GenAI4ELab/papercli-papers huggingface dataset:
FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction
Authors: Jiale Xu; Shenghua Gao; Ying Shan
Venue: ICCV 2025 (main)

Abstract: Sparse-view reconstruction models typically require precise camera poses, yet obtaining these parameters from sparse-view images remains challenging. We introduce FreeSplatter, a scalable feed-forward framework that generates high-quality 3D Gaussians from uncalibrated sparse-view images while estimating camera parameters within seconds. Our approach employs a streamlined transformer architecture where self-attention blocks facilitate information exchange among multi-view image tokens, decoding them into pixel-aligned 3D Gaussian primitives within a unified reference frame. This representation enables both high-fidelity 3D modeling and efficient camera parameter estimation using off-the-shelf solvers. We develop two specialized variants--for object-centric and scene-level reconstruction--trained on comprehensive datasets. Remarkably, FreeSplatter outperforms several pose-dependent Large Reconstruction Models (LRMs) by a notable margin while achieving comparable or even better pose estimation accuracy compared to state-of-the-art pose-free reconstruction approach MASt3R in challenging benchmarks. Beyond technical benchmarks, FreeSplatter streamlines text/image-to-3D content creation pipelines, eliminating the complexity of camera pose management while delivering exceptional visual fidelity.
FreqPDE: Rethinking Positional Depth Embedding for Multi-View 3D Object Detection Transformers
Authors: Haisheng Su; Junjie Zhang; Feixiang Song; Sanping Zhou; Wei Wu; Junchi Yan; Nanning Zheng
Venue: ICCV 2025 (main)

Abstract: Detecting 3D objects accurately from multi-view 2D images is a challenging yet essential task in the field of autonomous driving. Current methods resort to integrating depth prediction to recover the spatial information for object query decoding, which necessitates explicit supervision from LiDAR points during the training phase. However, the predicted depth quality is still unsatisfactory such as depth discontinuity of object boundaries and indistinction of small objects, which are mainly caused by the sparse supervision of projected points and the use of high-level image features for depth prediction. Besides, cross-view consistency and scale invariance are also overlooked in previous methods. In this paper, we introduce Frequency-aware Positional Depth Embedding (FreqPDE) to equip 2D image features with spatial information for 3D detection transformer decoder, which can be obtained through three main modules. Specifically, the Frequency-aware Spatial Pyramid Encoder (FSPE) constructs a feature pyramid by combining high-frequency edge clues and low-frequency semantics from different levels respectively. Then the Cross-view Scale-invariant Depth Predictor (CSDP) estimates the pixel-level depth distribution with cross-view and efficient channel attention mechanism. Finally, the Positional Depth Encoder (PDE) combines the 2D image features and 3D position embeddings to generate the 3D depth-aware features for query decoding. Additionally, hybrid depth supervision is adopted for complementary depth learning from both metric and distribution aspects. Extensive experiments conducted on the nuScenes dataset demonstrate the effectiveness and superiority of our proposed method.
Hierarchy-Aware Pseudo Word Learning with Text Adaptation for Zero-Shot Composed Image Retrieval
Authors: Zhe Li; Lei Zhang; Zheren Fu; Kun Zhang; Zhendong Mao
Venue: ICCV 2025 (main)

Abstract: Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve the target image based on a reference image and a text describing the user's intention without training on the triplet datasets. The key to this task is to make specified changes to specific objects in the reference image based on the text. Previous works generate single or multiple pseudo words by projecting the reference image to the word embedding space. However, these methods ignore the fact that the editing objects of CIR are naturally hierarchical, and lack the ability of text adaptation, thus failing to adapt to multi-level editing needs. In this paper, we argue that the hierarchical object decomposition is the key to learning pseudo words, and propose a hierarchy-aware dynamic pseudo word learning (HIT) framework to equip with HIerarchy semantic parsing and Text-adaptive filtering. The proposed HIT enjoys several merits. First, HIT is empowered to dynamically decompose the image into different granularity of editing objects by a set of learnable group tokens as guidance, thus naturally forming the hierarchical semantic concepts. Second, the text-adaptive filtering strategy is proposed to screen out specific objects from different levels based on the text, so as to learn hierarchical pseudo words that meet diverse editing needs. Extensive experiments on three challenging benchmarks show that HIT outperforms prior state-of-the-art ones by 5%-8% in average recall.
WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image
Authors: Yuci Liang; Xinheng Lyu; Wenting Chen; Meidan Ding; Jipeng Zhang; Xiangjian He; Song Wu; Xiaohan Xing; Sen Yang; Xiyue Wang; Linlin Shen
Venue: ICCV 2025 (main)

Abstract: Recent advances in computational pathology have introduced whole slide image (WSI)-level multimodal large language models (MLLMs) for automated pathological analysis. However, current WSI-level MLLMs face two critical challenges: limited explainability in their decision-making process and insufficient attention to morphological features crucial for accurate diagnosis. To address these challenges, we first introduce WSI-Bench, a large-scale morphology-aware benchmark containing 180k VQA pairs from 9,850 WSIs across 30 cancer types, specifically designed to evaluate MLLMs' understanding of morphological characteristics crucial for accurate diagnosis. To the best of our knowledge, WSI-Bench presents the first benchmarking systematically evaluate morphological understanding capabilities in WSI analysis. To enhance the model explainability, we present WSI-LLaVA, an MLLM framework for gigapixel WSI understanding with a three-stage training strategy, which can provide detailed morphological findings to explain its final answer. For more precise model assessment in pathological contexts, we develop two specialized WSI metrics: WSI-Precision and WSI-Relevance. Extensive evaluation on WSI-Bench reveals both the capabilities and limitations of current WSI MLLMs in morphological analysis and various pathology tasks, while demonstrating WSI-LLaVA's superior performance across all capabilities.
GLEAM: Learning Generalizable Exploration Policy for Active Mapping in Complex 3D Indoor Scene
Authors: Xiao Chen; Tai Wang; Quanyi Li; Tao Huang; Jiangmiao Pang; Tianfan Xue
Venue: ICCV 2025 (main)

Abstract: Generalizable active mapping in complex unknown environments remains a critical challenge for mobile robots. Existing methods, constrained by limited training data and conservative exploration strategies, struggle to generalize across scenes with diverse layouts and complex connectivity. To enable scalable training and reliable evaluation, we present GLEAM-Bench, the first large-scale benchmark with 1,152 diverse 3D scenes from synthetic and real datasets. In this work, we propose GLEAM, a generalizable exploration policy for active mapping. Its superior generalizability comes from our semantic representations, long-term goals, and randomized strategies. It significantly outperforms state-of-the-art methods, achieving 68.16% coverage (+11.41%) with efficient trajectories, and improved mapping accuracy on 128 unseen complex scenes.
DialNav: Multi-turn Dialog Navigation with a Remote Guide
Authors: Leekyeung Han; Hyunji Min; Gyeom Hwangbo; Jonghyun Choi; Paul Hongsuck Seo
Venue: ICCV 2025 (main)

Abstract: We introduce DialNav, a novel collaborative embodied dialog task, where a navigation agent (Navigator) and a remote guide (Guide) engage in multi-turn dialog to reach a goal location. Unlike prior work, DialNav aims for holistic evaluation and requires the Guide to infer the Navigator's location, making communication essential for task success. To support this task, we collect and release Remote Assistance in Navigation (RAIN) dataset, human-human dialog paired with navigation trajectories in photorealistic environments. We design a comprehensive benchmark to evaluate both navigation and dialog, and conduct extensive experiments analyzing the impact of different Navigator and Guide models. We highlight key challenges and publicly release the dataset, code, and evaluation framework to foster future research in embodied dialog. Our code and dataset are available at: https://happilee12.github.io/DialNav.
VLDrive: Vision-Augmented Lightweight MLLMs for Efficient Language-grounded Autonomous Driving
Authors: Ruifei Zhang; Wei Zhang; Xiao Tan; Sibei Yang; Xiang Wan; Xiaonan Luo; Guanbin Li
Venue: ICCV 2025 (main)

Abstract: Recent advancements in language-grounded autonomous driving have been significantly promoted by the sophisticated cognition and reasoning capabilities of large language models (LLMs). However, current LLM-based approaches encounter critical challenges: (1) Failure analysis reveals that frequent collisions and obstructions, stemming from limitations in visual representations, remain primary obstacles to robust driving performance. (2) The substantial parameters of LLMs pose considerable deployment hurdles. To address these limitations, we introduce VLDrive, a novel approach featuring a lightweight MLLM architecture with enhanced vision components. VLDrive achieves compact visual tokens through innovative strategies, including cycle-consistent dynamic visual pruning and memory-enhanced feature aggregation. Furthermore, we propose a distance-decoupled instruction attention mechanism to improve joint visual-linguistic feature learning, particularly for long-range visual tokens. Extensive experiments conducted in the CARLA simulator demonstrate VLDrive's effectiveness. Notably, VLDrive achieves state-of-the-art driving performance while reducing parameters by 81% (from 7B to 1.3B), yielding substantial driving score improvements of 15.4%, 16.8%, and 7.6% at tiny, short, and long distances, respectively, in closed-loop evaluations. Code is available at https://github.com/ReaFly/VLDrive.
Ponimator: Unfolding Interactive Pose for Versatile Human-human Interaction Animation
Authors: Shaowei Liu; Chuan Guo; Bing Zhou; Jian Wang
Venue: ICCV 2025 (main)

Abstract: Close-proximity human-human interactive poses convey rich contextual information about interaction dynamics. Given such poses, humans can intuitively infer the context and anticipate possible past and future dynamics, drawing on strong priors of human behavior. Inspired by this observation, we propose Ponimator, a simple framework anchored on proximal interactive poses for versatile interaction animation. Our training data consists of close-contact two-person poses and their surrounding temporal context from motion-capture interaction datasets. Leveraging interactive pose priors, Ponimator employs two conditional diffusion models: (1) a pose animator that uses the temporal prior to generate dynamic motion sequences from interactive poses, and (2) a pose generator that applies the spatial prior to synthesize interactive poses from a single pose, text, or both when interactive poses are unavailable. Collectively, Ponimator supports diverse tasks, including image-based interaction animation, reaction animation, and text-to-interaction synthesis, facilitating the transfer of interaction knowledge from high-quality mocap data to open-world scenarios. Empirical experiments across diverse datasets and applications demonstrate the universality of the pose prior and the effectiveness and robustness of our framework. Codes and video visualization can be found at https://stevenlsw.github.io/ponimator/
Streamlining Image Editing with Layered Diffusion Brushes
Authors: Peyman Gholami; Robert Xiao
Venue: ICCV 2025 (main)

Abstract: Denoising diffusion models have emerged as powerful tools for image manipulation, yet interactive, localized editing workflows remain underdeveloped. We introduce Layered Diffusion Brushes (LDB), a novel training-free framework that enables interactive, layer-based editing using standard diffusion models. LDB defines each "layer" as a self-contained set of parameters guiding the generative process, enabling independent, non-destructive, and fine-grained prompt-guided edits, even in overlapping regions. LDB leverages a unique intermediate latent caching approach to reduce each edit to only a few denoising steps, achieving 140 ms per edit on consumer GPUs. An editor implementing LDB, incorporating familiar layer concepts, was evaluated via user study and quantitative metrics. Results demonstrate LDB's superior speed alongside comparable or improved image quality, background preservation, and edit fidelity relative to state-of-the-art methods across various sequential image manipulation tasks. The findings highlight LDB's ability to significantly enhance creative workflows by providing an intuitive and efficient approach to diffusion-based image editing and its potential for expansion into related subdomains, such as video editing.
ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation
Authors: Daniel Winter; Asaf Shul; Matan Cohen; Dana Berman; Yael Pritch; Alex Rav-Acha; Yedid Hoshen
Venue: ICCV 2025 (main)

Abstract: This paper introduces a tuning-free method for both object insertion and subject-driven generation. The task involves composing an object, given multiple views, into a scene specified by either an image or text. Existing methods struggle to fully meet the task's challenging objectives: (i) seamlessly composing the object into the scene with photorealistic pose and lighting, and (ii) preserving the object's identity. We hypothesize that achieving these goals requires large-scale supervision, but manually collecting sufficient data is simply too expensive. The key observation in this paper is that many mass-produced objects recur across multiple images of large unlabeled datasets, in different scenes, poses, and lighting conditions. We use this observation to create massive supervision by retrieving sets of diverse views of the same object. This powerful paired dataset enables us to train a straightforward text-to-image diffusion architecture to map the object and scene descriptions to the composite image. We compare our method, ObjectMate, with state-of-the-art methods for object insertion and subject-driven generation, using a single or multiple references. Empirically, ObjectMate achieves superior identity preservation and more photorealistic composition. Differently from many other multi-reference methods, ObjectMate does not require slow test-time tuning.
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
Authors: Yi Liu; Shengqian Li; Zuzeng Lin; Feng Wang; Si Liu
Venue: ICCV 2025 (main)

Abstract: The current conditional autoregressive image generation methods have shown promising results, yet their potential remains largely unexplored in the practical unsupervised image translation domain, which operates without explicit cross-domain correspondences.A critical limitation stems from the discrete quantization inherent in traditional Vector Quantization-based frameworks, which disrupts gradient flow between the Variational Autoencoder decoder and causal Transformer, impeding end-to-end optimization during adversarial training in image space.To tackle this issue, we propose using Softmax Relaxed Quantization, a novel approach that reformulates codebook selection as a continuous probability mixing process via Softmax, thereby preserving gradient propagation. Building upon this differentiable foundation, we introduce CycleVAR, which reformulates image-to-image translation as image-conditional visual autoregressivegeneration by injecting multi-scale source image tokens as contextual prompts, analogous to prefix-based conditioning in language models.CycleVAR exploits two modes to generate the target image tokens, including (1) serial multi-step generation enabling iterative refinement across scales and (2) parallel one-step generation synthesizing all resolution outputs in a single forward pass.Experimental findings indicate that the parallel one-step generation mode attains superior translation quality with quicker inference speed than the serial multi-step mode in unsupervised scenarios.Furthermore, both quantitativeand qualitative results indicate that CycleVAR surpasses previous state-of-the-artunsupervised image translation models, e.g., CycleGAN-Turbo.
Synthetic Video Enhances Physical Fidelity in Video Synthesis
Authors: Qi Zhao; Xingyu Ni; Ziyu Wang; Feng Cheng; Ziyan Yang; Lu Jiang; Bohan Wang
Venue: ICCV 2025 (main)

Abstract: We investigate how to enhance the physical fidelity of video generation models by leveraging synthetic videos generated via standard computer graphics techniques. These rendered videos respect real-world physics -- such as maintaining 3D consistency -- thereby serving as a valuable resource that can potentially improve video generation models. To harness this potential, we propose a solution that curates and integrates synthetic data while introducing a method to transfer its physical realism to the model, minimizing unwanted artifacts. Through experiments on three representative tasks emphasizing physical consistency, we demonstrate its effectiveness in enhancing physical fidelity. While our model still lacks a deep understanding of physics, our work offers one of the first empirical demonstrations that synthetic video enhances physical fidelity in video synthesis.
Enhancing Partially Relevant Video Retrieval with Hyperbolic Learning
Authors: Jun Li; Jinpeng Wang; Chaolei Tan; Niu Lian; Long Chen; Yaowei Wang; Min Zhang; Shu-Tao Xia; Bin Chen
Venue: ICCV 2025 (main)

Abstract: Partially Relevant Video Retrieval (PRVR) addresses the critical challenge of matching untrimmed videos with text queries describing only partial content. Existing methods suffer from geometric distortion in Euclidean space that sometimes misrepresents the intrinsic hierarchical structure of videos and overlooks certain hierarchical semantics, ultimately leading to suboptimal temporal modeling. To address this issue, we propose the first hyperbolic modeling framework for PRVR, namely HLFormer, which leverages hyperbolic space learning to compensate for the suboptimal hierarchical modeling capabilities of Euclidean space. Specifically, HLFormer integrates the Lorentz Attention Block and Euclidean Attention Block to encode video embeddings in hybrid spaces, using the Mean-Guided Adaptive Interaction Module to dynamically fuse features. Additionally, we introduce a Partial Order Preservation Loss to enforce "\text text  \prec \text video " hierarchy through Lorentzian cone constraints. This approach further enhances cross-modal matching by reinforcing partial relevance between video content and text queries. Extensive experiments show that HLFormer outperforms state-of-the-art methods. Code is released at \href https://github.com/lijun2005/ICCV25-HLFormer  https://github.com/lijun2005/ICCV25-HLFormer .
Ultra High-Resolution Image Inpainting with Patch-Based Content Consistency Adapter
Authors: Jianhui Zhang; Shen Cheng; Qirui Sun; Jia Liu; Wang Luyang; Chaoyu Feng; Chen Fang; Lei Lei; Jue Wang; Shuaicheng Liu
Venue: ICCV 2025 (main)

Abstract: In this work, we present Patch-Adapter, an effective framework for high-resolution text-guided image inpainting. Unlike existing methods limited to lower resolutions, our approach achieves 4K+ resolution while maintaining precise content consistency and prompt alignment--two critical challenges in image inpainting that intensify with increasing resolution and texture complexity.Patch-Adapter leverages a two-stage adapter architecture to scale the Diffusion models's resolution from 1K to 4K+ without requiring structural overhauls:(1)Dual Context Adapter: Learns coherence between masked and unmasked regions at reduced resolutions to establish global structural consistency.(2)Reference Patch Adapter: Implements a patch-level attention mechanism for full-resolution inpainting, preserving local detail fidelity through adaptive feature fusion.This dual-stage architecture uniquely addresses the scalability gap in high-resolution inpainting by decoupling global semantics from localized refinement. Experiments demonstrate that Patch-Adapter not only resolves artifacts common in large-scale inpainting but also achieves state-of-the-art performance on the OpenImages and photo-concept-bucket datasets, outperforming existing methods in both perceptual quality and text-prompt adherence. The code is available at: https://github.com/Roveer/Patch-Based-Adapter
OURO: A Self-Bootstrapped Framework for Enhancing Multimodal Scene Understanding
Authors: Tianrun Xu; Guanyu Chen; Ye Li; Yuxin Xi; Zeyu Mu; Ruichen Wang; Tianren Zhang; Haichuan Gao; Feng Chen
Venue: ICCV 2025 (main)

Abstract: Multimodal large models have made significant progress, yet fine-grained understanding of complex scenes remains a challenge. High-quality, large-scale vision-language datasets are essential for addressing this issue. However, existing methods often rely on labor-intensive manual annotations or closed-source models with optimal performance, making large-scale data collection costly. To overcome these limitations, we propose a self-bootstrapped training pipeline that leverages the model's own multimodal capabilities to recursively refine its understanding. By decomposing existing multimodal data into localized sub-regions and generating hierarchical scene descriptions and multi-faceted question-answer pairs, we construct a dataset based on 1.4M image-task instances. We further utilize this dataset to train the base model, significantly enhancing its ability to interpret complex visual scenes and perform various vision-related tasks. Our OURO model, fine-tuned on Qwen2-VL-7B-Instruct using LoRA, achieves substantial improvements over both the base model and similarly-sized counterparts across multiple multimodal benchmarks. Our self-bootstrapped training pipeline offers a novel paradigm for the continuous improvement of multimodal models. Code and datasets are available at https://github.com/tinnel123666888/OURO.git.
DiGA3D: Coarse-to-Fine Diffusional Propagation of Geometry and Appearance for Versatile 3D Inpainting
Authors: Jingyi Pan; Dan Xu; Qiong Luo
Venue: ICCV 2025 (main)

Abstract: Developing a unified pipeline that enables users to remove, re-texture, or replace objects in a versatile manner is crucial for text-guided 3D inpainting. However, there are still challenges in performing multiple 3D inpainting tasks within a unified framework: 1) Single reference inpainting methods lack robustness when dealing with views that are far from the reference view; 2) Appearance inconsistency arises when independently inpainting multi-view images with 2D diffusion priors; 3) Geometry inconsistency limits performance when there are significant geometric changes in the inpainting regions. To tackle these challenges, we introduce DiGA3D, a novel and versatile 3D inpainting pipeline that leverages diffusion models to propagate consistent appearance and geometry in a coarse-to-fine manner. First, DiGA3D develops a robust strategy for selecting multiple reference views to reduce errors during propagation. Next, DiGA3D designs an Attention Feature Propagation (AFP) mechanism that propagates attention features from the selected reference views to other views via diffusion models to maintain appearance consistency. Furthermore, DiGA3D introduces a Texture-Geometry Score Distillation Sampling (TG-SDS) loss to further improve the geometric consistency of inpainted 3D scenes. Extensive experiments on multiple 3D inpainting tasks demonstrate the effectiveness of our method. The project page is available at https://rorisis.github.io/DiGA3D/.