GenAI4ELab/papercli-papers huggingface dataset:
FlowStyler: Artistic Video Stylization via Transformation Fields Transports
Authors: Yuning Gong; Jiaming Chen; Xiaohua Ren; Yuanjun Liao; Yanci Zhang
Venue: ICCV 2025 (main)

Abstract: Contemporary video stylization approaches struggle to achieve artistic stylization while preserving temporal consistency. While generator-based methods produce visually striking stylized results, they suffer from flickering artifacts in dynamic motion scenarios and require prohibitive computational resources. Conversely, non-generative techniques frequently show either temporal inconsistency or inadequate style preservation.We address these limitations by adapting the physics-inspired transport principles from the Transport-based Neural Style Transfer (TNST) framework (originally developed for volumetric fluid stylization) to enforce inter-frame consistency in video stylization.Our framework employs two complementary transformation fields for artistic stylization: a geometric stylization velocity field governing deformation and an orthogonality-regularized color transfer field managing color adaptations. We further strengthen temporal consistency through two key enhancements to our field architecture: a momentum-preserving strategy mitigating vibration artifacts, and an occlusion-aware temporal lookup strategy addressing motion trailing artifacts. Extensive experiments demonstrate FlowStyler's superior performance across dual dimensions: Compared to generator-based approaches, we achieve 4xlower short-term warping errors, while maintaining comparable style fidelity; Against non-generative methods, FlowStyler attains 22% higher style fidelity with slightly improved temporal stability.
7DGS: Unified Spatial-Temporal-Angular Gaussian Splatting
Authors: Zhongpai Gao; Benjamin Planche; Meng  Zheng; Anwesa  Choudhuri; Terrence  Chen; Ziyan Wu
Venue: ICCV 2025 (main)

Abstract: Real-time rendering of dynamic scenes with view-dependent effects remains a fundamental challenge in computer graphics. While recent advances in Gaussian Splatting have shown promising results separately handling dynamic scenes (4DGS) and view-dependent effects (6DGS), no existing method unifies these capabilities while maintaining real-time performance. We present 7D Gaussian Splatting (7DGS), a unified framework representing scene elements as seven-dimensional Gaussians spanning position (3D), time (1D), and viewing direction (3D). Our key contribution is an efficient conditional slicing mechanism that transforms 7D Gaussians into view- and time-conditioned 3D Gaussians, maintaining compatibility with existing 3D Gaussian Splatting pipelines while enabling joint optimization. Experiments demonstrate that 7DGS outperforms prior methods by up to 7.36 dB in PSNR while achieving real-time rendering (401 FPS) on challenging dynamic scenes with complex view-dependent effects.
Robust and Efficient 3D Gaussian Splatting for Urban Scene Reconstruction
Authors: Zhensheng Yuan; Haozhi Huang; Zhen Xiong; Di Wang; Guanghua Yang
Venue: ICCV 2025 (main)

Abstract: We present a framework that enables fast reconstruction and real-time rendering of urban-scale scenes while maintaining robustness against appearance variations across multi-view captures. Our approach begins with scene partitioning for parallel training, employing a visibility-based image selection strategy to optimize training efficiency. A controllable level-of-detail (LOD) strategy explicitly regulates Gaussian density under a user-defined budget, enabling efficient training and rendering while maintaining high visual fidelity. The appearance transformation module mitigates the negative effects of appearance inconsistencies across images while enabling flexible adjustments. Additionally, we utilize enhancement modules, such as depth regularization, scale regularization, and antialiasing, to improve reconstruction fidelity. Experimental results demonstrate that our method effectively reconstructs urban-scale scenes and outperforms previous approaches in both efficiency and quality. The source code is available at: https://yzslab.github.io/REUrbanGS.
DWIM: Towards Tool-aware Visual Reasoning via Discrepancy-aware Workflow Generation & Instruct-Masking Tuning
Authors: Fucai Ke; Vijay Kumar B G; Xingjian Leng; Zhixi Cai; Zaid Khan; Weiqing Wang; Pari Delir Haghighi; Hamid Rezatofighi; Manmohan Chandraker
Venue: ICCV 2025 (main)

Abstract: Visual reasoning (VR), which is crucial in many fields for enabling human-like visual understanding, remains highly challenging. Recently, compositional visual reasoning approaches, which leverage the reasoning abilities of large language models (LLMs) with integrated tools to solve problems, have shown promise as more effective strategies than end-to-end VR methods. However, these approaches face limitations, as frozen LLMs lack tool awareness in VR, leading to performance bottlenecks. While leveraging LLMs for reasoning is widely used in other domains, they are not directly applicable to VR due to limited training data, imperfect tools that introduce errors and reduce data collection efficiency in VR, and challenging in fine-tuning on noisy workflows. To address these challenges, we propose DWIM: i) Discrepancy-aware training Workflow generation, which assesses tool usage and extracts more viable workflows for training; and ii) Instruct-Masking fine-tuning, which guides the model to only clone effective actions, enabling the generation of more practical solutions. Our experiments demonstrate that DWIM achieves state-of-the-art performance across various VR tasks, exhibiting strong generalization on multiple widely-used datasets.
Iris: Breaking GUI Complexity with Adaptive Focus and Self-Refining
Authors: Zhiqi Ge; Juncheng Li; Xinglei Pang; Minghe Gao; Kaihang Pan; Wang Lin; Hao Fei; Wenqiao Zhang; Siliang Tang; Yueting Zhuang
Venue: ICCV 2025 (main)

Abstract: Digital agents are increasingly employed to automate tasks in interactive digital environments such as web pages, software applications, and operating systems. While text-based agents built on Large Language Models (LLMs) often require frequent updates due to platform-specific APIs, visual agents leveraging Multimodal Large Language Models (MLLMs) offer enhanced adaptability by interacting directly with Graphical User Interfaces (GUIs). However, these agents face significant challenges in visual perception, particularly when handling high-resolution, visually complex digital environments. This paper introduces Iris, a foundational visual agent that addresses these challenges through two key innovations: Information-Sensitive Cropping (ISC) and Self-Refining Dual Learning (SRDL). ISC dynamically identifies and prioritizes visually dense regions using an edge detection algorithm, enabling efficient processing by allocating more computational resources to areas with higher information density. SRDL enhances the agent's ability to handle complex tasks by leveraging a dual-learning loop, where improvements in referring (describing UI elements) reinforce grounding (locating elements) and vice versa, all without requiring additional annotated data. Empirical evaluations demonstrate that Iris achieves state-of-the-art performance across multiple benchmarks with only 850K GUI annotations, outperforming methods using 10x more training data. These improvements further translate to significant gains in both web and OS agent downstream tasks.
Rethink Sparse Signals for Pose-guided Text-to-image Generation
Authors: Wenjie Xuan; Jing Zhang; Juhua Liu; Bo Du; Dacheng Tao
Venue: ICCV 2025 (main)

Abstract: Recent works favored dense signals (e.g., depth, DensePose), as an alternative to sparse signals (e.g., OpenPose), to provide detailed spatial guidance for pose-guided text-to-image generation. However, dense representations raised new challenges including editing difficulties and potential inconsistencies with textual prompts. This fact motivates us to revisit sparse signals for pose guidance, owing to their simplicity and shape-agnostic nature, which remains underexplored. This paper proposes a novel Spatial-Pose ControlNet (SP-Ctrl), equipping sparse signals with robust controllability for pose-guided image generation. Specifically, we extend OpenPose to a learnable spatial representation, making keypoint embeddings discriminative and expressive. Additionally, we introduce keypoint concept learning, which encourages keypoint tokens to attend to the spatial positions of each keypoint, thus improving pose alignment. Experiments on animal- and human-centric image generation tasks demonstrate that our method outperforms recent spatially controllable text-to-image generation approaches under the sparse-pose guidance and even matches the performance of dense signal-based methods. Moreover, SP-Ctrl shows promising capabilities in diverse and cross-species generation through sparse signals. Code is available at https://github.com/DREAMXFAR/SP-Ctrl.
RoboTrom-Nav: A Unified Framework for Embodied Navigation Integrating Perception, Planning, and Prediction
Authors: Yufeng Zhong; Chengjian Feng; Feng Yan; Fanfan Liu; Liming Zheng; Lin Ma
Venue: ICCV 2025 (main)

Abstract: In language-guided visual navigation, agents locate target objects in unseen environments using natural language instructions. For reliable navigation in unfamiliar scenes, agents should possess strong perception, planning, and prediction capabilities. Additionally, when agents revisit previously explored areas during long-term navigation, they may retain irrelevant and redundant historical perceptions, leading to suboptimal results. In this work, we propose RoboTron-Nav, a unified framework that integrates  p erception,  p lanning, and  p rediction capabilities through multitask collaborations on navigation and embodied question answering tasks, thereby enhancing navigation performances. Furthermore, RoboTron-Nav employs an adaptive 3D-aware history sampling strategy to effectively and efficiently utilize historical observations. By leveraging large language model, RoboTron-Nav comprehends diverse commands and complex visual scenes, resulting in appropriate navigation actions. RoboTron-Nav achieves an 81.1% success rate in object goal navigation on the \mathrm CHORES -\mathbb S  benchmark, setting a new state-of-the-art performance.
PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-Resolution
Authors: Yong Liu; Hang Dong; Jinshan Pan; Qingji Dong; Kai Chen; Rongxiang Zhang; Lean Fu; Fei Wang
Venue: ICCV 2025 (main)

Abstract: While diffusion models significantly improve the perceptual quality of super-resolved images, they usually require a large number of sampling steps, resulting in high computational costs and long inference times. Recent efforts have explored reasonable acceleration schemes by reducing the number of sampling steps. However, these approaches treat all regions of the image equally, overlooking the fact that regions with varying levels of reconstruction difficulty require different sampling steps. To address this limitation, we propose PatchScaler, an efficient patch-independent diffusion pipeline for single image super-resolution. Specifically, PatchScaler introduces a Patch-adaptive Group Sampling (PGS) strategy that groups feature patches by quantifying their reconstruction difficulty and establishes shortcut paths with different sampling configurations for each group. To further optimize the patch-level reconstruction process of PGS, we propose a texture prompt that provides rich texture conditional information to the diffusion model. The texture prompt adaptively retrieves texture priors for the target patch from a common reference texture memory. Extensive experiments show that our PatchScaler achieves favorable performance in both quantitative and qualitative evaluations, while significantly speeding up inference. Project page: https://github.com/yongliuy/PatchScaler.
RefEdit: A Benchmark and Method for Improving Instruction-based Image Editing Model on Referring Expressions
Authors: Bimsara Pathiraja; Maitreya Patel; Shivam Singh; Yezhou Yang; Chitta Baral
Venue: ICCV 2025 (main)

Abstract: Despite recent advances in inversion and instruction-based image editing, existing approaches primarily excel at editing single, prominent objects but significantly struggle when applied to complex scenes containing multiple entities. To quantify this gap, we first introduce **`RefEdit-Bench`**, a rigorous real-world benchmark rooted in RefCOCO, where even baselines trained on millions of samples perform poorly.To overcome this limitation, we introduce **`RefEdit`** -- an instruction-based editing model trained on our scalable synthetic data generation pipeline.Our **`RefEdit`**, trained on only 20,000 editing triplets, outperforms the Flux/SD3 model-based baselines trained on millions of data. Extensive evaluations across various benchmarks demonstrate that our model not only excels in referring expression tasks but also enhances performance on traditional benchmarks, achieving state-of-the-art results comparable to closed-source methods.We will release our code, data, and checkpoints.
ETA: Energy-based Test-time Adaptation for Depth Completion
Authors: Younjoon Chung; Hyoungseob Park; Patrick Rim; Xiaoran Zhang; Jihe He; Ziyao Zeng; Safa Cicek; Byung-Woo Hong; James S. Duncan; Alex Wong
Venue: ICCV 2025 (main)

Abstract: We propose a method of adapting pretrained depth completion models to test time data in an unsupervised manner. Depth completion models are (pre)trained to produce dense depth maps from pairs of RGB image and sparse depth maps in ideal capture conditions (source domain), e.g., well-illuminated, high signal-to-noise. When models are transferred to capture conditions out of ideal case (target domain), they produce erroneous output dense depth maps due to the covariate shift. To identify cases of out-of-distribution errors, we propose an learn an energy model in the source domain that assigns scalars that represent the likelihood of the output dense depth maps. This energy model is further used to adapt the pretrained depth completion models at test time, leading to our method: Energy-based Test-time Adaptation (ETA). ETA can localize regions of errors as high energy; test-time adaptation involves updating the model weights to minimize the energy, which in turn mitigates the covariate shift. We evaluate ETA across 3 indoor and 3 outdoor datasets, where ETA improves over the previous state of the art by an average of 6.94% on outdoor and 10.23% on indoor settings.
Amodal3R: Amodal 3D Reconstruction from Occluded 2D Images
Authors: Tianhao Wu; Chuanxia Zheng; Frank Guan; Andrea Vedaldi; Tat-Jen Cham
Venue: ICCV 2025 (main)

Abstract: Most existing image-to-3D models assume that objects are fully visible, ignoring occlusions that commonly occur in real-world scenarios. In this paper, we introduce Amodal3R, a conditional image-to-3D model designed to reconstruct plausible 3D geometry and appearance from partial observations. We extend a "foundation" 3D generator by introducing a visible mask-weighted attention mechanism and an occlusion-aware attention layer that explicitly leverages visible and occlusion priors to guide the reconstruction process. We demonstrate that, by training solely on synthetic data, Amodal3R learns to recover full 3D objects even in the presence of occlusions in real scenes. It substantially outperforms state-of-the-art methods that independently perform 2D amodal completion followed by 3D reconstruction, thereby establishing a new benchmark for occlusion-aware 3D reconstruction.
A Unified Framework for Motion Reasoning and Generation in Human Interaction
Authors: Jeongeun Park; Sungjoon Choi; Sangdoo Yun
Venue: ICCV 2025 (main)

Abstract: Recent advancements in large language models (LLMs) have significantly improved their ability to generate natural and contextually appropriate text, enabling more human-like interactions. However, understanding and generating interactive human-like motion, especially involving coordinated interactive motion, remains a challenging problem due to its inherent complexity. To address this, we present MoLaM, the Interactive Motion-LAnguage Model, a unified architecture that jointly processes language and motion modalities for understanding, generating, and controlling interactive motions in multi-turn conversational settings. Unlike prior approaches limited to uni-directional tasks (e.g., text-to-motion or motion-to-text), MoLaM supports a wide range of bi-directional and multi-turn tasks. We also introduce Inter-MT2, a large-scale instruction tuning dataset containing 82.7K multi-turn interactive motion instructions and 153K motion samples, covering tasks such as editing, question answering, and story generation. Leveraging LLMs and motion diffusion models, MoLaM demonstrates strong performance across five interactive motion tasks: motion-to-text, text-to-motion, reaction generation, motion editing, and motion reasoning, outperforming or matching task-specific baselines with a single, unified model.
Dynamic Group Detection using VLM-augmented Temporal Groupness Graph
Authors: Kaname Yokoyama; Chihiro Nakatani; Norimichi Ukita
Venue: ICCV 2025 (main)

Abstract: This paper proposes dynamic human group detection in videos. For detecting complex groups, not only the local appearance features of in-group members but also the global context of the scene are important. Such local and global appearance features in each frame are extracted using a Vision-Language Model (VLM) augmented for group detection in our method. For further improvement, the group structure should be consistent over time. While previous methods are stabilized on the assumption that groups are not changed in a video, our method detects dynamically changing groups by global optimization using a graph with all frames' groupness probabilities estimated by our groupness-augmented CLIP features. Our experimental results demonstrate that our method outperforms state-of-the-art group detection methods on public datasets. Code: https://github.com/irajisamurai/VLM-GroupDetection.git
SciVid: Cross-Domain Evaluation of Video Models in Scientific Applications
Authors: Yana Hasson; Pauline Luc; Liliane Momeni; Maks Ovsjanikov; Guillaume Le Moing; Alina Kuznetsova; Ira Ktena; Jennifer J. Sun; Skanda Koppula; Dilara Gokay; Joseph Heyward; Etienne Pot; Andrew Zisserman
Venue: ICCV 2025 (main)

Abstract: In recent years, there has been a proliferation of spatiotemporal foundation models in different scientific disciplines. While promising, these models are often domain-specific and are only assessed within the particular applications for which they are designed. Given that many tasks can be represented as video modeling problems, video foundation models (ViFMs) hold considerable promise as general-purpose domain-agnostic approaches. However, it is not known whether the knowledge acquired on large-scale but potentially out-of-domain data can be effectively transferred across diverse scientific disciplines, and if a single, pretrained ViFM can be competitive with domain-specific baselines. To address this, we introduce SciVid, a comprehensive benchmark comprising five *Sci*entific *Vid*eo tasks, across medical computer vision, animal behavior, and weather forecasting. We adapt six leading ViFMs to SciVid using simple trainable readout modules, establishing strong baselines and demonstrating the potential for effective transfer learning. Specifically, we show that state-of-the-art results can be obtained in several applications by leveraging the general-purpose representations from ViFM backbones. Furthermore, our results reveal the limitations of existing ViFMs, and highlight opportunities for the development of generalizable models for high-impact scientific applications. We release our code at github.com/google-deepmind/scivid to facilitate further research in the development of ViFMs.
RoboPearls: Editable Video Simulation for Robot Manipulation
Authors: Tang Tao; Likui Zhang; Youpeng Wen; Kaidong Zhang; Jia-Wang Bian; Xia Zhou; Tianyi Yan; Kun Zhan; Peng Jia; Hefeng Wu; Liang Lin; Xiaodan Liang
Venue: ICCV 2025 (main)

Abstract: The development of generalist robot manipulation policies has seen significant progress, driven by large-scale demonstration data across diverse environments. However, the high cost and inefficiency of collecting real-world demonstrations hinder the scalability of data acquisition. While existing simulation platforms enable controlled environments for robotic learning, the challenge of bridging the sim-to-real gap remains. To address these challenges, we propose RoboPearls, an editable video simulation framework for robotic manipulation. Built on 3D Gaussian Splatting (3DGS), RoboPearls enables the construction of photo-realistic, view-consistent simulations from demonstration videos, and supports a wide range of simulation operators, including various object manipulations, powered by advanced modules like Incremental Semantic Distillation (ISD) and 3D regularized NNFM Loss (3D-NNFM). Moreover, by incorporating large language models (LLMs), RoboPearls automates the simulation production process in a user-friendly manner through flexible command interpretation and execution. Furthermore, RoboPearls employs a vision-language model (VLM) to analyze robotic learning issues to close the simulation loop for performance enhancement. To demonstrate the effectiveness of RoboPearls, we conduct extensive experiments on multiple datasets and scenes, including RLBench, COLOSSEUM, Ego4D, Open X-Embodiment, and a real-world robot, which demonstrate our satisfactory simulation performance.
FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model
Authors: Yukang Cao; Chenyang Si; Jinghao Wang; Ziwei Liu
Venue: ICCV 2025 (main)

Abstract: We present FreeMorph, the first tuning-free method for image morphing that accommodates inputs with varying semantics or layouts. Unlike existing methods, which rely on fine-tuning pre-trained diffusion models and are limited by time constraints and semantic/layout discrepancies, FreeMorph delivers high-fidelity image morphing without extensive training. Despite its efficiency and potential, tuning-free methods still face challenges in maintaining high-quality image morphing due to the non-linear nature of the multi-step denoising process and bias inherited from the pre-trained diffusion model. In this paper, we introduce FreeMorph to address this challenge by integrating two key innovations. 1) We first propose a guidance-aware spherical interpolation design that incorporates the explicit guidance from the input images by modifying the self-attention modules, addressing identity loss, and ensuring directional transitions throughout the generated sequences. 2) We further introduce a step-oriented variation trend that blends self-attention modules derived from each input image to achieve controlled and consistent transitions that respect both input images. Our extensive evaluations demonstrate that FreeMorph outperforms existing methods with training that is 10X   50X faster, establishing a new state-of-the-art for image morphing.