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首次观测 · 记录 #1CVF · CVPR 2026:cvf:01a77fc0a16917221d22ca7cf0abe07dCVF · CVPR 2026

Unifying Precise Keyframes and Semantic Control via Multi-level Diffusion

Wu, Linjun, Yu, Jiejia, Jin, Leyang, Wang, He, Zheng, Bowen, Yang, Xu, Jiang, Hao, Xia, Fei 等 11 位作者

原文摘要

Text-conditioned human motion in-betweening leverages keyframes for spatio-temporal control, with text providing high-level semantic guidance for the transitions. However, existing methods are unable to establish a coherent alignment between textual semantics and the spatio-temporal constraints provided by keyframes, often resulting in insufficiently constrained motions with unintended behavior. Moreover, they struggle with precise spatial control, often generating motions that deviate from keyframe constraints. To address these issues, we propose a multi-level diffusion framework that integrates textual semantics with implicit cues from keyframe sequences to modulate global motion dynamics, while leveraging individual keyframes to guide local transitions around them. During inference, to ensure strict keyframe adherence, we propose a novel trajectory refinement strategy that adjusts the root positions of the generated motion, followed by diffusion imputation to refine the poses of the generated keyframes. Additionally, our framework enables semantics-preserving motion editing, allowing for plausible modifications while retaining the original motion semantics. Extensive experiments demonstrate that our method generates high-quality motions that strictly satisfy keyframe constraints while achieving precise semantic alignment.

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:0306d8a6b5d1209a408072f949f3a7a2CVF · CVPR 2026

RoMo: A Large-Scale, Richly Organized Dataset and Semantic Taxonomy for Human Motion Generation

Zhang, Jiahao, Liu, Joseph, Lee, Young-Yoon, Moon, Seonghyeon, Zordan, Victor, Tevet, Guy, Liu, C. Karen, Gould, Stephen 等 12 位作者

原文摘要

Success in generative modeling across language, image, and video demonstrates that large, well-curated datasets are the key driver for building capable models. 3D Human motion, however, has lagged behind, constrained by an unsatisfying choice between small, high-fidelity motion capture datasets and large-scale in-the-wild collections dominated by static or low-quality sequences.We introduce RoMo, a rich, large-scale, carefully curated dataset of in-the-wild human motions that resolves these tradeoffs. To ensure quality, we introduce a taxonomy-aware filtering pipeline that aggressively removes static and artifact-prone sequences. Every sequence is annotated with detailed captions and organized by a novel three-level semantic taxonomy. This hierarchical structure provides the first benchmark for fine-grained, per-category evaluation, revealing model strengths and weaknesses obscured by global metrics. We demonstrate that models trained on RoMo achieve state-of-the-art fidelity and diversity while gaining a superior understanding of complex, subtle text prompts. Finally, we release the Motion Toolbox to standardize metrics, data conversion, and visualization, establishing a foundation for reproducible and interpretable motion generation research.

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:10d6a9e802828fb58915bb6448d3bc83CVF · CVPR 2026

Uni3R: Unified 3D Reconstruction and Semantic Understanding via Generalizable Gaussian Splatting from Unposed Multi-View Images

Sun, Xiangyu, Jiang, Haoyi, Liu, Liu, Nam, Seungtae, Kang, Gyeongjin, Wang, Xinjie, Sui, Wei, Su, Zhizhong 等 11 位作者

原文摘要

Reconstructing and semantically interpreting 3D scenes from sparse 2D views remains a fundamental challenge in computer vision. Conventional methods often decouple semantic understanding from reconstruction or necessitate costly per-scene optimization, thereby restricting their scalability and generalizability. In this paper, we introduce Uni3R, a novel feed-forward framework that jointly reconstructs a unified 3D scene representation enriched with open-vocabulary semantics, directly from unposed multi-view images. Our approach leverages a Cross-View Transformer to robustly integrate information across arbitrary multi-view inputs, which then regresses a set of 3D Gaussian primitives endowed with semantic feature fields. This unified representation facilitates high-fidelity novel view synthesis, open-vocabulary 3D semantic segmentation, and depth prediction--all within a single, feed-forward pass. Extensive experiments demonstrate that Uni3R sets a new state of the art across multiple benchmarks, including in-domain datasets such as RE10K and ScanNet, as well as the out-of-domain dataset Mip-NeRF360. This work represents a new paradigm toward generalizable and unified 3D scene reconstruction and understanding.

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:12355e8d0d582d53084c826bea74590fCVF · CVPR 2026

GM-R^2: Generative Matching Learning for Unsupervised Geometric Representation and Registration

Jiang, Haobo, Yu, Liang, Zheng, Jianmin

原文摘要

This paper proposes GM-R^2, a novel Generative Matching Learning framework for unsupervised geometric descriptor learning and correspondence matching. By reformulating descriptor learning as geometry-conditioned cross-view image generation, GM-R^2 leverages the proxy supervisory signal from structurally aligned view synthesis to implicitly enforce feature consistency across correspondence, enabling robust 3D matching. To instantiate GM-R^2, we introduce Denoising-Agnostic Coupled ControlNet conditioned on depth maps as the required geometry-conditioned cross-view generator. It effectively extends the single-view generation of naive ControlNet to the cross-view via coupled depth-map input design and further remove the latent noise dependency to support geometry-only inference (expected by 3D matching). Moreover, we present Zoomable Equirectangular Projection for intrinsics-free point cloud-to-depth mapping that adaptively zooms into the angular region occupies by the narrow-FOV input for dense range-map acquisition. Extensive experiments on 3DMatch and ScanNet datasets verify the superior precision of our GM-R^2, even surpassing supervised methods.

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:382ed1dddf852ae9f7e255f130c2fdc0CVF · CVPR 2026

Unified Personalized Understanding, Generating and Editing

Zhong, Yu, Lin, Tianwei, Zhu, Ruike, Yuan, Yuqian, Zheng, Haoyu, Liang, Liang, Zhang, Wenqiao, Shao, Feifei 等 12 位作者

原文摘要

Unified large multimodal models (LMMs) have achieved remarkable progress in general-purpose multimodal understanding and generation. However, they still operate under a "one-size-fits-all" paradigm and struggle to model user-specific concepts (e.g., generate a photo of \texttt \ ) in a consistent and controllable manner. Existing personalization methods typically rely on external retrieval, which is inefficient and poorly integrated into unified multimodal pipelines. Recent personalized unified models introduce learnable soft prompts to encode concept information, yet they either couple understanding and generation or depend on complex multi-stage training, leading to cross-task interference and ultimately to fuzzy or misaligned personalized knowledge.We present OmniPersona, an end-to-end personalization framework for unified LMMs that, for the first time, integrates personalized understanding, generation, and image editing within a single architecture. OmniPersona introduces structurally decoupled concept tokens, allocating dedicated subspaces for different tasks to minimize interference, and incorporates an explicit knowledge replay mechanism that propagates personalized attribute knowledge across tasks, enabling consistent personalized behavior.To systematically evaluate unified personalization, we propose \texttt OmniPBench , extending the public UnifyBench concept set with personalized editing tasks and cross-task evaluation protocols integrating understanding, generation, and editing. Experimental results demonstrate that OmniPersona delivers competitive and robust performance across diverse personalization tasks. We hope OmniPersona will serve as a strong baseline and spur further research on controllable, unified personalization.

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:669bafe89f49882cb088aefdbf97649aCVF · CVPR 2026

FUSER: Feed-Forward Multiview 3D Registration Transformer and SE(3)$^N$ Diffusion Refinement

Jiang, Haobo, Xie, Jin, Yang, Jian, Yu, Liang, Zheng, Jianmin

原文摘要

Registration of multiview point clouds typically depends on extensive pairwise matching to build a pose graph for global synchronization, which is computationally expensive and ill-posed without holistic geometric constraints. In this paper, we propose FUSER, the first feed-forward multi-view registration transformer that processes all scans jointly in a unified, compact latent space to directly predict global poses without any pairwise estimation. To maintain tractability, FUSER employs a sparse 3D CNN to encode each scan into low-resolution superpoint features preserving absolute translation cues, followed by a Geometric Alternating Attention module for efficient intra- and inter-scan reasoning. Particularly, we transfer 2D attention priors from off-the-shelf foundation models (i.e., \pi^3) to enhance 3D feature attention. Building upon FUSER and its estimates, we further introduce FUSER-DF, an SE(3) diffusion refinement framework to correct FUSER's estimates through a denoising process over the joint SE(3)^N space. Here, FUSER serves as a surrogate multiview register to model the denoiser, and a prior-conditioned SE(3)^N variational lower bound is derived for denoising supervision. Extensive experiments on 3DMatch and ScanNet confirm the superior registration accuracy and efficiency 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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:69f45e725ba549e9d801fa9a4ce7e17dCVF · CVPR 2026

Denoising as Path Planning: Training-Free Acceleration of Diffusion Models with DPCache

Cui, Bowen, Wang, Yuanbin, Xu, Huajiang, Chen, Biaolong, Zhang, Aixi, Jiang, Hao, Jin, Zhengzheng, Liu, Xu 等 9 位作者

原文摘要

Diffusion models have demonstrated remarkable success in image and video generation, yet their practical deployment remains hindered by the substantial computational overhead of multi-step iterative sampling. Among acceleration strategies, caching-based methods offer a training-free and effective solution by reusing or predicting features across timesteps. However, existing approaches rely on fixed or locally adaptive schedules without considering the global structure of the denoising trajectory, often leading to error accumulation and visual artifacts. To overcome this limitation, we propose DPCache, a novel training-free acceleration framework that formulates diffusion sampling acceleration as a global path planning problem. DPCache constructs a Path-Aware Cost Tensor from a small calibration set to quantify the path-dependent error of skipping timesteps conditioned on the preceding key timestep. Leveraging this tensor, DPCache employs dynamic programming to select an optimal sequence of key timesteps that minimizes the total path cost while preserving trajectory fidelity. During inference, the model performs full computations only at these key timesteps, while intermediate outputs are efficiently predicted using cached features. Extensive experiments on DiT, FLUX, and HunyuanVideo demonstrate that DPCache achieves strong acceleration with minimal quality loss, outperforming prior acceleration methods by +0.031 ImageReward at 4.87x speedup and even surpassing the full-step baseline by +0.028 ImageReward at 3.54x speedup on FLUX, validating the effectiveness of our path-aware global scheduling framework. Code is available at https://github.com/argsss/DPCache.

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:81495d1eae54bbbca72a41d4a73252c0CVF · CVPR 2026

A Cross-view Fusion Framework for Robust 6-DoF Grasp Pose Estimation

Zhu, Kangjian, Jiang, Haobo, Qian, Jianjun, Xie, Jin

原文摘要

In this paper, we propose a cross-view fusion framework that enhances the robustness of 6-DoF grasp pose estimation in corner views.Our framework alleviates occlusion by incorporating an auxiliary view and avoids the time-consuming, task-agnostic multi-view reconstruction through a post-fusion strategy.To enable cross-view fusion, we propose a self-supervised contrastive learning strategy that leverages cross-view associations to regularize point cloud features.In brief, a cross-view point pair is considered a match if the two points correspond to the same 3D location, and a non-match if they represent distinct grasp directions.The learning strategy significantly enhances the spatial consistency and direction distinctiveness of point features, thereby facilitating cross-view fusion and improving estimation robustness.Furthermore, we propose a cross-view-aligned cylinder integration module to fuse grasp-relevant geometry into a comprehensive representation.Specifically, the module first aligns the cross-view points and features according to their similarity to enhance the robustness against noise.Subsequently, these points are registered into the cylindrical coordinate frame, emphasizing the rotation-symmetric geometry which is important for grasping.Finally, local self-attention and seed cross-attention layers are alternately employed, respectively enabling interactions within single views and across views, which supports fine-grained representation of grasp-relevant geometry.Our framework achieves strong performance on the GraspNet-1Billion benchmark and in real-world applications. Code is available at GitHub.

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:97a839cebb6485d9f76b4acada4c2839CVF · CVPR 2026

VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation

Yuan, Jiayi, Jiang, Haobo, Soh, De Wen, Zhao, Na

原文摘要

This paper presents VGGT-360, a novel training-free framework for zero-shot, geometry-consistent panoramic depth estimation. Unlike prior view-independent training-free approaches, VGGT-360 reformulates the task as panoramic reprojection over multi-view reconstructed 3D models by leveraging the intrinsic 3D consistency of VGGT-like foundation models, thereby unifying fragmented per-view reasoning into a coherent panoramic understanding. To achieve robust and accurate estimation, VGGT-360 integrates three plug-and-play modules that form a unified panorama-to-3D-to-depth framework: (i) Uncertainty-guided adaptive projection slices panoramas into perspective views to bridge the domain gap between panoramic inputs and VGGT's perspective prior. It estimates gradient-based uncertainty to allocate denser views to geometry-poor regions, yielding geometry-informative inputs for VGGT. (ii) Structure-saliency enhanced attention strengthens VGGT's robustness during 3D reconstruction by injecting structure-aware confidence into its attention layers, guiding focus toward geometrically reliable regions and enhancing cross-view coherence. (iii) Correlation-weighted 3D model correction refines the reconstructed 3D model by reweighting overlapping points using attention-inferred correlation scores, providing a consistent geometric basis for accurate panoramic reprojection. Extensive experiments show that VGGT-360 outperforms both trained and training-free state-of-the-art methods across multiple resolutions and diverse indoor and outdoor datasets. The code is available at https://github.com/Yuanjiayii/VGGT-360.

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:a2d639fe45b948e5f80b9753e1bb2da1CVF · CVPR 2026

MetroGS: Efficient and Stable Reconstruction of Geometrically Accurate High-Fidelity Large-Scale Scenes

Chen, Kehua, Mao, Tianlu, Ma, Xinzhu, Jiang, Hao, Li, Zehao, Liu, Zihan, Gao, Shuqin, Zhao, Honglong 等 11 位作者

原文摘要

Recently, 3D Gaussian Splatting and its derivatives have achieved significant breakthroughs in large-scale scene reconstruction. However, how to efficiently and stably achieve high-quality geometric fidelity remains a core challenge. To address this issue, we introduce MetroGS, a novel Gaussian Splatting framework for efficient and robust reconstruction in complex urban environments. Our method is built upon a distributed 2D Gaussian Splatting representation as the core foundation, serving as a unified backbone for subsequent modules. To handle potential sparse regions in complex scenes, we propose a structured dense enhancement scheme that utilizes SfM priors and a pointmap model to achieve a denser initialization, while incorporating a sparsity compensation mechanism to improve reconstruction completeness. Furthermore, we design a progressive hybrid geometric optimization strategy that organically integrates monocular and multi-view optimization to achieve efficient and accurate geometric refinement. Finally, to address the appearance inconsistency commonly observed in large-scale scenes, we introduce a depth-guided appearance modeling approach that learns spatial features with 3D consistency, facilitating effective decoupling between geometry and appearance and further enhancing reconstruction stability. Experiments on large-scale urban datasets demonstrate that MetroGS achieves superior geometric accuracy, rendering quality, offering a unified solution for high-fidelity large-scale scene reconstruction. Project page: https://m3phist0.github.io/MetroGS.

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:ab4710fbea9d5c640cffb5857fdc5368CVF · CVPR 2026

ShreddingNet: Coarse-to-Fine Restoration for Multi-Source Shredded Manuscripts

Cui, Haoyang, Jiang, Hao, Mu, Yadong

原文摘要

As an important research task of human cultural heritage, the restoration of artworks and calligraphy is of great significance. Seldom existing works have taken the multi-source (i.e., fragments are not ensured to be from the same piece of artworks) fragment-oriented restoration task into account. We propose ShreddingNet, a coarse-to-fine two-stage pipeline for multi-source manuscript restoration that operates without restrictive conditions. The proposed coarse stage compares the features of each fragment, selecting top-K candidates and clustering fragments by source. This design leverages the key insight that erroneous matches rarely cross source boundaries, enabling high-precision clustering. The proposed fine-grained stage evaluates candidates, yielding matching scores and filters out erroneous matching pairs from the candidate set; producing more precise final matching pairs for global assembly. Experiments conducted on more than 4,000 images from two datasets demonstrate the average reconstruction F1-score achieves 98.37%, which is 5.72% higher than the current state-of-the-art method, confirming the method's effectiveness and robustness. Source code is available at github.com/tqychy/shreddingnet.

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:b30dbca36e474ac1e8f2909810126881CVF · CVPR 2026

End-to-End Language-Action Model for Humanoid Whole Body Control

Wang, Yuxuan, Jiang, Haobin, Yao, Shiqing, Ding, Ziluo, Lu, Zongqing

原文摘要

Existing humanoid control systems often rely on teleoperation or modular generation pipelines that separate language understanding from physical execution. However, the former is entirely human-driven, and the latter lacks tight alignment between language commands and physical behaviors. In this paper, we present SENTINEL, a fully end-to-end language-action framework for humanoid whole-body control. We construct a large-scale dataset by tracking human motions in simulation using a pretrained whole body controller, combined with their text annotations. The model directly maps language commands and proprioceptive inputs to low-level actions without any intermediate representation. The model generates action chunks using flow matching, which can be subsequently refined by a residual action head for real-world deployment. Our method exhibits strong semantic understanding and stable execution on humanoid robots in both simulation and real-world deployment, and also supports multi-modal extensions by converting inputs into texts.

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:e91bbc9c9b43b88b4ae1fb62cb26e163CVF · CVPR 2026

Models as Lego Builders: Assembling Malice from Benign Blocks via Semantic Blueprints

Li, Chenxi, Liu, Xianggan, Shen, Dake, Du, Yaosong, Yao, Zhibo, Jiang, Hao, Jiang, Linyi, Cao, Chengwei 等 12 位作者

原文摘要

Despite the rapid progress of Large Vision-Language Models (LVLMs), the integration of visual modalities introduces new safety vulnerabilities that adversaries can exploit to elicit biased or malicious outputs. In this paper, we demonstrate an underexplored vulnerability via semantic slot filling, where LVLMs complete missing slot values with unsafe content even when the slot types are deliberately crafted to appear benign. Building on this finding, we propose StructAttack, a simple yet effective single-query jailbreak framework under black-box settings. StructAttack decomposes a harmful query into a central topic and a set of benign-looking slot types, then embeds them as structured visual prompts (e.g., mind maps, tables, or sunburst diagrams) with small random perturbations. Paired with a completion-guided instruction, LVLMs automatically recompose the concealed semantics and generate unsafe outputs without triggering safety mechanisms. Although each slot appears benign in isolation (local benignness), StructAttack exploits LVLMs' reasoning to assemble these slots into coherent harmful semantics. Extensive experiments on multiple models and benchmarks show the efficacy of our proposed StructAttack.

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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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:f63abd72f6d89ea421dd5d23bc6e0356CVF · CVPR 2026

CICA: Coupling Confidence-Aware Pretraining with Confidence-Informed Attention for Robust Multimodal Sentiment Analysis

Jiang, Haoyu, Chen, Xiaoliang, Miao, Duoqian, Qin, Xiaolin, Li, Xianyong, Du, Yajun

原文摘要

Multimodal sentiment analysis requires integrating language, visual, and acoustic cues, yet these modalities are often noisy, incomplete, or contradictory, making fusion unreliable. Most existing methods assume uniformly trustworthy modalities and thus degrade when signals conflict. To address this, we propose CICA, a framework that couples Confidence-Aware Pretraining with Confidence-Informed Attention. In pretraining, each modality encoder learns to estimate the reliability of its own representation, producing both embeddings and confidence scores. These scores then guide a confidence-informed attention mechanism, which strengthens contributions from reliable modalities while suppressing noisy or conflicting ones, enabling adaptive fusion under varying signal conditions. CICA achieves state-of-the-art performance across four major benchmarks on MOSI, MOSEI, CH-SIMS, and CH-SIMSv2. It achieves MAE 0.630 and Corr 0.855 on MOSI, and MAE 0.489 and Corr 0.856 on MOSEI, significantly surpassing prior methods. Consistent improvements are also observed across Acc-7, Acc-2, and F1 metrics. Under noisy and missing-modality conditions, CICA maintains significantly more stable performance, indicating improved robustness and interpretability.

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 ↗