GenAI4ELab/papercli-papers huggingface dataset:
InfoBridge: Balanced Multimodal Integration through Conditional Dependency Modeling
Authors: Chenxin Li; Yifan Liu; Panwang Pan; Hengyu Liu; Xinyu Liu; Wuyang Li; Cheng Wang; Weihao Yu; Yiyang Lin; Yixuan Yuan
Venue: ICCV 2025 (main)

Abstract: Developing systems that interpret diverse real-world signals remains a fundamental challenge in multimodal learning. Current approaches face significant obstacles from inherent modal heterogeneity. While existing methods attempt to enhance fusion through cross-modal alignment or interaction mechanisms, they often struggle to balance effective integration with preserving modality-specific information. We introduce InfoBridge, a novel framework grounded in conditional information maximization principles addressing these limitations. Our approach reframes multimodal fusion through two key innovations: (i) we formulate fusion as conditional mutual information optimization with integrated protective margin that simultaneously encourages cross-modal information sharing while safeguarding against over-fusion eliminating modal characteristics; and (ii) we enable fine-grained contextual fusion by leveraging modality-specific conditions to guide integration. Extensive evaluations across benchmarks demonstrate that InfoBridge consistently outperforms state-of-the-art multimodal architectures, establishing a principled approach that better captures complementary information across input signals. Project page: https://cuhk-aim-group.github.io/InfoBridge/.
Towards Explicit Exoskeleton for the Reconstruction of Complicated 3D Human Avatars
Authors: Yifan Zhan; Qingtian Zhu; Muyao Niu; Mingze Ma; Jiancheng Zhao; Zhihang Zhong; Xiao Sun; Yu Qiao; Yinqiang Zheng
Venue: ICCV 2025 (main)

Abstract: In this paper, we highlight a critical yet often overlooked factor in most 3D human tasks, namely modeling complicated 3D human with with hand-held objects or loose-fitting clothing. It is known that the parameterized formulation of SMPL is able to fit human skin; while hand-held objects and loose-fitting clothing, are difficult to get modeled within the unified framework, since their movements are usually decoupled with the human body.To enhance the capability of SMPL skeleton in response to this situation, we propose a growth strategy that enables the joint tree of the skeleton to expand adaptively. Specifically, our method, called ToMiE, consists of parent joints localization and external joints optimization. For parent joints localization, we employ a gradient-based approach guided by both LBS blending weights and motion kernels.Once the external joints are obtained, we proceed to optimize their transformations in SE(3) across different frames, enabling rendering and explicit animation.ToMiE manages to outperform other methods across various cases with hand-held objects and loose-fitting clothing, not only in rendering quality but also by offering free animation of grown joints, thereby enhancing the expressive ability of SMPL skeleton for a broader range of applications.
CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization
Authors: Soorena Salari; Arash Harirpoush; Hassan Rivaz; Yiming Xiao
Venue: ICCV 2025 (main)

Abstract: Anatomical landmark detection in medical images is essential for various clinical and research applications, including disease diagnosis and surgical planning. However, manual landmark annotation is time-consuming and requires significant expertise. Existing deep learning (DL) methods often require large amounts of well-annotated data, which are costly to acquire. In this paper, we introduce CABLD, a novel self-supervised DL framework for 3D brain landmark detection in unlabeled scans with varying contrasts by using only a single reference example. To achieve this, we employed an inter-subject landmark consistency loss with an image registration loss while introducing a 3D convolution-based contrast augmentation strategy to promote model generalization to new contrasts. Additionally, we utilize an adaptive mixed loss function to schedule the contributions of different sub-tasks for optimal outcomes. We demonstrate the proposed method with the intricate task of MRI-based 3D brain landmark detection. With comprehensive experiments on four diverse clinical and public datasets, including both T1w and T2w MRI scans at different MRI field strengths, we demonstrate that CABLD outperforms the state-of-the-art methods in terms of mean radial errors (MREs) and success detection rates (SDRs). Our framework provides a robust and accurate solution for anatomical landmark detection, reducing the need for extensively annotated datasets and generalizing well across different imaging contrasts. Our code is publicly available at https://github.com/HealthX-Lab/CABLD.
One Object, Multiple Lies: A Benchmark for Cross-task Adversarial Attack on Unified Vision-Language Models
Authors: Jiale Zhao; Xinyang Jiang; Junyao Gao; Yuhao Xue; Cairong Zhao
Venue: ICCV 2025 (main)

Abstract: Unified vision-language models (VLMs) have recently shown remarkable progress, enabling a single model to flexibly address diverse tasks through different instructions within a shared computational architecture. This instruction-based control mechanism creates unique security challenges, as adversarial inputs must remain effective across multiple task instructions that may be unpredictably applied to process the same malicious content. In this paper, we introduce CrossVLAD, a new benchmark dataset carefully curated from MSCOCO with GPT-4-assisted annotations for systematically evaluating cross-task adversarial attacks on unified VLMs. CrossVLAD centers on the object-change objective--consistently manipulating a target object's classification across four downstream tasks--and proposes a novel success rate metric that measures simultaneous misclassification across all tasks, providing a rigorous evaluation of adversarial transferability. To tackle this challenge, we present CRAFT (Cross-task Region-based Attack Framework with Token-alignment), an efficient region-centric attack method. Extensive experiments on Florence-2 and other popular unified VLMs demonstrate that our method outperforms existing approaches in both overall cross-task attack performance and targeted object-change success rates, highlighting its effectiveness in adversarially influencing unified VLMs across diverse tasks.
A Differentiable Wave Optics Model for End-to-End Computational Imaging System Optimization
Authors: Chi-Jui Ho; Yash Belhe; Steve Rotenberg; Ravi Ramamoorthi; Tzu-Mao Li; Nicholas Antipa
Venue: ICCV 2025 (main)

Abstract: End-to-end optimization, which integrates differentiable optics simulators with computational algorithms, enables the joint design of hardware and software for data-driven imaging systems. However, existing methods usually compromise physical accuracy by neglecting wave optics or off-axis effects due to the high computational cost of modeling both aberration and diffraction. This limitation raises concerns about the robustness of optimized designs. In this paper, we propose a differentiable optics simulator that accurately and efficiently models aberration and diffraction in compound optics and allows us to analyze the role and impact of diffraction in end-to-end optimization. Experimental results demonstrate that compared with ray-optics-based optimization, diffraction-aware optimization improves system robustness to diffraction blur. Through accurate wave optics modeling, we also apply the simulator to optimize the Fizeau interferometer and freeform optics elements. These findings underscore the importance of accurate wave optics modeling in robust end-to-end optimization. Our code is publicly available at: https://github.com/JerryHoTaiwan/DeepWaveOptics
CHROME: Clothed Human Reconstruction with Occlusion-Resilience and Multiview-Consistency from a Single Image
Authors: Arindam Dutta; Meng Zheng; Zhongpai Gao; Benjamin Planche; Anwesa Choudhuri; Terrence Chen; Amit K. Roy-Chowdhury; Ziyan Wu
Venue: ICCV 2025 (main)

Abstract: Reconstructing clothed humans from a single image is a fundamental task in computer vision with wide-ranging applications. Although existing monocular clothed human reconstruction solutions have shown promising results, they often rely on the assumption that the human subject is in an occlusion-free environment. Thus, when encountering in-the-wild occluded images, these algorithms produce multiview inconsistent and fragmented reconstructions. Additionally, most algorithms for monocular 3D human reconstruction leverage geometric priors such as SMPL annotations for training and inference, which are extremely challenging to acquire in real-world applications. To address these limitations, we propose CHROME: Clothed Human Reconstruction with Occlusion-Resilience and Multiview-ConsistEncy from a Single Image, a novel pipeline designed to reconstruct occlusion-resilient 3D humans with multiview consistency from a single occluded image, without requiring either ground-truth geometric prior annotations or 3D supervision. Specifically, CHROME leverages a multiview diffusion model to first synthesize occlusion-free human images from the occluded input, compatible with off-the-shelf pose control to explicitly enforce cross-view consistency during synthesis. A 3D reconstruction model is then trained to predict a set of 3D Gaussians conditioned on both the occluded input and synthesized views, aligning cross-view details to produce a cohesive and accurate 3D representation. CHROME achieves significant improvements in terms of both novel view synthesis (upto 3 db PSNR) and geometric reconstruction under challenging conditions.
H3R: Hybrid Multi-view Correspondence for Generalizable 3D Reconstruction
Authors: Heng Jia; Linchao Zhu; Na Zhao
Venue: ICCV 2025 (main)

Abstract: Despite recent advances in feed-forward 3D Gaussian Splatting, generalizable 3D reconstruction remains challenging, particularly in multi-view correspondence modeling. Existing approaches face a fundamental trade-off: explicit methods achieve geometric precision but struggle with ambiguous regions, while implicit methods provide robustness but suffer from slow convergence. We present H3R, a hybrid framework that addresses this limitation by integrating volumetric latent fusion with attention-based feature aggregation. Our framework consists of two complementary components: an efficient latent volume that enforces geometric consistency through epipolar constraints, and a camera-aware Transformer that leverages Plucker coordinates for adaptive correspondence refinement. By integrating both paradigms, our approach enhances generalization while converging 2xfaster than existing methods. Furthermore, we show that spatial-aligned foundation models (e.g., SD-VAE) substantially outperform semantic-aligned models (e.g., DINOv2), resolving the mismatch between semantic representations and spatial reconstruction requirements. Our method supports variable-number and high-resolution input views while demonstrating robust cross-dataset generalization. Extensive experiments show that our method achieves state-of-the-art performance across multiple benchmarks, with significant PSNR improvements of 0.59 dB, 1.06 dB, and 0.22 dB on the RealEstate10K, ACID, and DTU datasets, respectively. Code is available at https://github.com/JiaHeng-DLUT/H3R.
Capturing head avatar with hand contacts from a monocular video
Authors: Haonan He; Yufeng Zheng; Jie Song
Venue: ICCV 2025 (main)

Abstract: Photorealistic 3D head avatars are vital for telepresence, gaming, and VR. However, most methods focus solely on facial regions, ignoring natural hand-face interactions, such as a hand resting on the chin or fingers gently touching the cheek, which convey cognitive states like pondering. In this work, we present a novel framework that jointly learns detailed head avatars and the non-rigid deformations induced by hand-face interactions.There are two principal challenges in this task. First, naively tracking hand and face separately fails to capture their relative poses. To overcome this, we propose to combine depth order loss with contact regularization during pose tracking, ensuring correct spatial relationships between the face and hand. Second, no publicly available priors exist for hand-induced deformations, making them non-trivial to learn from monocular videos. To address this, we learn a PCA basis specific to hand-induced facial deformations from a face-hand interaction dataset. This reduces the problem to estimating a compact set of PCA parameters rather than a full spatial deformation field. Furthermore, inspired by physics-based simulation, we incorporate a contact loss that provides additional supervision, significantly reducing interpenetration artifacts and enhancing the physical plausibility of the results.We evaluate our approach on RGB(D) videos captured by an iPhone. Additionally, to better evaluate the reconstructed geometry, we construct a synthetic dataset of avatars with various types of hand interactions. We show that our method can capture better appearance and more accurate deforming geometry of the face than SOTA surface reconstruction methods.
DoppDrive: Doppler-Driven Temporal Aggregation for Improved Radar Object Detection
Authors: Yuval Haitman; Oded Bialer
Venue: ICCV 2025 (main)

Abstract: Radar-based object detection is essential for autonomous driving due to radar's long detection range. However, the sparsity of radar point clouds, especially at long range, poses challenges for accurate detection. Existing methods increase point density through temporal aggregation with ego-motion compensation, but this approach introduces scatter from dynamic objects, degrading detection performance. We propose DoppDrive, a novel Doppler-Driven temporal aggregation method that enhances radar point cloud density while minimizing scatter. Points from previous frames are shifted radially according to their dynamic Doppler component to eliminate radial scatter, with each point assigned a unique aggregation duration based on its Doppler and angle to minimize tangential scatter. DoppDrive is a point cloud density enhancement step applied before detection, compatible with any detector, and we demonstrate that it significantly improves object detection performance across various detectors and datasets. Our project page: https://yuvalhg.github.io/DoppDrive/
3D Gaussian Map with Open-Set Semantic Grouping for Vision-Language Navigation
Authors: Jianzhe Gao; Rui Liu; Wenguan Wang
Venue: ICCV 2025 (main)

Abstract: Vision-language navigation (VLN) requires an agent to traverse complex 3D environments based on natural language instructions, necessitating a thorough scene understanding. While existing works equip agents with various scene representations to enhance spatial awareness, they often neglect the complex 3D geometry and rich semantics in VLN scenarios, limiting the ability to generalize across diverse and unseen environments. To address these challenges, this work proposes a 3D Gaussian Map that represents the environment as a set of differentiable 3D Gaussians and accordingly develops a navigation strategy for VLN. Specifically, Egocentric Scene Map is constructed online by initializing 3D Gaussians from sparse pseudo-lidar point clouds, providing informative geometric priors for scene understanding. Each Gaussian primitive is further enriched through Open-Set Semantic Grouping operation, which groups 3D Gaussians based on their membership in object instances or stuff categories within the open world, resulting in a unified 3D Gaussian Map. Building on this map, Multi-Level Action Prediction strategy, which combines spatial-semantic cues at multiple granularities, is designed to assist agents in decision-making. Extensive experiments conducted on three public benchmarks (i.e., R2R, R4R, and REVERIE) validate the effectiveness of our method.
M-SpecGene: Generalized Foundation Model for RGBT Multispectral Vision
Authors: Kailai Zhou; Fuqiang Yang; Shixian Wang; Bihan Wen; Chongde Zi; Linsen Chen; Qiu Shen; Xun Cao
Venue: ICCV 2025 (main)

Abstract: RGB-Thermal (RGBT) multispectral vision is essential for robust perception in complex environments. Most RGBT tasks follow a case-by-case research paradigm, relying on manually customized models to learn task-oriented representations. Nevertheless, this paradigm is inherently constrained by artificial inductive bias, modality bias, and data bottleneck. To address these limitations, we make the initial attempt to build a Generalized RGBT MultiSpectral foundation model (M-SpecGene), which aims to learn modality-invariant representations from large-scale broad data in a self-supervised manner. M-SpecGene provides new insights into multispectral fusion and integrates prior case-by-case studies into a unified paradigm. Considering the unique characteristic of information imbalance in RGBT data, we introduce the Cross-Modality Structural Sparsity (CMSS) metric to quantify the information density across two modalities. Then we develop the GMM-CMSS progressive masking strategy to facilitate a flexible, easy-to-hard, and object-centric pre-training process. Comprehensive experiments validate M-SpecGene's generalizability across eleven datasets for four RGBT downstream tasks.
YOLOE: Real-Time Seeing Anything
Authors: Ao Wang; Lihao Liu; Hui Chen; Zijia Lin; Jungong Han; Guiguang Ding
Venue: ICCV 2025 (main)

Abstract: Object detection and segmentation are widely employed in computer vision applications, yet conventional models like YOLO series, while efficient and accurate, are limited by predefined categories, hindering adaptability in open scenarios. Recent open-set methods leverage text prompts, visual cues, or prompt-free paradigm to overcome this, but often compromise between performance and efficiency due to high computational demands or deployment complexity. In this work, we introduce YOLOE, which integrates detection and segmentation across diverse open prompt mechanisms within a single highly efficient model, achieving real-time seeing anything. For text prompts, we propose Re-parameterizable Region-Text Alignment (RepRTA) strategy. It refines pretrained textual embeddings via a re-parameterizable lightweight auxiliary network and enhances visual-textual alignment with zero inference and transferring overhead. For visual prompts, we present Semantic-Activated Visual Prompt Encoder (SAVPE). It employs decoupled semantic and activation branches to bring improved visual embedding and accuracy with minimal complexity. For prompt-free scenario, we introduce Lazy Region-Prompt Contrast (LRPC) strategy. It utilizes a built-in large vocabulary and specialized embedding to identify all objects, avoiding costly language model dependency. Extensive experiments show YOLOE's exceptional zero-shot performance and transferability with high inference efficiency and low training cost. Notably, on LVIS, with 3xless training cost and 1.4xinference speedup, YOLOE-v8-S surpasses YOLO-Worldv2-S by 3.5 AP. When transferring to COCO, YOLOE-v8-L achieves 0.6 AP^b and 0.4 AP^m gains over closed-set YOLOv8-L with nearly 4xless training time. Code and models are available at \href https://github.com/THU-MIG/yoloe  here .
Joint Learning of Pose Regression and Denoising Diffusion with Score Scaling Sampling for Category-level 6D Pose Estimation
Authors: Seunghyun Lee; Tae-Kyun Kim
Venue: ICCV 2025 (main)

Abstract: Latest diffusion models have shown promising results in category-level 6D object pose estimation by modeling the conditional pose distribution with depth image input. The existing methods, however, suffer from slow convergence during training, learning its encoder with the diffusion denoising network in end-to-end fashion, and require an additional network that evaluates sampled pose hypotheses to filter out low-quality pose candidates. In this paper, we propose a novel pipeline that tackles these limitations by two key components. First, the proposed method pre-trains the encoder with the direct pose regression head, and jointly learns the networks via the regression head and the denoising diffusion head, significantly accelerating training convergence while achieving higher accuracy. Second, sampling guidance via time-dependent score scaling is proposed s.t. the exploration-exploitation trade-off is effectively taken, eliminating the need for the additional evaluation network. The sampling guidance maintains multi-modal characteristics of symmetric objects at early denoising steps while ensuring high-quality pose generation at final steps. Extensive experiments on multiple benchmarks including REAL275, HouseCat6D, and ROPE, demonstrate that the proposed method, simple yet effective, achieves state-of-the-art accuracies even with single-pose inference, while being more efficient in both training and inference.
Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs
Authors: Qizhe Zhang; Aosong Cheng; Ming Lu; Renrui Zhang; Zhiyong Zhuo; Jiajun Cao; Shaobo Guo; Qi She; Shanghang Zhang
Venue: ICCV 2025 (main)

Abstract: Large Vision-Language Models (VLMs) generally contain significantly more visual tokens than their textual counterparts, resulting in a considerable computational burden. Recent efforts have been made to tackle this issue by pruning visual tokens early within the language model. Most existing works use attention scores between text and visual tokens to assess the importance of visual tokens. However, in this study, we first analyze the text-visual attention in the language model and find that this score is not an ideal indicator for visual token pruning. Based on the analysis, We propose VisPruner, a plug-and-play method that utilizes visual cues for more effective token pruning in LVLMs. Specifically, we first use visual attention to select a limited number of significant tokens. Then, we remove duplicate tokens from the remaining ones based on their similarity. By retaining diverse tokens alongside the initially selected important tokens, we maximally preserve the visual information of the input image. Experimental results demonstrate that our VisPruner sustains strong performance across various VLM architectures and reduction ratios, significantly outperforming existing methods based on text-visual attention. Notably, without any training, VisPruner can reduce the FLOPs of LLaVA-1.5-7B by 91% and inference latency by 75%, while maintaining comparable performance. Our code is available at https://github.com/Theia-4869/VisPruner.
Event-guided HDR Reconstruction with Diffusion Priors
Authors: Yixin Yang; Jiawei Zhang; Yang Zhang; Yunxuan Wei; Dongqing Zou; Jimmy S. Ren; Boxin Shi
Venue: ICCV 2025 (main)

Abstract: Events provide High Dynamic Range (HDR) intensity change which can guide Low Dynamic Range (LDR) image for HDR reconstruction. However, events only provide temporal intensity differences and it is still ill-posed in over-/under-exposed areas due to missing initial reference brightness and color information. With strong generation ability, diffusion models have shown their potential for tackling ill-posed problems. Therefore, we introduce conditional diffusion models to hallucinate missing information. Whereas, directly adopting events and LDR image as conditions is complicated for diffusion models to sufficiently utilize their information. Thus we introduce a pretrained events-image encoder tailored for HDR reconstruction and a pyramid fusion module to provide HDR conditions, which can be efficiently and effectively utilized by the diffusion model. Moreover, the generation results of diffusion models usually exhibit distortion, particularly for fine-grained details. To better preserve fidelity and suppress distortion, we propose a fine-grained detail recovery approach using a histogram-based structural loss. Experiments on real and synthetic data show the effectiveness of the proposed method in terms of both detail preservation and information hallucination.