ICCV 2025 论文列表

Parametric human body models play a crucial role in computer graphics and vision, enabling applications ranging from human motion analysis to understanding human-environment interactions. Traditionally, these models use surface meshes, which pose challenges in efficiently handling interactions with other geometric entities, such as objects and scenes, typically represented as meshes or point clouds. To address this limitation, recent research has explored volumetric neural implicit body models. However, existing works are either insufficiently robust for complex human articulations or impose high computational and memory costs, limiting their widespread use. To this end, we introduce VolumetricSMPL, a neural volumetric body model that leverages Neural Blend Weights (NBW) to generate compact, yet efficient MLP decoders. Unlike prior approaches that rely on large MLPs, NBW dynamically blends a small set of learned weight matrices using predicted shape- and pose-dependent coefficients, significantly improving computational efficiency while preserving expressiveness. VolumetricSMPL outperforms prior volumetric occupancy model COAP with 10x faster inference, 6x lower GPU memory usage, enhanced accuracy, and a Signed Distance Function (SDF) for efficient and differentiable contact modeling. We demonstrate VolumetricSMPL's strengths across four challenging tasks: (1) reconstructing human-object interactions from in-the-wild images, (2) recovering human meshes in 3D scenes from egocentric views, (3) scene-constrained motion synthesis, and (4) resolving self-intersections. Our results highlight its broad applicability and significant performance and efficiency gains.

Virtual 3D meetings offer the potential to enhance copresence, increase engagement and thus improve effectiveness of remote meetings compared to standard 2D video calls. However, representing people in 3D meetings remains a challenge; existing solutions achieve high quality by using complex hardware, making use of fixed appearance via enrolment, or by inverting a pre-trained generative model. These approaches lead to constraints that are unwelcome and ill-fitting for videoconferencing applications.We present the first method to predict 3D Gaussian reconstructions in real time from a single 2D webcam feed, where the 3D representation is not only live and realistic, but also authentic to the input video. By conditioning the 3D representation on each video frame independently, our reconstruction faithfully recreates the input video from the captured viewpoint (a property we call authenticity), while generalizing realistically to novel viewpoints. Additionally, we introduce a stability loss to obtain reconstructions that are temporally stable on video sequences.We show that our method delivers state-of-the-art accuracy in visual quality and stability metrics compared to existing methods, and demonstrate our approach in live one-to-one 3D meetings using only a standard 2D camera and display. This demonstrates that our approach can allow anyone to communicate volumetrically, via a method for 3D videoconferencing that is not only highly accessible, but also realistic and authentic.

We present VoiceCraft-Dub, a novel approach for automated video dubbing that synthesizes high-quality speech from text and facial cues. This task has broad applications in filmmaking, multimedia creation, and assisting voice-impaired individuals. Building on the success of Neural Codec Language Models (NCLMs) for speech synthesis, our method extends their capabilities by incorporating video features, ensuring that synthesized speech is time-synchronized and expressively aligned with facial movements while preserving natural prosody. To inject visual cues, we design adapters to align facial features with the NCLM token space and introduce audio-visual fusion layers to merge audio-visual information within the NCLM framework. Additionally, we curate CelebV-Dub, a new dataset of expressive, real-world videos specifically designed for automated video dubbing. Extensive experiments show that our model achieves high-quality, intelligible, and natural speech synthesis with accurate lip synchronization, outperforming existing methods in human perception and performing favorably in objective evaluations. We also adapt VoiceCraft-Dub for the video-to-speech task, demonstrating its versatility for various applications.

Reconstructing 4D dynamic scenes from casually captured monocular videos is valuable but highly challenging, as each timestamp is observed from a single viewpoint. We introduce Vivid4D, a novel approach that enhances 4D monocular video synthesis by augmenting observation views -- synthesizing multi-view videos from a monocular input. Unlike existing methods that either solely leverage geometric priors for supervision or use generative priors while overlooking geometry, we integrate both. This reformulates view augmentation as a video inpainting task, where observed views are warped into new viewpoints based on monocular depth priors. To achieve this, we train a video inpainting model on unposed web videos with synthetically generated masks that mimic warping occlusions, ensuring spatially and temporally consistent completion of missing regions. To further mitigate inaccuracies in monocular depth priors, we introduce an iterative view augmentation strategy and a robust reconstruction loss. Experiments demonstrate that our method effectively improves monocular 4D scene reconstruction and completion.

Recent advances in diffusion models have significantly advanced image generation; however, existing models remain task-specific, limiting their efficiency and generalizability. While universal models attempt to address these limitations, they face critical challenges, including generalizable instruction design, appropriate task distributions, and unified architectural design. In this work, we propose VisualCloze, a universal image generation framework, to tackle these challenges. Unlike existing methods that rely on language-based task descriptions, leading to task ambiguity and weak generalization, we integrate visual in-context learning, allowing models to identify tasks from demonstrations. Meanwhile, the inherent sparsity of visual task distributions hampers the learning of transferable knowledge across tasks. To this end, we introduce Graph200K, a graph-structured dataset that establishes various interrelated tasks, enhancing task density and knowledge transfer. Furthermore, we uncover an intrinsic alignment between image infilling and in-context learning, enabling us to leverage the strong generative priors of pre-trained infilling models without modifying their architectures. Experiments demonstrate that VisualCloze achieves strong performance across more than 100 in-domain tasks while generalizing to unseen tasks in few-shot and zero-shot settings. Our codes and dataset are available at https://visualcloze.github.io/.

Existing knowledge editing works for MultiModal Large Language Models primarily focus on text-oriented, coarse-grained scenarios, where modifying textual content alone is sufficient. As a result, they fail to capture the unique challenges of multimodal editing, particularly when visual information is central to knowledge representation. In this paper, we introduce a visual-oriented, fine-grained multimodal knowledge editing task that targets precise modifications in images containing multiple interacting entities. To support this, we propose the Fine-Grained Visual Knowledge Editing (FGVEdit) benchmark, designed to evaluate the accuracy and effectiveness of multimodal editing at a granular level. To address this challenge, we present the Multimodal Scope Classifier-based Knowledge Editor (MSCKE), a new framework that leverages a multimodal scope classifier to integrate both textual and visual information. By accurately identifying and updating knowledge localized within images, MSCKE ensures precise editing while preserving unrelated content. Extensive experiments on the FGVEdit benchmark highlight the complexity of this new task and demonstrate that existing methods struggle with fine-grained multimodal editing. Our results highlight MSCKE as a scalable and promising framework for advancing multimodal knowledge editing. Code is available at https://github.com/zeng-zhen/FGVEdit.

We propose VisTex-OVLM, a novel image prompted object detection method that introduces visual textualization ---- a process that projects a few visual exemplars into the text feature space to enhance Object-level Vision-Language Models' (OVLMs) capability in detecting rare categories that are difficult to describe textually and nearly absent from their pre-training data, while preserving their pre-trained object-text alignment. Specifically, VisTex-OVLM leverages multi-scale textualizing blocks and a multi-stage fusion strategy to integrate visual information from visual exemplars, generating textualized visual tokens that effectively guide OVLMs alongside text prompts. Unlike previous methods, our method maintains the original architecture of OVLM, maintaining its generalization capabilities while enhancing performance in few-shot settings. VisTex-OVLM demonstrates superior performance across open-set datasets which have minimal overlap with OVLM's pre-training data and achieves state-of-the-art results on few-shot benchmarks PASCAL VOC and MSCOCO . The code will be released at VisTex-OVLM.

We introduce RegionFocus, a visual test-time scaling approach for Vision Language Model Agents. Understanding webpages is challenging due to the visual complexity of GUI images and the large number of interface elements, making accurate action selection difficult. Our approach dynamically zooms in on relevant regions, reducing background clutter and improving grounding accuracy. To support this process, we propose an image-as-map mechanism that visualizes key landmarks at each step, providing a transparent action record and enables the agent to effectively choose among action candidates. Even with a simple region selection strategy, we observe significant performance gains of 28+% on Screenspot-pro and 24+% on WebVoyager benchmarks on top of two state-of-the-art open vision language model agents, UI-TARS-72B and Qwen2.5-VL-72B, highlighting the effectiveness of visual test-time scaling in interactive settings. We achieve a new state-of-the-art grounding performance of 61.6% on the ScreenSpot-Pro benchmark by applying RegionFocus to a Qwen2.5-VL-72B model. Our code is publicly available at https://github.com/tiangeluo/RegionFocus.

Wave propagation on the surface of a material contains information about physical properties beneath its surface. We propose a method for inferring the thickness and stiffness of a structure from just a video of waves on its surface. Our method works by extracting a dispersion relation from the video and then solving a physics-based optimization problem to find the best-fitting thickness and stiffness parameters. We validate our method on both simulated and real data, in both cases showing strong agreement with ground-truth measurements. Our technique provides a proof-of-concept for at-home health monitoring of medically-informative tissue properties, and it is further applicable to fields such as human-computer interaction.

Human-object interaction (HOI) detection relies on fine-grained visual understanding to distinguish complex relationships between humans and objects. While recent generative diffusion models have demonstrated remarkable capability in learning detailed visual concepts through pixel-level generation, their potential for interaction-level relationship modeling remains largely unexplored. To bridge this gap, we propose a Visual Relation Diffusion model (VRDiff), which introduces dense visual relation conditions to guide interaction understanding. Specifically, we encode interaction-aware condition representations that capture both spatial responsiveness and contextual semantics of human-object pairs, conditioning the diffusion process purely on visual features rather than text-based inputs. Furthermore, we refine these relation representations through generative feedback from the diffusion model, enhancing HOI detection without requiring image synthesis. Extensive experiments on the HICO-DET benchmark demonstrate that VRDiff achieves competitive results under both standard and zero-shot HOI detection settings.

The zero-shot performance of object detectors degrades when tested on different modalities, such as infrared and depth. While recent work has explored image translation techniques to adapt detectors to new modalities, these methods are limited to a single modality and traditional detectors. Recently, vision-language detectors (VLDs), such as YOLO-World and Grounding DINO, have shown promising zero-shot capabilities; however, they have not yet been adapted for other visual modalities. Traditional fine-tuning approaches compromise the zero-shot capabilities of the detectors. The visual prompt strategies commonly used for classification with vision-language models apply the same linear prompt translation to each image, making them less effective. To address these limitations, we propose ModPrompt, a visual prompt strategy to adapt VLDs to new modalities without degrading zero-shot performance. In particular, an encoder-decoder visual prompt strategy is proposed, further enhanced by the integration of inference-friendly modality prompt decoupled residual, facilitating a more robust adaptation. Empirical benchmarking results show our method for modality adaptation on YOLO-World and Grounding DINO for challenging infrared (LLVIP, FLIR) and depth (NYUv2) datasets, achieving performance comparable to full fine-tuning while preserving the model's zero-shot capability. Our code is available at https://github.com/heitorrapela/ModPrompt.

Our daily life is highly influenced by what we consume and see. Attracting and holding one's attention -- the definition of (visual) interestingness -- is essential. The rise of Large Multimodal Models (LMMs) trained on large-scale visual and textual data has demonstrated impressive capabilities. We explore these models' potential to understand to what extent the concepts of visual interestingness are captured and examine the alignment between human assessments and GPT-4o's, a leading LMM, predictions through comparative analysis. Our studies reveal partial alignment between humans and GPT-4o. It already captures the concept as best compared to state-of-the-art methods. Hence, this allows for the effective labeling of image pairs according to their (commonly) interestingness, which are used as training data to distill the knowledge into a learning-to-rank model. The insights pave the way for a deeper understanding of human interest.

Visual grounding associates textual descriptions with objects in an image. Conventional methods target third-person image inputs and named object queries. In applications such as AI assistants, the perspective shifts -- inputs are egocentric, and objects may be referred to implicitly through needs and intentions. To bridge this gap, we introduce EgoIntention, the first dataset for egocentric visual intention grounding. EgoIntention challenges multimodal LLMs to 1) understand and ignore unintended contextual objects and 2) reason about uncommon object functionalities. Benchmark results show that current models misidentify context objects and lack affordance understanding in egocentric views. We also propose Reason-to-Ground (RoG) instruction tuning; it enables hybrid training with normal descriptions and egocentric intentions with a chained intention reasoning and object grounding mechanism. RoG significantly outperforms naive finetuning and hybrid training on EgoIntention, while maintaining or slightly improving naive description grounding. This advancement enables unified visual grounding for egocentric and exocentric visual inputs while handling explicit object queries and implicit human intentions.

We present a system using Multimodal LLMs (MLLMs) to analyze a large database with tens of millions of images captured at different times, with the aim of discovering patterns in temporal changes. Specifically, we aim to capture frequent co-occurring changes ("trends") across a city over a certain period. Unlike previous visual analyses, our analysis answers open-ended queries (e.g., "what are the frequent types of changes in the city?") without any predetermined target subjects or training labels. These properties cast prior learning-based or unsupervised visual analysis tools unsuitable. We identify MLLMs as a novel tool for their open-ended semantic understanding capabilities. Yet, our datasets are four orders of magnitude too large for an MLLM to ingest as context. So we introduce a bottom-up procedure that decomposes the massive visual analysis problem into more tractable sub-problems. We carefully design MLLM-based solutions to each sub-problem. During experiments and ablation studies with our system, we find it significantly outperforms baselines and is able to discover interesting trends from images captured in large cities (e.g., "addition of outdoor dining,", "overpass was painted blue," etc.).

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.

Mathematical problems in real-world scenarios are often presented in a purely vision-form, where textual problem statement and accompanying math figures, e.g., geometry figures and functional graphs, are integrated into a single image. This vision-form problem-solving task requires precise comprehension and reasoning on both textual and graphical elements in the images, posing significant challenge to current Multimodal Large Language Models (MLLMs), which process text and math figures in isolation. In this work, we propose VisionMath, the first exploration for vision-form mathematical problem-solving model, which employs a three-stage progressive multimodal reasoning alignment strategy to systematically enhance task-specific capabilities. Building upon a LLM proficient in unimodal mathematical reasoning, VisionMath first establishes foundational OCR capabilities through capturing rendered mathematical problem images. Subsequently, the model develops comprehensive understanding of figure structures and properties via learning from figure descriptions and mathematical educational videos. Finally, the model's reasoning capacity is activated using carefully constructed visual-form problem-solving datasets VisionMath-IT with chain-of-thought annotations. For comprehensive evaluation, we construct multilingual benchmarks covering diverse problem types, including geometry, algebra, function problems in both English and Chinese. Our model weights, data and code will be made available at https://github.com/mengqiDyangge/VisionMath.

Retinopathy comprises a group of retinal disorders that can lead to severe visual impairments or blindness. The heterogeneous morphology of lesions poses a significant challenge in developing robust diagnostic systems. Supervised approaches rely on large labeled datasets and often struggle with generalization. To address these limitations, we propose an unsupervised vision-language neural graph featurization method. This method first segments fundus images into a set of superpixels via Simple Linear Iterative Clustering (SLIC). The superpixels are then decomposed into an undirected graph where each superpixel serves as a node, and spatially adjacent nodes are connected by edges. A Hamiltonian path systematically traverses the graph and iteratively updates and propagates node and edge latent space embeddings throughout the graph until convergence is achieved. Then, a normalized cut separates the converged embeddings into two clusters within a latent space that represent the lesion and healthy superpixels of the input scans. The lesion superpixels are further classified into lesion categories using a prompt-based zero-shot vision-language model. The proposed method is rigorously tested on four public datasets, dubbed ODIR, FIVES, BIOMISA, and IDRiD, achieving F1-scores of 0.89, 0.92, 0.93, and 0.92, respectively, with significant performance gains over state-of-the-art methods.

We present Saliency Benchmark (SalBench), a novel benchmark designed to assess the capability of Large Vision-Language Models (LVLM) in detecting visually salient features that are readily apparent to humans, such as a large circle amidst a grid of smaller ones. This benchmark focuses on low-level features including color, intensity, and orientation, which are fundamental to human visual processing. Our SalBench consists of images that highlight rare, unusual, or unexpected elements within scenes, and naturally draw human attention. It comprises three novel tasks for evaluating the perceptual capabilities of LVLM: Odd-One-Out Detection, Referring Odd-One-Out, and Visual Referring Odd-One-Out. We perform a comprehensive evaluation of state-of-the-art LVLM using SalBench and our findings reveal a surprising limitation: LVLM struggle to identify seemingly obvious visual anomalies, with even the advanced GPT-4o achieving only 47.6% accuracy on such a simple task. SalBench will be an important step in measuring the capabilities of LVLM that align with the subtle definition of human attention.