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首次观测 · 记录 #1CVF · CVPR 2026:cvf:8be8ceb24f47e7f77d71fcaba578b730CVF · CVPR 2026

MAD: Motion Appearance Decoupling for efficient Driving World Models

Rahimi, Ahmad, Gerard, Valentin, Zablocki, Eloi, Cord, Matthieu, Alahi, Alexandre

原文摘要

Recent video diffusion models generate photorealistic, temporally coherent videos, yet they fall short as reliable world models for autonomous driving, where structured motion and physically consistent interactions are essential. Adapting these generalist video models to driving domains has shown promise but typically requires massive domain-specific data and costly fine-tuning. We propose an efficient adaptation framework that converts generalist video diffusion models into controllable driving world models with minimal supervision. The key idea is to decouple motion learning from appearance synthesis. First, the model is adapted to predict structured motion in a simplified form: videos of skeletonized agents and scene elements, focusing learning on physical and social plausibility. Then, the same backbone is reused to synthesize realistic RGB videos conditioned on these motion sequences, effectively "dressing" the motion with texture and lighting. This two-stage process mirrors a reasoning-rendering paradigm: first infer dynamics, then render appearance. Our experiments show this decoupled approach is exceptionally efficient: adapting SVD, we match prior SOTA models with less than 6% of their compute. Scaling to LTX, our MAD-LTX model outperforms all open-source competitors, and supports a comprehensive suite of text, ego, and object controls. Project page: \href https://vita-epfl.github.io/MAD-World-Model/ https://vita-epfl.github.io/MAD-World-Model/

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8bea8db7dd27364f1009a6297c31a6d7CVF · CVPR 2026

SAM 3D Body: Robust Full-Body Human Mesh Recovery

Yang, Xitong, Kukreja, Devansh, Pinkus, Don, Fan, Taosha, Park, Jinhyung, Shin, Soyong, Cao, Jinkun, Liu, Jia-Wei 等 14 位作者

原文摘要

We introduce SAM 3D Body (3DB), a promptable model for single-image full-body 3D human mesh recovery (HMR) that demonstrates state-of-the-art performance, with strong generalization and consistent accuracy in diverse in-the-wild conditions. 3DB estimates the human pose of the body, feet, and hands. It is the first model to use a new parametric mesh representation, Momentum Human Rig (MHR), which decouples skeletal pose and body shape. 3DB employs an encoder-decoder architecture and supports auxiliary prompts, including 2D keypoints and masks, enabling user-guided inference similar to the SAM family of models. We derive high-quality annotations from a multi-stage annotation pipeline that uses various combinations of manual keypoint annotation, differentiable optimization, multi-view geometry, and dense keypoint detection. Our data engine efficiently selects and processes data to ensure data diversity, collecting unusual poses and rare imaging conditions. We present a new evaluation dataset organized by pose and appearance categories, enabling nuanced analysis of model behavior. Our experiments demonstrate superior generalization and substantial improvements over prior methods in both qualitative user preference studies and traditional quantitative analysis. Both 3DB and MHR are open-source.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8bf3a43f02783cd1b56af8a34dde920dCVF · CVPR 2026

Illumination-Consistent Human-Scene Reconstruction from Monocular Video

Zheng, Rongbin, Li, Wensheng, Zeng, Lingzhe, Wang, Dong, Gao, Chengying

原文摘要

Reconstructing 3D humans and scenes from monocular videos is a challenging task, particularly due to human motion, varying illumination, and dynamic scene shadows. While recent works have explored scene disentanglement by jointly modeling humans and their surrounding scenes, they often overlook illumination and shadow effects--resulting in inconsistent human appearance and degraded scene realism. To address this gap, we propose a photometrically consistent integration of human and scene reconstruction based on 3D Gaussian Splatting, with a key focus on modeling spatially-varying illumination and shadows. Central to our method is a learnable light volume that provides localized lighting cues to human Gaussians, enabling more realistic and consistent appearance synthesis. To further ensure accurate human geometry and alignment, we adopt a two-stage reconstruction strategy: we first optimize a human mesh and then anchor Gaussians to the refined surface. In addition, we introduce an implicit shadow estimation module that disentangles cast shadows from the scene, thus supporting plausible human shadow synthesis. Our framework also facilitates human relighting and compositing into novel scenes with contextually appropriate lighting. Quantitative and qualitative results demonstrate that our method achieves state-of-the-art performance, producing consistent appearances, realistic illumination, and enhanced overall scene realism.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:8bfd62bc0dcb827e2f6475e136eaefe4CVF · CVPR 2026

UniDef: Universal Defense Against Unauthorized Image Manipulation

Shao, Mingwen, Meng, Lingzhuang, Lv, Xiang, Wu, Mengyao, Chen, Xinyuan, Zhang, Qiao, Liu, Chang, Qiao, Yuanjian 等 9 位作者

原文摘要

Image protection against unauthorized diffusion-based editing has achieved encouraging progress. However, existing methods face two critical limitations: (1) They only disturb the denoising direction at local step, resulting in generated images still retaining original or edited semantics. (2) Their optimization rely heavily on model-specific gradient, limiting transferable protection across different models and tasks. To address these challenges, we propose a Universal Defense (UniDef) framework for protection against unauthorized image manipulation. Specifically, we first discover that different variants of diffusion models tend to pursue a consistent distribution objective during complete denoising process. Based on this discovery, we design Consistent Distribution Deviation strategy to perturb the diffusion direction at the global denoising, thereby disrupting the overall image semantics. Furthermore, to mitigate model dependency, we devise a Finite Difference-based Jacobian Estimation module to approximate the global gradient in a model-agnostic manner, thus ensuring more transferable protection. Benefiting from the above designs, our method yields generated images no longer preserve the image semantic while possessing excellent generalization. Extensive experiments demonstrate that our UniDef not only outperforms existing methods, but also exhibits universal protection across diverse models and tasks.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:8c0e24f904ee90871f57afd039b299b5CVF · CVPR 2026

Text-Phase Synergy Network with Dual Priors for Unsupervised Cross-Domain Image Retrieval

Yang, Jing, Xue, Hui, Zhu, Shipeng, Fang, Pengfei

原文摘要

This paper studies unsupervised cross-domain image retrieval (UCDIR), which aims to retrieve images of the same category across different domains without relying on labeled data. Existing methods typically utilize pseudo-labels, derived from clustering algorithms, as supervisory signals for intra-domain representation learning and cross-domain feature alignment. However, these discrete pseudo-labels often fail to provide accurate and comprehensive semantic guidance. Moreover, the alignment process frequently overlooks the entanglement between domain-specific and semantic information, leading to semantic degradation in the learned representations and ultimately impairing retrieval performance. This paper addresses the limitations by proposing a Text-Phase Synergy Network with Dual Priors (TPSNet). Specifically, we first employ CLIP to generate a set of class-specific prompts per domain, termed as domain prompt, serving as a text prior that offers more precise semantic supervision. In parallel, we further introduce a phase prior, represented by domain-invariant phase features, which is integrated into the original image representations to bridge the domain distribution gaps while preserving semantic integrity. Leveraging the synergy of these dual priors, TPSNet significantly outperforms state-of-the-art methods on UCDIR benchmarks.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8c234660b60687c55d84f6471ce287ffCVF · CVPR 2026

ParkGaussian: Surround-view 3D Gaussian Splatting for Autonomous Parking

Wei, Xiaobao, Ye, Zhangjie, Gu, Yuxiang, Zhu, Zunjie, Guo, Yunfei, Shen, Yingying, Zhao, Shan, Lu, Ming 等 13 位作者

原文摘要

Parking is a critical task for autonomous driving systems (ADS), with unique challenges in crowded parking slots and GPS-denied environments. However, existing works focus on 2D parking slot perception, mapping, and localization, 3D reconstruction remains underexplored, which is crucial for capturing complex spatial geometry in parking scenarios. Naively improving the visual quality of reconstructed parking scenes does not directly benefit autonomous parking, as the key entry point for parking is the slots perception module. To address these limitations, we curate the first benchmark named ParkRecon3D, specifically designed for parking scene reconstruction. It includes sensor data from four surround-view fisheye cameras with calibrated extrinsics and dense parking slot annotations. We then propose ParkGaussian, the first framework that integrates 3D Gaussian Splatting (3DGS) for parking scene reconstruction. To further improve the alignment between reconstruction and downstream parking slot detection, we introduce a slot-aware reconstruction strategy that leverages existing parking perception methods to enhance the synthesis quality of slot regions. Experiments on ParkRecon3D demonstrate that ParkGaussian achieves state-of-the-art reconstruction quality and better preserves perception consistency for downstream tasks. The code and dataset will be released.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8c37b2c9aa7cc98af018ef767a73d8b6CVF · CVPR 2026

W2W: Language-Model-Based Trajectory Prediction with Reinforcement Learning

Xu, Zirui, Yang, Biao, Ni, Rongrong, Zhou, Zhongkai, Shen, Shaobo

原文摘要

Pedestrian trajectory prediction is crucial for applications such as autonomous driving and social robots. Recently, language model (LM)-based trajectory prediction has offered both prediction accuracy and interpretability. However, the L2 loss commonly used in trajectory prediction cannot be directly applied to LM optimization, resulting in degraded prediction performance. Moreover, current LM-based trajectory prediction methods lack explicit expressions of social interactions, and their scene descriptions are overly simplistic, making it challenging to impose practical scene constraints. To address these issues, we propose Write-to-Walk (W2W). First, we convert observed trajectories and interaction cues (companion/following/obstacle) into parsable textual prompts, so that interaction semantics are expressed more explicitly in the model input. Afterward, a T5-Small backbone is trained in a two-stage manner: (1) Full-parameter supervised fine-tuning with cross-entropy loss for language learning, enabling formatted question answering; (2) Reinforcement Learning (RL) to optimize W2W, where a reward function combining ADE error and off-road penalties strengthens scene constraints, producing future trajectories consistent with the scene context and further improving prediction accuracy. Experiments on benchmark datasets (ETH/UCY and SDD) demonstrate that W2W remains competitive with recent LM-based prediction methods and strong trajectory prediction baselines on ADE/FDE. The interpretability of LMs further supports W2W's deployment in safety-critical domains such as autonomous driving.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:8c3a24e2d968e1f35dfeb3e3912df884CVF · CVPR 2026

ReSAM: Refine, Requery, and Reinforce: Self-Prompting Point-Supervised Segmentation for Remote Sensing Images

Subhani, Muhammad Naseer

原文摘要

Interactive segmentation models such as the Segment Anything Model (SAM) have demonstrated remarkable generalization on natural images, but they perform suboptimally on remote sensing imagery (RSI) due to severe domain shifts and the scarcity of dense annotations. To address this limitation, we propose a point-supervised, self-prompting framework that adapts SAM to RSI using only sparse point annotations. Our method employs a Refine-Requery-Reinforce loop, in which coarse pseudo-masks are generated from initial points (Refine), improved with self-constructed box prompts (Requery), and embeddings are aligned with Soft Semantic Alignment (SSA) to mitigate error propagation. (Reinforce). Without relying on full-mask supervision, our approach progressively enhances SAM's segmentation quality and domain robustness through self-guided prompt adaptation. We evaluate our proposed method on three RSI benchmark datasets, WHU, HRSID, and NWPU VHR-10, demonstrating that it consistently outperforms pretrained SAM and recent point-supervised segmentation methods. Compared to the fully supervised model, our approach reduces the performance gap to 1.3% (WHU), 4.9% (HRSID), and 8.5% (NWPU) while relying only on 1-point annotations. Our results demonstrate that self-prompting and semantic alignment provide an efficient path towards scalable, point-level adaptation of foundation segmentation models for remote sensing applications.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8c513fb1031b5a7d835cc481bcc4b11dCVF · CVPR 2026

mmWaveFlow: Unified Enhancement and Generation of mmWave Human Point Clouds

Su, Chang, Jin, Beihong, Shi, Qiwen, Wang, Zhi

原文摘要

Millimeter-wave (mmWave) point clouds have attracted increasing interest in human sensing due to their robustness, privacy preservation, and low cost. However, their practical adoption is hindered by the inherent sparsity of data and the lack of large-scale annotated dataset. We revisit generative modeling and propose a unified flow-matching framework mmWaveFlow that unifies enhancement and generation of mmWave point clouds by learning an invertible transport between dense and sparse point clouds. We leverage paired data and a Cross-modal Latent Alignment module to enforce semantic alignment and bridge the modality gap. We find that condition-free flow matching is more vulnerable to latent path crossings, which impair transport. Therefore, we propose Origin-Aware Flow Matching (OA-Flow) by conditioning transport on the path origin to mitigate ambiguity in bidirectional transport. Results of experiments across multiple datasets demonstrate the effectiveness of mmWaveFlow for mmWave human point clouds generation and enhancement. We also observe consistent gains in downstream tasks, highlighting the promise of our framework for human sensing. Codes are available at https://github.com/suchang-99/mmWaveFlow.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8c682d27e6a2ddcffbff89c8f9aa334bCVF · CVPR 2026

High-Quality and Efficient Turbulence Mitigation with Events

Zhang, Xiaoran, Ding, Jian, Duan, Yuxing, Liu, Haoyue, Chen, Gang, Chang, Yi, Yan, Luxin

原文摘要

Turbulence mitigation (TM) is highly ill-posed due to the stochastic nature of atmospheric turbulence. Most methods rely on multiple frames recorded by conventional cameras to capture stable patterns in natural scenarios. However, they inevitably suffer from a trade-off between accuracy and efficiency: more frames enhance restoration at the cost of higher system latency and larger data overhead. Event cameras, equipped with microsecond temporal resolution and efficient sensing of dynamic changes, offer an opportunity to break the bottleneck. In this work, we present EHETM, a high-quality and efficient TM method inspired by the superiority of events to model motions in continuous sequences. We discover two key phenomena: (1) turbulence-induced events exhibit distinct polarity alternation correlated with sharp image gradients, providing structural cues for restoring scenes; and (2) dynamic objects form spatiotemporally coherent "event tubes" in contrast to irregular patterns within turbulent events, providing motion priors for disentangling objects from turbulence. Based on these insights, we design two complementary modules that respectively leverage polarity-weighted gradients for scene refinement and event-tube constraints for motion decoupling, achieving high-quality restoration with few frames. Furthermore, we construct two real-world event-frame turbulence datasets covering atmospheric and thermal cases. Extensive experiments show that EHETM outperforms SOTA methods, especially under scenes with dynamic objects, while reducing data overhead and system latency by approximately 77.3% and 89.5%, respectively.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8c8e80b3cb2690f266998195a516d72dCVF · CVPR 2026

TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size

Lionar, Stefan, Lee, Gim Hee

原文摘要

Physics-based humanoid control has achieved remarkable progress in enabling realistic and high-performing single-agent behaviors, yet extending these capabilities to cooperative human-object interaction (HOI) remains challenging. We present TeamHOI, a framework that enables a single decentralized policy to handle cooperative HOIs across any number of cooperating agents. Each agent operates using local observations while attending to other teammates through a Transformer-based policy network with teammate tokens, allowing scalable coordination across variable team sizes. To enforce motion realism while addressing the scarcity of cooperative HOI data, we further introduce a masked Adversarial Motion Prior (AMP) strategy that uses single-human reference motions while masking object-interacting body parts during training. The masked regions are then guided through task rewards to produce diverse and physically plausible cooperative behaviors. We evaluate TeamHOI on a challenging cooperative carrying task involving two to eight humanoid agents and varied object geometries. Finally, to promote stable carrying, we design a team-size- and shape-agnostic formation reward. TeamHOI achieves high success rates and demonstrates coherent cooperation across diverse configurations with a single policy.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8c9bba9ee328a3322e04dab09faf2483CVF · CVPR 2026

Hierarchical Visual Relocalization with Nearest View Synthesis from Feature Gaussian Splatting

Tao, Huaqi, Liu, Bingxi, Chen, Guangcheng, Tang, Fulin, He, Li, Zhang, Hong

原文摘要

Visual relocalization is a fundamental task in the field of 3D computer vision, estimating a camera's pose when it revisits a previously known scene. While point-based hierarchical relocalization methods have shown strong scalability and efficiency, they are often limited by sparse image observations and weak feature matching. In this work, we propose SplatHLoc, a novel hierarchical visual relocalization framework that uses Feature Gaussian Splatting as the scene representation. To address the sparsity of database images, we propose an adaptive viewpoint retrieval method that synthesizes virtual candidates with viewpoints more closely aligned with the query, thereby improving the accuracy of initial pose estimation. For feature matching, we observe that Gaussian-rendered features and those extracted directly from images exhibit different strengths across the two-stage matching process: the former performs better in the coarse stage, while the latter proves more effective in the fine stage. Therefore, we introduce a hybrid feature matching strategy, enabling more accurate and efficient pose estimation. Extensive experiments on both indoor and outdoor datasets show that SplatHLoc enhances the robustness of visual relocalization, setting a new state-of-the-art.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8cd565a10dbe90c74af3a7f1ffd59eb4CVF · CVPR 2026

HumanBA: Human-Aware Bundle Adjustment via Global Human-Camera Decoupling

Yang, Fengyuan, Sur, Tanuj, Tse, Tze Ho Elden, Yao, Angela

原文摘要

Recovering global human and camera motion from monocular video is essential for world-coordinate human reconstruction but remains challenging due to entangled motions in image space. Traditional SLAM methods estimate monocular camera motion but fail in scenes dominated by foreground objects such as humans. A common workaround is to mask out dynamic objects, yet this approach becomes brittle when humans occupy most of the view or the background is too noisy, leading to unstable tracking and loss of constraints. This paper takes the opposite stance and reintegrates human motion as informative landmarks. We introduce HumanBA, a human-aware bundle adjustment framework that transforms dynamic humans into usable constraints via motion decoupling. HumanBA subtracts the human-induced component from observed joint trajectories, isolating a camera-induced (pseudo-static) component that can be safely incorporated into bundle adjustment alongside background features. To mitigate noise in global human estimates, HumanBA applies motion refinements and motion-aware reliability weighting. Across EMDB and SLOPER4D benchmarks, we show consistent improvements on camera pose estimation and reduce global human reconstruction error, demonstrating the benefits of treating humans as dynamic yet informative landmarks. Our code is available at https://github.com/MartaYang/HumanBA.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8cd5f5442e01858f003124c4cbf0fb7cCVF · CVPR 2026

GP-4DGS: Probabilistic 4D Gaussian Splatting from Monocular Video via Variational Gaussian Processes

Kim, Mijeong, Kim, Jungtaek, Han, Bohyung

原文摘要

We present GP-4DGS, a novel framework that integrates Gaussian Processes (GPs) into 4D Gaussian Splatting (4DGS) for principled probabilistic modeling of dynamic scenes. While existing 4DGS methods focus on deterministic reconstruction, they are inherently limited in capturing motion ambiguity and lack mechanisms to assess prediction reliability. By leveraging the kernel-based probabilistic nature of GPs, our approach introduces three key capabilities: (i) uncertainty quantification for motion predictions, (ii) motion estimation for unobserved or sparsely sampled regions, and (iii) temporal extrapolation beyond observed training frames. To scale GPs to the large number of Gaussian primitives in 4DGS, we design spatio-temporal kernels that capture the correlation structure of deformation fields and adopt variational Gaussian Processes with inducing points for tractable inference. Our experiments show that GP-4DGS enhances reconstruction quality while providing reliable uncertainty estimates that effectively identify regions of high motion ambiguity. By addressing these challenges, our work takes a meaningful step toward bridging probabilistic modeling and neural graphics.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:8cd75a27677c18247179d13b61ec1d52CVF · CVPR 2026

Turning Pre-Trained Vision Transformers into End-to-End Histopathology Whole Slide Image Models for Survival Prediction

Li, Jiawen, Hu, Jiali, Ling, Xitong, Yan, Renao, Chen, Yuxuan, Guan, Tian, He, Yonghong

原文摘要

Conventional whole slide image (WSI) analysis pipelines follow a two-stage process. First, an image encoder, such as a vision transformer (ViT), is used to perform batched offline feature extraction on a series of tiles cropped from the WSI. Second, a multiple instance learning (MIL) model is trained with slide-level labels to obtain task-specific slide embeddings. However, several limitations exist: strong reliance on pre-trained weights of the tile encoder, the absence of receptive fields from the original image, and a lack of task-independent WSI representations. An ideal improvement would be to develop an end-to-end pre-trained WSI model, but training it from scratch will face challenges such as high training costs and computational complexity. In this work, we deconstruct the key steps of ViT-based pathology image representation and propose a conversion strategy called E2E-ViT, which transforms a vanilla ViT into an end-to-end pre-trained WSI model without introducing additional parameters. E2E-ViT directly inputs the entire tissue region in WSIs to efficiently feed image sequences into the transformer backbone, achieving information interaction from the original receptive fields and generating slide features. Through multiple survival prediction tasks, we demonstrate that transformed pre-trained ViTs outperform two-stage MIL models and slide foundation models (SFM). Our work presents a new end-to-end learning paradigm that provides a promising direction for the next generation of computational pathology models.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:8ce17049186bf7bf03b6060cf59c4c37CVF · CVPR 2026

GaussFusion: Improving 3D Reconstruction in the Wild with A Geometry-Informed Video Generator

Zhu, Liyuan, Narayana, Manjunath, Stary, Michal, Hutchcroft, Will, Wetzstein, Gordon, Armeni, Iro

原文摘要

We present GaussFusion, a novel approach for improving 3D Gaussian splatting (3DGS) reconstructions in the wild through geometry-informed video generation. GaussFusion mitigates common 3DGS artifacts, including floaters, flickering, and blur caused by camera pose errors, incomplete coverage, and noisy geometry initialization. Unlike prior RGB-based approaches limited to a single reconstruction pipeline, our method introduces a geometry-informed video-to-video generator that refines 3DGS renderings across both optimization-based and feed-forward methods. Given an existing reconstruction, we render a Gaussian primitive video buffer encoding depth, normals, opacity, and covariance, which the generator refines to produce temporally coherent, artifact-free frames. We further introduce an artifact synthesis pipeline that simulates diverse degradation patterns, ensuring robustness and generalization. GaussFusion achieves state-of-the-art performance on novel-view synthesis benchmarks, and an efficient variant runs in real time at 21 FPS while maintaining similar performance, enabling interactive 3D applications.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:8cf07054f57616fdd28802da5231bd09CVF · CVPR 2026

VDE: Training-Free Accelerating Rectified Flow Model via Velocity Decomposition and Estimation

Tan, Junwen, Liang, Jinglin, Chen, Hongyuan, Huang, Shuangping

原文摘要

Though rectified flow models have achieved remarkable performance in image, video, and 3D generation, their practical deployments are challenged by slow inference speeds. Prior acceleration methods reuse cached features from previous steps, which neglects the growing mismatch between static caches and the evolving input, leading to reduced output fidelity. This work proposes Velocity Decomposition and Estimation (VDE), a training-free acceleration method that shifts the paradigm from caching-and-reusing to decomposing-and-estimating. Specifically, VDE decomposes the model's velocity into components parallel and orthogonal to the input, exploiting their temporal predictability and directional consistency for precise, input-adaptive estimation. To prevent error accumulation, it periodically anchors the model's state via full forward passes. Extensive experiments on image and video generation tasks demonstrate that VDE achieves substantial acceleration with minimal loss in visual quality. Notably, VDE accelerates Flux by 3.22x and achieves an LPIPS of 0.069 on Qwen-Image, outperforming the best baseline with a 52.2% reduction. Code: https://github.com/Tan-Junwen/VDE

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8cf0a2dea6140c5cd97b42673a13eef2CVF · CVPR 2026

Vision-Oriented Lightweight Neural Architecture Search with Budget-Adaptive Evaluation

Fan, Yi, Yang, Yu-Bin

原文摘要

In the deep-learning-based computer vision community, Neural Architecture Search (NAS) has become the de-facto tool for acquiring task-optimal network structures. Nevertheless, NAS methods are trapped in a fundamental accuracy-efficiency dilemma: training-based approaches deliver reliable performance but incur prohibitive search costs, whereas training-free strategies are ultra-fast but often yield relatively unreliable rankings. To reconcile this conflict, we propose a vision-oriented lightweight training-based NAS framework. We first design six micro vision tasks whose training time is negligible, yet together they probe a broad spectrum of representational capacities. Built upon these tasks, we introduce a budget-adaptive performance evaluator to produce the most accurate ranking attainable within the limit. Experiments on popular NAS benchmarks show that our method achieves a ranking correlation higher than existing methods. Furthermore, we construct a search space from prevalent neural blocks and run our method at a cost close to training-free methods; the discovered architecture surpasses the current state-of-the-art under identical training recipes. Our codes are available at https://github.com/fanyi-plus/tf-nas.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Symphony: A Cognitively-Inspired Multi-Agent System for Long-Video Understanding

Yan, Haiyang, Zhou, Hongyun, Xu, Peng, Feng, Xiaoxue, Liu, Mengyi

原文摘要

Despite rapid developments and widespread applications of MLLM agents, they still struggle with long-form video understanding (LVU) tasks, which are characterized by high information density and extended temporal spans. Recent research on LVU agents demonstrates that simple task decomposition and collaboration mechanisms are insufficient for long-chain reasoning tasks. Moreover, directly reducing the time context through embedding-based retrieval may lose key information of complex problems. In this paper, we propose Symphony, a multi-agent system, to alleviate these limitations. By emulating human cognition patterns, Symphony decomposes LVU into fine-grained subtasks and incorporates a deep reasoning collaboration mechanism enhanced by reflection, effectively improving the reasoning capability. Additionally, Symphony provides a VLM-based grounding approach to analyze LVU tasks and assess the relevance of video segments, which significantly enhances the ability to locate complex problems with implicit intentions and large temporal spans. Experimental results show that Symphony achieves state-of-the-art performance on LVBench, LongVideoBench, VideoMME, and MLVU, with a 5.0% improvement over the prior state-of-the-art method on LVBench. Code is available at https://github.com/Haiyang0226/Symphony.

cs.CV · cs.MA

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Decoding 3D Perception via BrainSSD: Synergistic Fusion of EEG Representations from Static and Dynamic Visual Streams

Yao, Yincheng, Shi, Enze, Zhang, Shu

原文摘要

Understanding how the brain constructs coherent 3D visual percepts from multifaceted experiences remains a pivotal yet underexplored challenge. To investigate this, we introduce BrainSSD, a novel framework for decoding 3D representations from electroencephalography (EEG) signals. The core of BrainSSD is a neuro-inspired fusion architecture, Hierarchical Phase-Amplitude Coupling guided Fusion (HPACF), which synergistically integrates EEG from two distinct viewing paradigms: brief presentations of a static 3D object view, and sustained observation of the object undergoing full rotation. HPACF embodies two key principles of neural computation, namely hierarchical processing realized through multi-level cross-attention, and neural synchrony actualized by using a differentiable estimator of Phase-Amplitude Coupling (PAC) to dynamically guide the integration. The resulting fused representations are subsequently mapped to the visual domain via a multi-level alignment loss. Our framework establishes a new state-of-the-art across a range of EEG decoding tasks, achieving superior discriminative power and exceptional generative fidelity. Furthermore, our static-dynamic dominance analysis provides the first direct visual evidence for a functional specialization in the brain's 3D perception, revealing that neural responses to static object views primarily underpin the object's holistic structure and form, while responses to rotational observation are indispensable for resolving its fine-grained geometric details. Our work presents an advanced framework for probing EEG-based visual decoding and offers computational insights into the brain's synergistic strategies for 3D perception.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Learning Forgery-Aware Lip Representations Without Forgery Priors

Chen, Bofan, Zhu, Hongyu, He, Yi, Liang, Sichu, Wang, Shi-Lin

原文摘要

Visual Speaker Authentication (VSA) verifies identity by analyzing lip dynamics during prompted speech, offering enhanced privacy compared to full-face methods while maintaining discriminability for high-security applications. However, recent advances in talking face generation (TFG) have enabled realistic forgeries that closely mimic lip dynamics in sync with speech, posing severe threats to VSA systems. Prevailing defenses rely heavily on supervised classifiers trained on known forgeries via empirical risk minimization, resulting in poor generalization to unseen attacks, dependency on continuously updated fake data, and complete failure in the absence of effective forgery priors. In this paper, we revisit the design of forgery detectors and argue that over-reliance on fake priors hinders the exploitation of rich authenticity signals inherently present in real videos. We propose a novel detector trained exclusively on authentic data, learning forgery-aware representations through three key components: (1) lightweight modules that capture forgery-indicative statistics from real videos; (2) an asymmetric contrastive objective that compacts real samples while repelling potential forgeries in representation space; and (3) a theoretically grounded regularizer that shapes real representations into a tractable, isotropic Gaussian. To support rigorous evaluation, we introduce a benchmark suite spanning diverse TFG forgeries. Across eight modern forgery attacks and ten state-of-the-art (SOTA) detectors, our method achieves over a 10% reduction in error rates while preserving identity-verification capability with minimal overhead, and demonstrates consistent gains on datasets that better emulate real-world scenarios.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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EgoFlow: Gradient-Guided Flow Matching for Egocentric 6DoF Object Motion Generation

Saroha, Abhishek, Zeng, Huajian, Zuo, Xingxing, Cremers, Daniel, Wang, Xi

原文摘要

Understanding and predicting object motion from egocentric video is fundamental to embodied perception and interaction. However, generating physically consistent 6DoF trajectories remains challenging due to occlusions, fast motion, and the lack of explicit physical reasoning in existing generative models. We present EgoFlow, a flow-matching framework that synthesizes realistic and physically plausible trajectories conditioned on multimodal egocentric observations. EgoFlow employs a hybrid Mamba-Transformer-Perceiver architecture to jointly model temporal dynamics, scene geometry, and semantic intent, while a gradient-guided inference process enforces differentiable physical constraints such as collision avoidance and motion smoothness. This combination yields coherent and controllable motion generation without post-hoc filtering or additional supervision. Experiments on HD-EPIC, EgoExo4D, and HOT3D show that EgoFlow outperforms diffusion-based and transformer baselines in accuracy, generalization, and physical realism, reducing collision rates by up to 79%, and strong generalization to unseen scenes. Our results highlight the promise of flow-based generative modeling for scalable and physically grounded egocentric motion understanding. Project page: https://abhi-rf.github.io/egoflow/

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Topology-aware Feature Propagation for Unsupervised Non-rigid Point Cloud Correspondence

Chen, Haozhe, Li, Rui, Wang, Zhengbao, Zhu, Xinhao, Li, Linjie, Xiong, Tianyu, Ouyang, Xuan, Yang, Jiaqi

原文摘要

Unsupervised non-rigid point cloud correspondence aims to predict point-to-point correspondences without annotations. Existing methods leverage the spatial-relation-based feature propagation strategy that includes non-physical connections, which are sensitive to non-rigid deformation. To address this issue, we advocate to learn shape topology robust to non-rigid deformation, and propose the topology-aware feature propagation module integrated into a coarse-to-fine propagation and optimization pipeline. To extract point features robust to non-rigid deformation, we estimate keypoints as superpoints and encode superpoint features with topology weights, which learns reasonable topologies under non-rigid deformation. The vector quantization codebook is leveraged to enhance the original superpoint features with stored representative features across the dataset, improving feature robustness against shape variance. Robust point-wise correspondence is yielded after coarse-to-fine feature fusion and efficient test-time optimization. Extensive experiments on multiple benchmarks demonstrate the state-of-the-art performance of our method.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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FEAST: Fully Connected Expressive Attention for Spatial Transcriptomics

Jeong, Taejin, Kim, Joohyeok, Kim, Jinyeong, Kim, Chanyoung, Hwang, Seong Jae

原文摘要

Spatial Transcriptomics (ST) provides spatially-resolved gene expression, offering crucial insights into tissue architecture and complex diseases. However, its prohibitive cost limits widespread adoption, leading to significant attention on inferring spatial gene expression from readily available whole slide images. While graph neural networks have been proposed to model interactions between tissue regions, their reliance on pre-defined sparse graphs prevents them from considering potentially interacting spot pairs, resulting in a structural limitation in capturing complex biological relationships. To address this, we propose FEAST (Fully connected Expressive Attention for Spatial Transcriptomics), an attention-based framework that models the tissue as a fully connected graph, enabling the consideration of all pairwise interactions. To better reflect biological interactions, we introduce negative-aware attention, which models both excitatory and inhibitory interactions, capturing essential negative relationships that standard attention often overlooks. Furthermore, to mitigate the information loss from truncated or ignored context in standard spot image extraction, we introduce an off-grid sampling strategy that gathers additional images from intermediate regions, allowing the model to capture a richer morphological context. Experiments on public ST datasets show that FEAST surpasses state-of-the-art methods in gene expression prediction while providing biologically plausible attention maps that clarify positive and negative interactions. Our code is available at https://github.com/starforTJ/ FEAST.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Region-Wise Correspondence Prediction between Manga Line Art Images

Li, Yingxuan, Mao, Jiafeng, Qiu, Qianru, Matsui, Yusuke

原文摘要

Understanding region-wise correspondences between manga line art images is fundamental for high-level manga processing, supporting downstream tasks such as line art colorization and in-between frame generation. Unlike natural images that contain rich visual cues, manga line art consists only of sparse black-and-white strokes, making it challenging to determine which regions correspond across images. In this work, we introduce a new task: predicting region-wise correspondence between raw manga line art images without any annotations. To address this problem, we propose a Transformer-based framework trained on large-scale, automatically generated region correspondences. The model learns to suppress noisy matches and strengthen consistent structural relationships, resulting in robust patch-level feature alignment within and across images. During inference, our method segments each line art and establishes coherent region-level correspondences through edge-aware clustering and region matching. We construct manually annotated benchmarks for evaluation, and experiments across multiple datasets demonstrate both high patch-level accuracy and strong region-level correspondence performance, achieving 78.4-84.4% region-level accuracy. These results highlight the potential of our method for real-world manga and animation applications.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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RINO: Rotation-Invariant Non-Rigid Correspondences

Gao, Maolin, Hu-Chen, Shao Jie, Deng, Congyue, Marin, Riccardo, Guibas, Leonidas, Cremers, Daniel

原文摘要

Dense 3D shape correspondence remains a central challenge in computer vision and graphics as many deep learning approaches still rely on intermediate geometric features or handcrafted descriptors, limiting their effectiveness under non-isometric deformations, partial data, and non-manifold inputs. To overcome these issues, we introduce RINO, an unsupervised, rotation-invariant dense correspondence framework that effectively unifies rigid and non-rigid shape matching. The core of our method is the novel RINONet, a feature extractor that integrates vector-based SO(3)-invariant learning with orientation-aware complex functional maps to extract robust features directly from raw geometry. This allows for a fully end-to-end, data-driven approach that bypasses the need for shape pre-alignment or handcrafted features. Extensive experiments show unprecedented performance of RINO across challenging non-rigid matching tasks, including arbitrary poses, non-isometry, partiality, non-manifoldness, and noise.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Sketch2CT: Multimodal Diffusion for Structure-Aware 3D Medical Volume Generation

An, Delin, Wang, Chaoli

原文摘要

Diffusion probabilistic models have demonstrated significant potential in generating high-quality, realistic medical images, providing a promising solution to the persistent challenge of data scarcity in the medical field. Nevertheless, producing 3D medical volumes with anatomically consistent structures under multimodal conditions remains a complex and unresolved problem. We introduce Sketch2CT, a multimodal diffusion framework for structure-aware 3D medical volume generation, jointly guided by a user-provided 2D sketch and a textual description that captures 3D geometric semantics. The framework initially generates 3D segmentation masks of the target organ from random noise, conditioned on both modalities. To effectively align and fuse these inputs, we propose two key modules that refine sketch features with localized textual cues and integrate global sketch-text representations. Built upon a capsule-attention backbone, these modules leverage the complementary strengths of sketches and text to produce anatomically accurate organ shapes. The synthesized segmentation masks subsequently guide a latent diffusion model for 3D CT volume synthesis, enabling realistic reconstruction of organ appearances that are consistent with user-defined sketches and descriptions. Extensive experiments on public CT datasets demonstrate that Sketch2CT achieves superior performance in generating multimodal medical volumes. Its controllable, low-cost generation pipeline enables principled, efficient augmentation of medical datasets. Code is available at https://github.com/adlsn/Sketch2CT.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Event Structural Valley: A Unified Theoretical and Practical Framework for Event Camera Autofocus

Xiang, Xijie, Zhu, Lin, Zhang, Wei, Tian, Yonghong

原文摘要

Autofocus in dynamic environments remains challenging for conventional frame-based sensors, which often fail under fast motion, low light, or high dynamic range conditions. Event cameras, with microsecond temporal resolution and asynchronous brightness detection, offer a promising alternative. However, typical event-based autofocus methods assume that the sharpest focus corresponds to the maximum event rate. In this paper, we reveal a counterintuitive yet consistent phenomenon: the true focus actually corresponds to a local minimum in the event-rate curve. We theoretically derive this behavior from the physics of event generation and show that as defocus blur increases, the event rate first rises and then declines, forming a dual-peak-valley structure across focal distances. Based on this insight, we propose an Event Structural Valley-based Autofocus (ESVA) framework that identifies the valley between two dominant peaks as the true focal position. ESVA integrates structural smoothing, consistency filtering, and a dual-peak constraint to robustly recover the valley under noise and motion disturbances. Extensive experiments on multiple synthetic and real-world datasets demonstrate that ESVA delivers accurate and stable focus estimation, consistently outperforming existing event-only autofocus methods without requiring image reconstruction or supervision.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Common Inpainted Objects In-N-Out of Context

Yang, Tianze, Jordan, Tyson, Sun, Ruitong, Liu, Ninghao, Sun, Jin

原文摘要

We present Common Inpainted Objects In-N-Out of Context (COinCO), a novel dataset addressing the scarcity of out-of-context examples in existing vision datasets. By systematically replacing objects in COCO images through diffusion-based inpainting, we create 97,722 unique images featuring both contextually coherent and inconsistent scenes, enabling effective context learning. Each inpainted object is meticulously verified and categorized as in- or out-of-context through Large Vision Language Model assessments. We demonstrate three key tasks enabled by COinCO: (1) a fine-grained context reasoning approach that classifies objects as in- or out-of-context based on three criteria; (2) a novel Objects-from-Context prediction task that determines which new objects naturally belong in given scenes at both instance and clique level semantics, and (3) context-enhanced fake detection on state-of-the-art methods without fine-tuning. COinCO provides a controlled testbed with contextual variations, establishing a foundation for advancing context-aware visual understanding in computer vision, including image forensics. Code and dataset are available at https://co-in-co.github.io/.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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TopoMesh: High-Fidelity Mesh Autoencoding via Topological Unification

Luo, Guan, Li, Xiu, Chen, Rui, Yi, Xuanyu, Lin, Jing, Chen, Chia Hao, Liu, Jiahang, Zhang, Song-Hai 等 9 位作者

原文摘要

The dominant paradigm for high-fidelity 3D generation relies on a VAE-Diffusion pipeline, where the VAE's reconstruction capability sets a firm upper bound on generation quality. A fundamental challenge limiting existing VAEs is the representation mismatch between ground-truth meshes and network predictions: GT meshes have arbitrary, variable topology, while VAEs typically predict fixed-structure implicit fields (e.g., SDF on regular grids). This inherent misalignment prevents establishing explicit mesh-level correspondences, forcing prior work to rely on indirect supervision signals such as SDF or rendering losses. Consequently, fine geometric details, particularly sharp features, are poorly preserved during reconstruction. To address this, we introduce TopoMesh, a sparse voxel-based VAE that unifies both GT and predicted meshes under a shared Dual Marching Cubes (DMC) topological framework. Specifically, we convert arbitrary input meshes into DMC-compliant representations via a remeshing algorithm that preserves sharp edges using an Linfinity distance metric. Our decoder outputs meshes in the same DMC format, ensuring that both predicted and target meshes share identical topological structures. This establishes explicit correspondences at the vertex and face level, allowing us to derive explicit mesh-level supervision signals for topology, vertex positions, and face orientations with clear gradients. Our sparse VAE architecture employs this unified framework and is trained with Teacher Forcing and progressive resolution training for stable and efficient convergence. Extensive experiments demonstrate that TopoMesh significantly outperforms existing VAEs in reconstruction fidelity, achieving superior preservation of sharp features and geometric details, as shown in Fig. 1.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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