propose a novel and efficient method, termed PLADIS, which boosts pre-trained models (U-Net/Transformer) by leveraging sparse attention. Specifically, we extrapolate query-key correlations using softmax and its sparse counterpart in the cross-attention layer during inference, without requiring extra training or NFEs. By leveraging the noise robustness of sparse attention, our PLADIS unleashes the latent potential of text-to-image diffusion models, enabling them to excel in areas where they once struggled with newfound effectiveness. It integrates seamlessly with guidance techniques, including guidance-distilled models. Extensive experiments show notable improvements in text alignment and human preference, offering a highly efficient and universally applicable solution. See our project page: https://github.com/cubeyoung/PLADIS
GT-Loc: Unifying When and Where in Images Through a Joint Embedding Space
Authors: David G. Shatwell; Ishan Rajendrakumar Dave; Sirnam Swetha; Mubarak Shah
Venue: ICCV 2025 (main)

Abstract: Timestamp prediction aims to determine when an image was captured using only visual information, supporting applications such as metadata correction, retrieval, and digital forensics. In outdoor scenarios, hourly estimates rely on cues like brightness, hue, and shadow positioning, while seasonal changes and weather inform date estimation. However, these visual cues significantly depend on geographic context, closely linking timestamp prediction to geo-localization. To address this interdependence, we introduce GT-Loc, a novel retrieval-based method that jointly predicts the capture time (hour and month) and geo-location (GPS coordinates) of an image. Our approach employs separate encoders for images, time, and location, aligning their embeddings within a shared high-dimensional feature space. Recognizing the cyclical nature of time, instead of conventional contrastive learning with hard positives and negatives, we propose a temporal metric-learning objective providing soft targets by modeling pairwise time differences over a cyclical toroidal surface. We present new benchmarks demonstrating that our joint optimization surpasses previous time prediction methods, even those using the ground-truth geo-location as an input during inference. Additionally, our approach achieves competitive results on standard geo-localization tasks, and the unified embedding space facilitates compositional and text-based image retrieval.
Robust Test-Time Adaptation for Single Image Denoising Using Deep Gaussian Prior
Authors: Qing Ma; Pengwei Liang; Xiong Zhou; Jiayi Ma; Junjun Jiang; Zhe Peng
Venue: ICCV 2025 (main)

Abstract: Gaussian denoising often serves as the initiation of research in the field of image denoising, owing to its prevalence and intriguing properties. However, deep Gaussian denoiser typically generalizes poorly to other types of noises, such as Poisson noise and real-world noise. In this paper, we reveal that deep Gaussian denoisers have an underlying ability to handle other noises with only ten iterations of self-supervised learning, which is referred to as deep denoiser prior. Specifically, we first pre-train a Gaussian denoising model in a self-supervised manner. Then, for each test image, we construct a pixel bank based on the self-similarity and randomly sample pseudo-instance examples from it to perform test-time adaptation. Finally, we fine-tune the pre-trained Gaussian denoiser using the randomly sampled pseudo-instances. Extensive experiments demonstrate that our test-time adaptation method helps the pre-trained Gaussian denoiser rapidly improve performance in removing both in-distribution and out-of-distribution noise, achieving superior performance compared to existing single-image denoising methods while also significantly reducing computational time. Code available at: https://github.com/qingma2016/TTAD.
MATE: Motion-Augmented Temporal Consistency for Event-based Point Tracking
Authors: Han Han; Wei Zhai; Yang Cao; Bin Li; Zheng-jun Zha
Venue: ICCV 2025 (main)

Abstract: Tracking Any Point (TAP) plays a crucial role in motion analysis. Video-based approaches rely on iterative local matching for tracking, but they assume linear motion during the blind time between frames, which leads to point loss under large displacements or nonlinear motion. The high temporal resolution and motion blur-free characteristics of event cameras provide continuous, fine-grained motion information, capturing subtle variations with microsecond precision. This paper presents an event-based framework for tracking any point, which tackles the challenges posed by spatial sparsity and motion sensitivity in events through two tailored modules. Specifically, to resolve ambiguities caused by event sparsity, a motion-guidance module incorporates kinematic vectors into the local matching process. Additionally, a variable motion aware module is integrated to ensure temporally consistent responses that are insensitive to varying velocities, thereby enhancing matching precision.To validate the effectiveness of the approach, two event dataset for tracking any point is constructed by simulation. The method improves the Survival_ 50  metric by 17.9% over event-only tracking of any point baseline. Moreover, on standard feature tracking benchmarks, it outperforms all existing methods, even those that combine events and video frames.
Diffusion-Based Extreme High-speed Scenes Reconstruction with the Complementary Vision Sensor
Authors: Yapeng Meng; Yihan Lin; Taoyi Wang; Yuguo Chen; Lijian Wang; Rong Zhao
Venue: ICCV 2025 (main)

Abstract: Recording and reconstructing high-speed scenes poses a significant challenge. While high-speed cameras can capture fine temporal details, their extremely high bandwidth demands make continuous recording unsustainable. Conversely, traditional RGB cameras, typically operating at 30 FPS, rely on frame interpolation to synthesize high-speed motion, often introducing artifacts and motion blur. Human visual system inspired sensors, like event cameras, offer high-speed sparse temporal or spatial variation data, partially alleviating these issues. However, existing methods still suffer from RGB blur, temporal aliasing, and loss of event information. To overcome these challenges, we leverage a novel complementary vision sensor, Tianmouc, which outputs high-speed, multi-bit, sparse spatio-temporal difference information with RGB frames. Building on this unique sensing modality, we introduce a Cascaded Bi-directional Recurrent Diffusion Model (CBRDM) that achieves accurate, sharp, color-rich video frames reconstruction. Our method outperforms state-of-the-art RGB interpolation algorithms in quantitative evaluations and surpasses event-based methods in real-world comparisons. Code and dataset are at https://github.com/Tianmouc/GenRec.
RnGCam: High-speed video from rolling & global shutter measurements
Authors: Kevin Tandi; Xiang Dai; Chinmay Talegaonkar; Gal Mishne; Nick Antipa
Venue: ICCV 2025 (main)

Abstract: Compressive video capture encodes a short high-speed video into a single measurement using a low-speed sensor, then computationally reconstructs the original video. Prior implementations rely on expensive hardware and are restricted to imaging sparse scenes with empty backgrounds. We propose RnGCam, a system that fuses measurements from low-speed consumer-grade rolling-shutter (RS) and global-shutter (GS) sensors into video at kHz frame rates. The RS sensor is combined with a pseudorandom optic, called a diffuser, which spatially multiplexes scene information. The GS sensor is coupled with a conventional lens. The RS-diffuser provides low spatial detail and high temporal detail, complementing the GS-lens system's high spatial detail and low temporal detail. We propose a reconstruction method using implicit neural representations (INR) to fuse the measurements into a high-speed video. Our INR method separately models the static and dynamic scene components, while regularizing dynamics explicitly. In simulation, we show that our approach significantly outperforms previous RS compressive video methods, as well as state-of-the-art frame interpolators. We validate our approach in a dual-camera hardware setup, which generates 230 frames of video at 4,800 frames per second for dense scenes, using hardware that costs 10x less than previous compressive video systems.
OV3D-CG: Open-vocabulary 3D Instance Segmentation with Contextual Guidance
Authors: Mingquan Zhou; Chen He; Ruiping Wang; Xilin Chen
Venue: ICCV 2025 (main)

Abstract: Open-vocabulary 3D instance segmentation (OV-3DIS), which aims to segment and classify objects beyond predefined categories, is a critical capability for embodied AI applications. Existing methods rely on pre-trained 2D foundation models, focusing on instance-level features while overlooking contextual relationships, limiting their ability to generalize to rare or ambiguous objects. To address these limitations, we propose an OV-3DIS framework guided by contextual information. First, we employ a Class-agnostic Proposal Module, integrating a pre-trained 3D segmentation model with a SAM-guided segmenter to extract robust 3D instance masks. Subsequently, we design a Semantic Reasoning Module, which selects the best viewpoint for each instance and constructs three 2D context-aware representations. The representations are processed using Multimodal Large Language Models with Chain-of-Thought prompting to enhance semantic inference. Notably, our method outperforms state-of-the-art methods on the ScanNet200 and Replica datasets, demonstrating superior open-vocabulary segmentation capabilities. Moreover, preliminary implementation in real-world scenarios verifies our method's robustness and accuracy, highlighting its potential for embodied AI tasks such as object-driven navigation. Our project page is at: https://vipl-vsu.github.io/OV3D-CG/.
FG-OrIU: Towards Better Forgetting via Feature-Gradient Orthogonality for Incremental Unlearning
Authors: Qian Feng; JiaHang Tu; Mintong Kang; Hanbin Zhao; Chao Zhang; Hui Qian
Venue: ICCV 2025 (main)

Abstract: Incremental unlearning (IU) is critical for pre-trained models to comply with sequential data deletion requests, yet existing methods primarily suppress parameters or confuse knowledge without explicit constraints on both feature and gradient level, resulting in superficial forgetting where residual information remains recoverable. This incomplete forgetting risks security breaches and disrupts retention balance, especially in IU scenarios. We propose FG-OrIU (Feature-Gradient Orthogonality for Incremental Unlearning), the first framework unifying orthogonal constraints on both features and gradients level to achieve deep forgetting, where the forgetting effect is irreversible. FG-OrIU decomposes feature spaces via Singular Value Decomposition (SVD), separating forgetting and remaining class features into distinct subspaces. It then enforces dual constraints: feature orthogonal projection on both forgetting and remaining classes, while gradient orthogonal projection prevents the reintroduction of forgotten knowledge and disruption to remaining classes during updates. Additionally, dynamic subspace adaptation merges newly forgetting subspaces and contracts remaining subspaces, ensuring a stable balance between removal and retention across sequential unlearning tasks. Extensive experiments demonstrate the effectiveness of our method.
InfiniCube: Unbounded and Controllable Dynamic 3D Driving Scene Generation with World-Guided Video Models
Authors: Yifan Lu; Xuanchi Ren; Jiawei Yang; Tianchang Shen; Zhangjie Wu; Jun Gao; Yue Wang; Siheng Chen; Mike Chen; Sanja Fidler; Jiahui Huang
Venue: ICCV 2025 (main)

Abstract: We present InfiniCube, a scalable and controllable method to generate unbounded and dynamic 3D driving scenes with high fidelity.Previous methods for scene generation are constrained either by their applicability to indoor scenes or by their lack of controllability.In contrast, we take advantage of recent advances in 3D and video generative models to achieve large dynamic scene generation that allows flexible controls through HD maps, vehicle bounding boxes, and text descriptions.First, we construct a map-conditioned 3D voxel generative model to unleash its power for unbounded voxel world generation. Then, we re-purpose a video model and ground it on the voxel world through a set of pixel-aligned guidance buffers, synthesizing a consistent appearance on long-video generation for large-scale scenes.Finally, we propose a fast feed-forward approach that employs both voxel and pixel branches to lift videos to dynamic 3D Gaussians with controllable objects.Our method can generate controllable and realistic 3D driving scenes, and extensive experiments validate the effectiveness of our model design. Code will be released upon acceptance.
Scene Graph Guided Generation: Enable Accurate Relations Generation in Text-to-Image Models via Textural Rectification
Authors: Guibao Shen; Luozhou Wang; Jiantao Lin; Wenhang Ge; Chaozhe Zhang; Xin Tao; Di Zhang; Pengfei Wan; Guangyong Chen; Yijun Li; Ying-cong Chen
Venue: ICCV 2025 (main)

Abstract: Recent advancements in text-to-image generation have been propelled by the development of diffusion models and multi-modality learning. However, since text is typically represented sequentially in these models, it often falls short in providing accurate contextualization and structural control. So the generated images do not consistently align with human expectations, especially in complex scenarios involving multiple objects and relationships. In this paper, we introduce the Scene Graph Adapter(SG-Adapter), leveraging the structured representation of scene graphs to rectify inaccuracies in the original text embeddings. The SG-Adapter's explicit and non-fully connected graph representation greatly improves the fully connected, transformer-based text representations. This enhancement is particularly notable in maintaining precise correspondence in scenarios involving multiple relationships. To address the challenges posed by low-quality annotated datasets like Visual Genome, we have manually curated a highly clean, multi-relational scene graph-image paired dataset MultiRels. Furthermore, we design three metrics derived from GPT-4V to effectively and thoroughly measure the correspondence between images and scene graphs. Both qualitative and quantitative results validate the efficacy of our approach in controlling the correspondence in multiple relationships.
After the Party: Navigating the Mapping From Color to Ambient Lighting
Authors: Florin-Alexandru Vasluianu; Tim Seizinger; Zongwei Wu; Radu Timofte
Venue: ICCV 2025 (main)

Abstract: Illumination in practical scenarios is inherently complex, involving colored light sources, occlusions, and diverse material interactions that produce intricate reflectance and shading effects. However, existing methods often oversimplify this challenge by assuming a single light source or uniform, white-balanced lighting, leaving many of these complexities unaddressed. In this paper, we introduce CL3AN, the first large-scale, high-resolution dataset of its kind designed to facilitate the restoration of images captured under multiple Colored Light sources to their Ambient-Normalized counterparts. Through benchmarking, we find that leading approaches often produce artifacts--such as illumination inconsistencies, texture leakage, and color distortion--primarily due to their limited ability to precisely disentangle illumination from reflectance. Motivated by this insight, we achieve such a desired decomposition through a novel learning framework that leverages explicit chromaticity-luminance components guidance, drawing inspiration from the principles of the Retinex model. Extensive evaluations on existing benchmarks and our dataset demonstrate the effectiveness of our approach, showcasing enhanced robustness under non-homogeneous color lighting and material-specific reflectance variations, all while maintaining a highly competitive computational cost. The benchmark, codes, and models are available at www.github.com/fvasluianu97/RLN2.
Disentangled Clothed Avatar Generation with Layered Representation
Authors: Weitian Zhang; Yichao Yan; Sijing Wu; Manwen Liao; Xiaokang Yang
Venue: ICCV 2025 (main)

Abstract: Clothed avatar generation has wide applications in virtual and augmented reality, filmmaking, and more. While existing methods have made progress in creating animatable digital avatars, generating avatars with disentangled components (e.g., body, hair, and clothes) has long been a challenge. In this paper, we propose LayerAvatar, a novel feed-forward diffusion-based method capable of generating high-quality component-disentangled clothed avatars in seconds. We propose a layered UV feature plane representation, where components are distributed in different layers of the Gaussian-based UV feature plane with corresponding semantic labels. This representation can be effectively learned with current feed-forward generation pipelines, facilitating component disentanglement and enhancing details of generated avatars. Based on the well-designed representation, we train a single-stage diffusion model and introduce constrain terms to mitigate the severe occlusion issue of the innermost human body layer. Extensive experiments demonstrate the superior performances of our method in generating highly detailed and disentangled clothed avatars. In addition, we explore its applications in component transfer. The project page is available at https://olivia23333.github.io/LayerAvatar.
Teleportraits: Training-Free People Insertion into Any Scene
Authors: Jialu Gao; K J Joseph; Fernando De La Torre
Venue: ICCV 2025 (main)

Abstract: The task of realistically inserting a human from a reference image into a background scene is highly challenging, requiring the model to (1) determine the correct location and poses of the person and (2) perform high-quality personalization conditioned on the background. Previous approaches often treat them as separate problems, overlooking their interconnections, and typically rely on training to achieve high performance. In this work, we introduce a unified training-free pipeline that leverages pre-trained text-to-image diffusion models. We show that diffusion models inherently possess the knowledge to place people in complex scenes without requiring task-specific training. By combining inversion techniques with classifier-free guidance, our method achieves affordance-aware global editing, seamlessly inserting people into scenes. Furthermore, our proposed mask-guided self-attention mechanism ensures high-quality personalization, preserving the subject's identity, clothing, and body features from just a single reference image. To the best of our knowledge, we are the first to perform realistic human insertions into scenes in a training-free manner and achieve state-of-the-art results in diverse composite scene images with excellent identity preservation in backgrounds and subjects.
DepthSync: Diffusion Guidance-Based Depth Synchronization for Scale- and Geometry-Consistent Video Depth Estimation
Authors: Yue-Jiang Dong; Wang Zhao; Jiale Xu; Ying Shan; Song-Hai Zhang
Venue: ICCV 2025 (main)

Abstract: Diffusion-based video depth estimation methods have achieved remarkable success with strong generalization ability. However, predicting depth for long videos remains challenging. Existing methods typically split videos into overlapping sliding windows, leading to accumulated scale discrepancies across different windows, particularly as the number of windows increases. Additionally, these methods rely solely on 2D diffusion priors, overlooking the inherent 3D geometric structure of video depths, which results in geometrically inconsistent predictions.In this paper, we propose DepthSync, a novel, training-free framework using diffusion guidance to achieve scale- and geometry-consistent depth predictions for long videos. Specifically, we introduce scale guidance to synchronize the depth scale across windows and geometry guidance to enforce geometric alignment within windows based on the inherent 3D constraints in video depths. These two terms work synergistically, steering the denoising process toward consistent depth predictions. Experiments on various datasets validate the effectiveness of our method in producing depth estimates with improved scale and geometry consistency, particularly for long videos.