GenAI4ELab/papercli-papers huggingface dataset:
TokensGen: Harnessing Condensed Tokens for Long Video Generation
Authors: Wenqi Ouyang; Zeqi Xiao; Danni Yang; Yifan Zhou; Shuai Yang; Lei Yang; Jianlou Si; Xingang Pan
Venue: ICCV 2025 (main)

Abstract: Generating consistent long videos is a complex challenge: while diffusion-based generative models generate visually impressive short clips, extending them to longer durations often leads to memory bottlenecks and long-term inconsistency. In this paper, we propose TokensGen, a novel two-stage framework that leverages condensed tokens to address these issues. Our method decomposes long video generation into three core tasks: (1) inner-clip semantic control, (2) long-term consistency control, and (3) inter-clip smooth transition. First, we train To2V (Token-to-Video), a short video diffusion model guided by text and video tokens, with a Video Tokenizer that condenses short clips into semantically rich tokens. Second, we introduce T2To (Text-to-Token), a video token diffusion transformer that generates all tokens at once, ensuring global consistency across clips. Finally, during inference, an adaptive FIFO-Diffusion strategy seamlessly connects adjacent clips, reducing boundary artifacts and enhancing smooth transitions.Experimental results demonstrate that our approach significantly enhances long-term temporal and content coherence without incurring prohibitive computational overhead. By leveraging condensed tokens and pre-trained short video models, our method provides a scalable, modular solution for long video generation, opening new possibilities for storytelling, cinematic production, and immersive simulations.
Cross-View Isolated Sign Language Recognition via View Synthesis and Feature Disentanglement
Authors: Xin Shen; Xinyu Wang; Lei Shen; Kaihao Zhang; Xin Yu
Venue: ICCV 2025 (main)

Abstract: Cross-view isolated sign language recognition (CV-ISLR) addresses the challenge of identifying isolated signs from viewpoints unseen during training, a problem aggravated by the scarcity of multi-view data in existing benchmarks. To bridge this gap, we introduce a novel two-stage framework comprising View Synthesis and Contrastive Multi-task View-Semantics Recognition. In the View Synthesis stage, we simulate unseen viewpoints by extracting 3D keypoints from the front-view training dataset and synthesizing common-view 2D skeleton sequences with virtual camera rotation, which enriches view diversity without the cost of multi-camera setups. However, direct training on these synthetic samples leads to limited improvement, as viewpoint-specific and semantics-specific features remain entangled. To overcome this drawback, we present a Contrastive Multi-task View-Semantics Recognition (CMVSR) module that disentangles viewpoint-dependent features from sign semantics. In this way, CMVSR obtains view-invariant representations of the sign video, leading to robust recognition performance against various camera viewpoints. We evaluate our approach on the MM-WLAuslan dataset, the first benchmark for CV-ISLR, and on our extended protocol MTV-Test that includes additional multi-view data captured in the wild. Experimental results demonstrate that our method not only improves the accuracy of front-view skeleton-based isolated sign language recognition, but also exhibits superior generalization to novel viewpoints.
R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization
Authors: Yi Yang; Xiaoxuan He; Hongkun Pan; Xiyan Jiang; Yan Deng; Xingtao Yang; Haoyu Lu; Dacheng Yin; Fengyun Rao; Minfeng Zhu; Bo Zhang; Wei Chen
Venue: ICCV 2025 (main)

Abstract: Large Language Models have demonstrated remarkable reasoning capability in complex textual tasks. However, multimodal reasoning, which requires integrating visual and textual information, remains a significant challenge. Existing visual-language models often struggle to effectively analyze and reason visual content, resulting in suboptimal performance on complex reasoning tasks. Moreover, the absence of comprehensive benchmarks hinders the accurate assessment of multimodal reasoning capabilities. In this paper, we introduce R1-Onevision, a multimodal reasoning model designed to bridge the gap between visual perception and deep reasoning. To achieve this, we propose a cross-modal reasoning pipeline that transforms images into formal textual representations, enabling precise language-based reasoning. Leveraging this pipeline, we construct the R1-Onevision dataset which provides detailed, step-by-step multimodal reasoning annotations across diverse domains. We further develop the R1-Onevision model through supervised fine-tuning and reinforcement learning to cultivate advanced reasoning and robust generalization abilities. To comprehensively evaluate multimodal reasoning performance across different grades, we introduce R1-Onevision-Bench, a benchmark aligned with human educational stages, covering exams from junior high school to university and beyond. Experimental results show that R1-Onevision achieves state-of-the-art performance, outperforming models such as GPT-4o and Qwen2.5-VL on multiple challenging multimodal reasoning benchmarks. Code, dataset and benchmark are available at https://github.com/Fancy-MLLM/R1-Onevision
HIS-GPT: Towards 3D Human-In-Scene Multimodal Understanding
Authors: Jiahe Zhao; Ruibing Hou; Zejie Tian; Hong Chang; Shiguang Shan
Venue: ICCV 2025 (main)

Abstract: We propose a new task to benchmark human-in-scene understanding for embodied agents: Human-In-Scene Question Answering (HIS-QA). Given a human motion within a 3D scene, HIS-QA requires the agent to comprehend human states and behaviors, reason about its surrounding environment, and answer human-related questions within the scene. To support this new task, we present HIS-Bench, a multimodal benchmark that systematically evaluates HIS understanding across a broad spectrum, from basic perception to commonsense reasoning and planning. Our evaluation of various vision-language models on HIS-Bench reveals significant limitations in their ability to handle HIS-QA tasks. To this end, we propose HIS-GPT, the first foundation model for HIS understanding. HIS-GPT integrates 3D scene context and human motion dynamics into large language models while incorporating specialized mechanisms to capture human-scene interactions. Extensive experiments demonstrate that HIS-GPT sets a new state-of-the-art on HIS-QA tasks. We hope this work inspires future research of human behavior analysis in 3D scenes, advancing embodied AI and world models.
Learnable Logit Adjustment for Imbalanced Semi-Supervised Learning under Class Distribution Mismatch
Authors: Hyuck Lee; Taemin Park; Heeyoung Kim
Venue: ICCV 2025 (main)

Abstract: In class-imbalanced learning (CIL), post-hoc logit adjustment (LA) effectively mitigates class imbalance by adjusting biased logits according to label frequencies. Given the success of LA in CIL, recent class-imbalanced semi-supervised learning (CISSL) algorithms incorporated LA, leading to improved performance when labeled and unlabeled datasets share the same class distribution. However, a common real-world scenario involves the unknown class distribution of the unlabeled set, which may mismatch that of the labeled set. In this case, LA may result in an inappropriate degree of logit adjustments, potentially degrading classification performance due to its inability to incorporate the unknown class distribution of the unlabeled set. To address this problem, we propose a novel CISSL algorithm named learnable logit adjustment (LLA). Unlike the original LA, LLA learns the appropriate degree of logit adjustment by minimizing the class-averaged loss computed for both the labeled and unlabeled sets. Based on the learned degree, LLA refines the biased pseudo-labels of base semi-supervised learning algorithms and adjusts the biased class predictions on the test set by adjusting the logits. Experimental results on benchmark datasets demonstrate that LLA achieves state-of-the-art performance in CISSL.
Subjective Camera 1.0: Bridging Human Cognition and Visual Reconstruction through Sequence-Aware Sketch-Guided Diffusion
Authors: Haoyang Chen; Dongfang Sun; Caoyuan Ma; Shiqin Wang; Kewei Zhang; Zheng Wang; Zhixiang Wang
Venue: ICCV 2025 (main)

Abstract: We introduce the concept of a subjective camera to reconstruct meaningful moments that physical cameras fail to capture. We propose Subjective Camera 1.0, a framework for reconstructing real-world scenes from readily accessible subjective readouts, i.e., textual descriptions and progressively drawn rough sketches. Built on optimization-based alignment of diffusion models, our approach avoids large-scale paired training data and mitigates generalization issues. To address the challenge of integrating multiple abstract concepts in real-world scenarios, we design a Sequence-Aware Sketch-Guided Diffusion framework with three loss terms for concept-wise sequential optimization, following the natural order of subjective readouts. Experiments on two datasets demonstrate that our method achieves state-of-the-art performance in image quality as well as spatial and semantic alignment with target scenes. User studies with 40 participants further confirm that our approach is consistently preferred. Our project page is at: subjective-camera.github.io
Learning Robust Image Watermarking with Lossless Cover Recovery
Authors: Jiale Chen; Wei Wang; Chongyang Shi; Li Dong; Xiping Hu
Venue: ICCV 2025 (main)

Abstract: Watermarking as a traceable authentication technology has been widely applied in image copyright protection. However, most existing watermarking methods embed watermarks by adding irremovable perturbations to the cover image, causing permanent distortion. To address this issue, we propose a novel watermarking approach termed Cover-Recoverable Watermark (CRMark). CRMark can losslessly recover the cover image and watermark in lossless channels and enables robust watermark extraction in lossy channels. CRMark leverages an integer Invertible Watermarking Network (iIWN) to achieve a lossless invertible mapping between the cover-image-watermark pair and the stego image. During the training phase, CRMark employs an encoder-noise-layer-decoder architecture to enhance its robustness against distortions. In the inference phase, CRMark first maps the cover-image-watermark pair into an overflowed stego image and a latent variable. Subsequently, the overflowed pixels and the latent variable are losslessly compressed into an auxiliary bitstream, which is then embedded into the clipped stego image using reversible data hiding. During extraction, in lossy channels, the noised stego image can directly undergo inverse mapping via iIWN to extract the watermark. In lossless channels, the latent variable and overflowed stego image are first recovered using reversible data hiding, followed by watermark extraction through iIWN. Extensive experimental results demonstrate that CRMark can be perfectly recovered in lossless channels while remaining robust to common distortions. Code is available at https://github.com/chenoly/CRMark.
Enhancing Transformers Through Conditioned Embedded Tokens
Authors: Hemanth Saratchandran; Simon  Lucey
Venue: ICCV 2025 (main)

Abstract: Transformers have transformed modern machine learning, driving breakthroughs in computer vision, natural language processing, and robotics. At the core of their success lies the attention mechanism, which enables the modeling of global dependencies among input tokens. However, we reveal that the attention block in transformers suffers from inherent ill-conditioning, which hampers gradient-based optimization and leads to inefficient training. To address this, we develop a theoretical framework that establishes a direct relationship between the conditioning of the attention block and that of the embedded tokenized data. Building on this insight, we introduce conditioned embedded tokens, a method that systematically modifies the embedded tokens to improve the conditioning of the attention mechanism. Our analysis demonstrates that this approach significantly mitigates ill-conditioning, leading to more stable and efficient training. We validate our methodology across various transformer architectures, achieving consistent improvements in image classification, object detection, instance segmentation, and natural language processing, highlighting its broad applicability and effectiveness.
Drawing Developmental Trajectory from Cortical Surface Reconstruction
Authors: Wenxuan Wu; Ruowen Qu; Zhongliang Liu; Zhuoyan Dai; Dongzi Shi; Sijin Yu; Tong Xiong; Shiping Liu; Xiangmin Xu; Xiaofen Xing; Xin Zhang
Venue: ICCV 2025 (main)

Abstract: Diffeomorphic-based cortical surface reconstruction typically involves a series of deformation processes to extract the cerebral cortex from brain magnetic resonance images (MRI). While most methods are designed for adult brains using Neural Ordinary Differential Equations (NODE) with fixed step sizes, the neonatal brain, which exhibits dramatic changes in cortical folding patterns early in life, requires a more adaptive approach. To address this, we develop a dual-task framework to directly characterize the brain development trajectory through processes of cortical surface reconstruction. For white matter (inner surfaces), we employ an Age-Conditioned ODE with adaptive step sizes. It is initially trained on a limited set of longitudinal paired data to establish a coarse trajectory, which is then refined through sample training of single-point data and knowledge distillation. For the pial surfaces (outer surfaces), we position the midthickness surfaces as intermediates and employ a cycle-consistent semi-supervised training strategy to depict a coherent brain development trajectory between the inner and outer surfaces. Our approach is the first to achieve precise developmental prediction directly on triangular meshes. Furthermore, by enhancing interpretability at each stage of the deformation process, this approach improves the applicability of diffeomorphic-based methods. The proposed method has demonstrated state-of-the-art performance in modeling developmental trajectories and cortical surface reconstruction within the developing Human Connectome Project dataset (dHCP).
CryoFastAR: Fast Cryo-EM Ab initio Reconstruction Made Easy
Authors: Jiakai Zhang; Shouchen Zhou; Haizhao Dai; Xinhang Liu; Peihao Wang; Zhiwen Fan; Yuan Pei; Jingyi Yu
Venue: ICCV 2025 (main)

Abstract: Pose estimation from unordered images is fundamental for 3D reconstruction, robotics, and scientific imaging. Recent geometric foundation models, such as DUSt3R, enable end-to-end dense 3D reconstruction but remain underexplored in scientific imaging fields like cryo-electron microscopy (cryo-EM) for near-atomic protein reconstruction. In cryo-EM, pose estimation and 3D reconstruction from unordered particle images still depend on time-consuming iterative optimization, primarily due to challenges such as low signal-to-noise ratios (SNR) and distortions from the contrast transfer function (CTF). We introduce CryoFastAR, the first geometric foundation model that can directly predict poses from Cryo-EM noisy images for Fast ab initio Reconstruction. By integrating multi-view features and training on large-scale simulated cryo-EM data with realistic noise and CTF modulations, CryoFastAR enhances pose estimation accuracy and generalization. To enhance training stability, we propose a progressive training strategy that first allows the model to extract essential features under simpler conditions before gradually increasing difficulty to improve robustness. Experiments show that CryoFastAR achieves comparable quality while significantly accelerating inference over traditional iterative approaches on both synthetic and real datasets.
3DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardt
Authors: Lukas Höllein; Aljaž Božič; Michael Zollhöfer; Matthias Nießner
Venue: ICCV 2025 (main)

Abstract: We present 3DGS-LM, a new method that accelerates the reconstruction of 3D Gaussian Splatting (3DGS) by replacing its ADAM optimizer with a tailored Levenberg-Marquardt (LM). Existing methods reduce the optimization time by decreasing the number of Gaussians or by improving the implementation of the differentiable rasterizer. However, they still rely on the ADAM optimizer to fit Gaussian parameters of a scene in thousands of iterations, which can take up to an hour. To this end, we change the optimizer to LM that runs in conjunction with the 3DGS differentiable rasterizer. For efficient GPU parallelization, we propose a caching data structure for intermediate gradients that allows us to efficiently calculate Jacobian-vector products in custom CUDA kernels. In every LM iteration, we calculate update directions from multiple image subsets using these kernels and combine them in a weighted mean. Overall, our method is 20% faster than the original 3DGS while obtaining the same reconstruction quality. Our optimization is also agnostic to other methods that accelerate 3DGS, thus enabling even faster speedups compared to vanilla 3DGS.
GauUpdate: New Object Insertion in 3D Gaussian Fields with Consistent Global Illumination
Authors: Chengwei Ren; Fan Zhang; Liangchao Xu; Liang Pan; Ziwei Liu; Wenping Wang; Xiao-Ping Zhang; Yuan Liu
Venue: ICCV 2025 (main)

Abstract: 3D Gaussian Splatting (3DGS) is a prevailing technique to reconstruct large-scale 3D scenes from multiview images for novel view synthesis, like a room, a block, and even a city. Such large-scale scenes are not static with changes constantly happening in these scenes, like a new building being built or a new decoration being set up. To keep the reconstructed 3D Gaussian fields up-to-date, a naive way is to reconstruct the whole scene after changing, which is extremely costly and inefficient. In this paper, we propose a new method called GauUpdate that allows partially updating an old 3D Gaussian field with new objects from a new 3D Gaussian field. However, simply inserting the new objects leads to inconsistent appearances because the old and new Gaussian fields may have different lighting environments from each other. GauUpdate addresses this problem by applying inverse rendering techniques in the 3DGS to recover both the materials and environmental lights. Based on the materials and lighting, we relight the new objects in the old 3D Gaussian field for consistent global illumination. For an accurate estimation of the materials and lighting, we put additional constraints on the materials and lighting conditions, that these two fields share the same materials but different environment lights, to improve their qualities. We conduct experiments on both synthetic scenes and real-world scenes to evaluate GauUpdate, which demonstrate that GauUpdate achieves realistic object insertion in 3D Gaussian fields with consistent appearances.
OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining
Authors: Ming Hu; Kun Yuan; Yaling Shen; Feilong Tang; Xiaohao Xu; Lin Zhou; Wei Li; Ying Chen; Zhongxing Xu; Zelin Peng; Siyuan Yan; Vinkle Srivastav; Diping Song; Tianbin Li; Danli Shi; Jin Ye; Nicolas Padoy; Nassir Navab; Junjun He; Zongyuan Ge
Venue: ICCV 2025 (main)

Abstract: Vision-language pretraining (VLP) enables open-world generalization beyond predefined labels, a critical capability in surgery due to the diversity of procedures, instruments, and patient anatomies. However, applying VLP to ophthalmic surgery presents unique challenges, including limited vision-language data, intricate procedural workflows, and the need for hierarchical understanding, ranging from fine-grained surgical actions to global clinical reasoning. To address these, we introduce OphVL, a large-scale, hierarchically structured dataset containing over 375K video-text pairs, making it 15x larger than existing surgical VLP datasets. OphVL captures a diverse range of ophthalmic surgical attributes, including surgical phases, operations, actions, instruments, medications, disease causes, surgical objectives, and postoperative care recommendations. By aligning short clips with detailed narratives and full-length videos with structured titles, OphVL provides both fine-grained surgical details and high-level procedural context. Building on OphVL, we propose OphCLIP, a hierarchical retrieval-augmented VLP framework. OphCLIP leverages silent surgical videos as a knowledge base, retrieving semantically relevant content to enhance narrated procedure learning. This enables OphCLIP to integrate explicit linguistic supervision with implicit visual knowledge, improving ophthalmic workflow modeling. Evaluations across 11 benchmark datasets for surgical phase recognition and multi-instrument identification demonstrate OphCLIP's robust generalization and superior performance, establishing it as a foundation model for ophthalmic surgery.
Hints of Prompt: Enhancing Visual Representation for Multimodal LLMs in Autonomous Driving
Authors: Hao Zhou; Zhanning Gao; Zhili Chen; Maosheng Ye; Qifeng Chen; Tongyi Cao; Honggang Qi
Venue: ICCV 2025 (main)

Abstract: In light of the dynamic nature of autonomous driving environments and stringent safety requirements, general MLLMs combined with CLIP alone often struggle to accurately represent driving-specific scenarios, particularly in complex interactions and long-tail cases. To address this, we propose the Hints of Prompt (HoP) framework, which introduces three key enhancements: Affinity hint to emphasize instance-level structure by strengthening token-wise connections, Semantic hint to incorporate high-level information relevant to driving-specific cases, such as complex interactions among vehicles and traffic signs, and Question hint to align visual features with the query context, focusing on question-relevant regions. These hints are fused through a Hint Fusion module, enriching visual representations by capturing driving-related representations with limited domain data, ensuring faster adaptation to driving scenarios. Extensive experiments confirm the effectiveness of the HoP framework, showing that it significantly outperforms previous state-of-the-art methods in all key metrics.
IM360: Large-scale Indoor Mapping with 360 Cameras
Authors: Dongki Jung; Jaehoon Choi; Yonghan Lee; Dinesh Manocha
Venue: ICCV 2025 (main)

Abstract: We present a novel 3D mapping pipeline for large-scale indoor environments. To address the significant challenges in large-scale indoor scenes, such as prevalent occlusions and textureless regions, we propose IM360, a novel approach that leverages the wide field of view of omnidirectional images and integrates the spherical camera model into the Structure-from-Motion (SfM) pipeline. Our SfM utilizes dense matching features specifically designed for 360 images, demonstrating superior capability in image registration. Furthermore, with the aid of mesh-based neural rendering techniques, we introduce a texture optimization method that refines texture maps and accurately captures view-dependent properties by combining diffuse and specular components. We evaluate our pipeline on large-scale indoor scenes, demonstrating its effectiveness in real-world scenarios. In practice, IM360 demonstrates superior performance, achieving a 3.5 PSNR increase in textured mesh reconstruction. We attain state-of-the-art performance in terms of camera localization and registration on Matterport3D and Stanford2D3D.