GenAI4ELab/papercli-papers huggingface dataset:
Rethinking Bimanual Robotic Manipulation: Learning with Decoupled Interaction Framework
Authors: Jian-Jian Jiang; Xiao-Ming Wu; Yi-Xiang He; Ling-An Zeng; Yi-Lin Wei; Dandan Zhang; Wei-Shi Zheng
Venue: ICCV 2025 (main)

Abstract: Bimanual robotic manipulation is an emerging and critical topic in the robotics community. Previous works primarily rely on integrated control models that take the perceptions and states of both arms as inputs to directly predict their actions. However, we think bimanual manipulation involves not only coordinated tasks but also various uncoordinated tasks that do not require explicit cooperation during execution, such as grasping objects with the closest hand, which integrated control frameworks ignore to consider due to their enforced cooperation in the early inputs. In this paper, we propose a novel decoupled interaction framework that considers the characteristics of different tasks in bimanual manipulation. The key insight of our framework is to assign an independent model to each arm to enhance the learning of uncoordinated tasks, while introducing a selective interaction module that adaptively learns weights from its own arm to improve the learning of coordinated tasks. Extensive experiments on seven tasks in the RoboTwin dataset demonstrate that: (1) Our framework achieves outstanding performance, with a 23.5% boost over the SOTA method. (2) Our framework is flexible and can be seamlessly integrated into existing methods. (3) Our framework can be effectively extended to multi-agent manipulation tasks, achieving a 28% boost over the integrated control SOTA. (4) The performance boost stems from the decoupled design itself, surpassing the SOTA by 16.5% in success rate with only 1/6 of the model size.
LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents
Authors: Boyu Chen; Zhengrong Yue; Siran Chen; Zikang Wang; Yang Liu; Peng Li; Yali Wang
Venue: ICCV 2025 (main)

Abstract: Existing MLLMs encounter significant challenges in modeling the temporal context within long videos. Currently, mainstream Agent-based methods use external tools to assist a single MLLM in answering long video questions. Despite such tool-based support, a solitary MLLM still offers only a partial understanding of long videos, resulting in limited performance. In order to better address long video tasks, we introduce LVAgent, the first framework enabling multi-round dynamic collaboration of MLLM agents in long video understanding. Our method consists of four key steps: 1) Selection: We pre-select appropriate agents from the model library to form optimal agent teams based on different tasks. 2) Perception: We design an effective retrieval scheme for long videos, improving the coverage of critical temporal segments while maintaining computational efficiency. 3) Action: Agents answer long video questions and exchange reasons. 4) Reflection: We evaluate each agent's performance in each round of discussion and optimize the agent team for dynamic collaboration. The agents iteratively refine their answers by multi-round dynamical collaboration of MLLM agents. LVAgent is the first agent system method that outperforms all closed-source models (like GPT-4o) and open-source models (like InternVL-2.5 and Qwen2-VL) in the long video understanding tasks. Our LVAgent achieves an accuracy of 80% on four mainstream long video understanding tasks. Notably, LVAgent improves accuracy by 13.3% on LongVideoBench. Code is available at https://github.com/64327069/LVAgent.
Avat3r: Large Animatable Gaussian Reconstruction Model for High-fidelity 3D Head Avatars
Authors: Tobias Kirschstein; Javier Romero; Artem Sevastopolsky; Matthias Nießner; Shunsuke Saito
Venue: ICCV 2025 (main)

Abstract: Traditionally, creating photo-realistic 3D head avatars requires a studio-level multi-view capture setup and expensive optimization during test-time, limiting the use of digital human doubles to the VFX industry or offline renderings. To address this shortcoming, we present Avat3r, which regresses a high-quality and animatable 3D head avatar from just a few input images, vastly reducing compute requirements during inference. More specifically, we make Large Reconstruction Models animatable and learn a powerful prior over 3D human heads from a large multi-view video dataset. For better 3D head reconstructions, we employ position maps from DUSt3R and generalized feature maps from the human foundation model Sapiens. To animate the 3D head, our key discovery is that simple cross-attention to an expression code is already sufficient. Finally, we increase robustness by feeding input images with different expressions to our model during training, enabling the reconstruction of 3D head avatars from inconsistent inputs, e.g., an imperfect phone capture with accidental movement, or frames from a monocular video. We compare Avat3r with current state-of-the-art methods for few-input and single-input scenarios, and find that our method has a competitive advantage in both tasks. Finally, we demonstrate the wide applicability of our proposed model, creating 3D head avatars from images of different sources, smartphone captures, single images, and even out-of-domain inputs like antique busts.
EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis
Authors: Alexander Mai; Peter Hedman; George Kopanas; Dor Verbin; David Futschik; Qiangeng Xu; Falko Kuester; Jonathan T. Barron; Yinda Zhang
Venue: ICCV 2025 (main)

Abstract: We present Exact Volumetric Ellipsoid Rendering (EVER), a method for real-time 3D reconstruction.EVER accurately blends an unlimited number of overlapping primitives together in 3D space, eliminating the popping artifacts that 3D Gaussian Splatting (3DGS) and other related methods exhibit.EVER represents a radiance field as a set of constant-density volumetric ellipsoids, which are raytraced by intersecting each primitive twice (once upon ray entrance and another on ray exit) and accumulating the derivatives of the densities and colors along the ray.Because EVER is built around ray tracing, it also enables effects such as defocus blur and fish-eye camera distortion, while still achieving frame rates of  30 FPS at 720p on an NVIDIA RTX4090. We show that our method is more accurate on the challenging large-scale scenes from the Zip-NeRF dataset, where it achieves state of the art SSIM, even higher than Zip-NeRF.
Democratizing High-Fidelity Co-Speech Gesture Video Generation
Authors: Xu Yang; Shaoli Huang; Shenbo Xie; Xuelin Chen; Yifei Liu; Changxing Ding
Venue: ICCV 2025 (main)

Abstract: Co-speech gesture video generation aims to synthesize realistic, audio-aligned videos of speakers, complete with synchronized facial expressions and body gestures. This task presents challenges due to the significant one-to-many mapping between audio and visual content, further complicated by the scarcity of large-scale public datasets and high computational demands. We propose a lightweight framework that utilizes 2D full-body skeletons as an efficient auxiliary condition to bridge audio signals with visual outputs. Our approach introduces a diffusion model conditioned on fine-grained audio segments and a skeleton extracted from the speaker's reference image, predicting skeletal motions through skeleton-audio feature fusion to ensure strict audio coordination and body shape consistency. The generated skeletons are then fed into an off-the-shelf human video generation model with the speaker's reference image to synthesize high-fidelity videos. To democratize research, we present CSG-405--the first public dataset with 405 hours of high-resolution videos across 71 speech types, annotated with 2D skeletons and diverse speaker demographics. Experiments show that our method exceeds state-of-the-art approaches in visual quality and synchronization while generalizing across speakers and contexts. Code, models, and CSG-405 are publicly released at https://mpi-lab.github.io/Democratizing-CSG
Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction
Authors: Luyao Tang; Kunze Huang; Chaoqi Chen; Yuxuan Yuan; Chenxin Li; Xiaotong Tu; Xinghao Ding; Yue Huang
Venue: ICCV 2025 (main)

Abstract: Human perceptual systems excel at inducing and recognizing objects across both known and novel categories, a capability far beyond current machine learning frameworks. While generalized category discovery (GCD) aims to bridge this gap, existing methods predominantly focus on optimizing objective functions. We present an orthogonal solution, inspired by the human cognitive process for novel object understanding: decomposing objects into visual primitives and establishing cross-knowledge comparisons. We propose ConGCD, which establishes primitive-oriented representations through high-level semantic reconstruction, binding intra-class shared attributes via deconstruction. Mirroring human preference diversity in visual processing, where distinct individuals leverage dominant or contextual cues, we implement dominant and contextual consensus units to capture class-discriminative patterns and inherent distributional invariants, respectively. A consensus scheduler dynamically optimizes activation pathways, with final predictions emerging through multiplex consensus integration. Extensive evaluations across coarse- and fine-grained benchmarks demonstrate ConGCD's effectiveness as a consensus-aware paradigm. Code is available at: https://github.com/lytang63/ConGCD.
DiffIP: Representation Fingerprints for Robust IP Protection of Diffusion Models
Authors: Zhuoling Li; Haoxuan Qu; Jason Kuen; Jiuxiang Gu; Qiuhong Ke; Jun Liu; Hossein Rahmani
Venue: ICCV 2025 (main)

Abstract: Intellectual property (IP) protection for diffusion models is a critical concern, given the significant resources and time required for their development. To effectively safeguard the IP of diffusion models, a key step is enabling the comparison of unique identifiers (fingerprints) between suspect and victim models. However, performing robust and effective fingerprint comparisons among diffusion models remains an under-explored challenge, particularly for diffusion models that have already been released. To address this, in this work, we propose DiffIP, a novel framework for robust and effective fingerprint comparison between suspect and victim diffusion models. Extensive experiments demonstrate the efficacy of our framework.
Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning
Authors: Yue Duan; Taicai Chen; Lei Qi; Yinghuan Shi
Venue: ICCV 2025 (main)

Abstract: Semi-supervised continual learning (SSCL) seeks to leverage both labeled and unlabeled data in a sequential learning setup, aiming to reduce annotation costs while managing continual data arrival. SSCL introduces complex challenges, including ensuring effective unlabeled learning (UL), while balancing memory stability (MS) and learning plasticity (LP). Previous SSCL efforts have typically focused on isolated aspects of the three, while this work presents USP, a divide-and-conquer framework designed to synergistically enhance these three aspects: (1) Feature Space Reservation (FSR) strategy for LP, which constructs reserved feature locations for future classes by shaping old classes into an equiangular tight frame; (2) Divide-and-Conquer Pseudo-labeling (DCP) approach for UL, which assigns reliable pseudo-labels across both high- and low-confidence unlabeled data; and (3) Class-mean-anchored Unlabeled Distillation (CUD) for MS, which reuses DCP's outputs to anchor unlabeled data to stable class means for distillation to prevent forgetting. Comprehensive evaluations show USP outperforms prior SSCL methods, with gains up to 5.94% in the last accuracy, validating its effectiveness. The code is available at https://github.com/NJUyued/USP4SSCL.
All Parts Matter: A Unified Mask-Free Virtual Try-On Framework
Authors: Chenghu Du; Shengwu Xiong; Yi Rong
Venue: ICCV 2025 (main)

Abstract: Current virtual try-on methods primarily enhance performance through network optimization, employing strategies such as coarse-to-fine structures and ReferenceNet to inject clothing information. However, these methods are often constrained by the limited quantity and diversity of training samples, which ultimately restricts their potential for further improvement. To address this challenge, we propose a unified, mask-free virtual try-on framework. Our approach leverages the inherent strengths of latent diffusion models to enhance the capability of each pipeline component to accurately model the target distribution, thereby achieving superior performance. Specifically, we introduce a text-driven pseudo-input preparation strategy that significantly increases the diversity of clothing regions within the generated person pseudo-samples. This encourages the generator to focus on variations in these areas and improves the model's generalization capability. Within the generator itself, we develop a gated manipulation mechanism to prevent weight forgetting and reduce training costs. Furthermore, we incorporate a texture-aware injection module to explicitly integrate human-perceptible clothing texture information into the generation process. During inference, we propose a refining conditional inference strategy that mitigates the randomness introduced by Gaussian noise. This effectively preserves identity information and fine clothing details in the final results. Extensive experiments demonstrate that our method outperforms existing state-of-the-art virtual try-on approaches.
CogNav: Cognitive Process Modeling for Object Goal Navigation with LLMs
Authors: Yihan Cao; Jiazhao Zhang; Zhinan Yu; Shuzhen Liu; Zheng Qin; Qin Zou; Bo Du; Kai Xu
Venue: ICCV 2025 (main)

Abstract: Object goal navigation (ObjectNav) is a fundamental task in embodied AI, requiring an agent to locate a target object in previously unseen environments. This task is particularly challenging because it requires both perceptual and cognitive processes, including object recognition and decision-making. While substantial advancements in perception have been driven by the rapid development of visual foundation models, progress on the cognitive aspect remains constrained, primarily limited to either implicit learning through simulator rollouts or explicit reliance on predefined heuristic rules. Inspired by neuroscientific findings demonstrating that humans maintain and dynamically update fine-grained cognitive states during object search tasks in novel environments, we propose CogNav, a framework designed to mimic this cognitive process using large language models. Specifically, we model the cognitive process using a finite state machine comprising fine-grained cognitive states, ranging from exploration to identification. Transitions between states are determined by a large language model based on a dynamically constructed heterogeneous cognitive map, which contains spatial and semantic information about the scene being explored. Extensive evaluations on the HM3D, MP3D, and RoboTHOR benchmarks demonstrate that our cognitive process modeling significantly improves the success rate of ObjectNav at least by relative 14% over the state-of-the-arts. The code has been submitted and will be released upon acceptance.
Spatio-Spectral Pattern Illumination for Direct and Indirect Separation from a Single Hyperspectral Image
Authors: Shin Ishihara; Imari Sato
Venue: ICCV 2025 (main)

Abstract: Hyperspectral imaging has proven effective for appearance inspection because it can identify material compositions and reveal hidden features. Similarly, direct/indirect separation provides essential information about surface appearance and internal conditions, including layer structures and scattering behaviors. This paper presents a novel illumination system incorporating dispersive optics to unify both advantages for scene analysis. In general, achieving distinct direct/indirect separation requires multiple images with varying patterns. In a hyperspectral scenario, using a hyperspectral camera or tunable filters extends exposure and measurement times, hindering practical application. Our proposed system enables the illumination of a wavelength-dependent, spatially shifted pattern. With proper consideration of reflectance differences, we demonstrate that robust separation of direct and indirect components for each wavelength can be achieved using a single hyperspectral image acquired under our single spatio-spectral pattern illumination.
Explaining Human Preferences via Metrics for Structured 3D Reconstruction
Authors: Jack Langerman; Denys Rozumnyi; Yuzhong Huang; Dmytro Mishkin
Venue: ICCV 2025 (main)

Abstract: "What cannot be measured cannot be improved" while likely never uttered by Lord Kelvin, summarizes effectively the driving force behind this work. This paper presents a detailed discussion of automated metrics for evaluating structured 3D reconstructions. Pitfalls of each metric are discussed, and an analysis through the lens of expert 3D modelers' preferences is presented. A set of systematic "unit tests" are proposed to empirically verify desirable properties, and context aware recommendations regarding which metric to use depending on application are provided. Finally, a learned metric distilled from human expert judgments is proposed and analyzed. The source code is available at https://github.com/s23dr/ wireframe-metrics-iccv2025.
LOCATEdit: Graph Laplacian Optimized Cross Attention for Localized Text-Guided Image Editing
Authors: Achint Soni; Meet Soni; Sirisha Rambhatla
Venue: ICCV 2025 (main)

Abstract: Text-guided image editing aims to modify specific regions of an image according to natural language instructions while maintaining the general structure and the background fidelity. Existing methods utilize masks derived from cross-attention maps generated from diffusion models to identify the target regions for modification. However, since cross-attention mechanisms focus on semantic relevance, they struggle to maintain the image integrity. As a result, these methods often lack spatial consistency, leading to editing artifacts and distortions. In this work, we address these limitations and introduce LOCATEdit, which enhances cross-attention maps through a graph-based approach utilizing self-attention-derived patch relationships to maintain smooth, coherent attention across image regions, ensuring that alterations are limited to the designated items while retaining the surrounding structure. LOCATEdit consistently and substantially outperforms existing baselines on PIE-Bench, demonstrating its state-of-the-art performance and effectiveness on various editing tasks. Code can be found on https://github.com/criticalml-uw/LOCATEdit
VistaDream: Sampling multiview consistent images for single-view scene reconstruction
Authors: Haiping Wang; Yuan Liu; Ziwei Liu; Wenping Wang; Zhen Dong; Bisheng Yang
Venue: ICCV 2025 (main)

Abstract: In this paper, we propose VistaDream, a novel framework to reconstruct a 3D scene from a single-view image. Recent diffusion models enable generating high-quality novel-view images from a single-view input image. Most existing methods only concentrate on building the consistency between the input image and the generated images while losing the consistency between the generated images. VistaDream addresses this problem by a two-stage pipeline. In the first stage, VistaDream builds a global coarse 3D scaffold by zooming out a little step with inpainted boundaries and an estimated depth map. Then, on this global scaffold, we use iterative diffusion-based RGB-D inpainting to generate novel-view images to inpaint the holes of the scaffold. In the second stage, we further enhance the consistency between the generated novel-view images by a novel training-free Multiview Consistency Sampling (MCS) that introduces multi-view consistency constraints in the reverse sampling process of diffusion models. Experimental results demonstrate that without training or fine-tuning existing diffusion models, VistaDream achieves high-quality scene reconstruction and novel view synthesis using a single-view image and outperforms baseline methods by a large margin.
LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion
Authors: Fangfu Liu; Hao Li; Jiawei Chi; Hanyang Wang; Minghui Yang; Fudong Wang; Yueqi Duan
Venue: ICCV 2025 (main)

Abstract: Recovering 3D structures with open-vocabulary scene understanding from 2D images is a fundamental but daunting task. Recent developments have achieved this by performing per-scene optimization with embedded language information. However, they heavily rely on the calibrated dense-view reconstruction paradigm, thereby suffering from severe rendering artifacts and implausible semantic synthesis when limited views are available. In this paper, we introduce a novel generative framework, coined LangScene-X, to unify and generate 3D consistent multi-modality information for reconstruction and understanding. Powered by the generative capability of creating more consistent novel observations, we can build generalizable 3D language-embedded scenes from only sparse views.Specifically, we first train a TriMap video diffusion model that can generate appearance (RGBs), geometry (normals), and semantics (segmentation maps) from sparse inputs through progressive knowledge integration. Furthermore, we propose a Language Quantized Compressor (LQC), trained on large-scale image datasets, to efficiently encode language embeddings, enabling cross-scene generalization without per-scene retraining. Finally, we reconstruct the language surface fields by aligning language information onto the surface of 3D scenes, enabling open-ended language queries. Extensive experiments on real-world data demonstrate the superiority of our LangScene-X over state-of-the-art methods in terms of quality and generalizability.
Orchid: Image Latent Diffusion for Joint Appearance and Geometry Generation
Authors: Akshay Krishnan; Xinchen Yan; Vincent Casser; Abhijit Kundu
Venue: ICCV 2025 (main)

Abstract: We introduce Orchid, a unified latent diffusion model that learns a joint appearance-geometry prior to generate color, depth, and surface normal images in a single diffusion process. This unified approach is more efficient and coherent than current pipelines that use separate models for appearance and geometry. Orchid is versatile - it directly generates color, depth, and normal images from text, supports joint monocular depth and normal estimation with color-conditioned finetuning, and seamlessly inpaints large 3D regions by sampling from the joint distribution. It leverages a novel Variational Autoencoder (VAE) that jointly encodes RGB, relative depth, and surface normals into a shared latent space, combined with a latent diffusion model that denoises these latents. Our extensive experiments demonstrate that Orchid delivers competitive performance against SOTA task-specific methods for geometry prediction, even surpassing them in normal-prediction accuracy and depth-normal consistency. It also inpaints color-depth-normal images jointly, with more qualitative realism than existing multi-step methods.