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首次观测 · 记录 #1CVF · CVPR 2026:cvf:4081b8fb25e362fff33f1e1f533748c2CVF · CVPR 2026

Mark4D: Temporally-Consistent Watermarking for 4D Gaussian Splatting

Lee, Jaejin, Jeong, Minjae, Park, Joonhyuk, Hwang, Yechan, Baek, Seunghun, Kim, Won Hwa

原文摘要

Embedding invisible and temporally consistent watermarks into dynamic 4D Gaussian Splatting (4DGS) models poses unique challenges due to continuous spatio-temporal deformation of Gaussians and diverse motion dynamics. Existing 3DGS watermarking methods, which directly fine-tune parameters within Gaussian splats, fail to preserve geometric fidelity and temporal coherence when extended to 4D settings. In this regime, we propose Mark4D, a temporally consistent watermarking technique that achieves robust, imperceptible, and motion-aware watermark embedding for 4DGS. Mark4D comprises 1) a decoder for watermark recovery in the latent video-text space for robustness against pixel-level distortions, 2) trajectory-aligned offsets that embed watermark signals along Gaussian motion paths to preserve geometry, and 3) a motion-adaptive loss weighting strategy that balances supervision across frames with varying motion intensities. Extensive experiments on synthetic and real-world dynamic scene datasets demonstrate the effectiveness of our method, establishing a foundation for secure and reliable protection of dynamic 4D scene assets.

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:4b418389586712e619224361267f2553CVF · CVPR 2026

OrionEdit: Bridging Reference and Source Images for Generalized Cross-Image Editing

Jiang, Zeyu, Po, Lai Man, Xu, Xuyuan, Wang, Yexin, Gong, Guoping, Wu, Haoxuan, Yan, Chenbo, Li, Kun 等 9 位作者

原文摘要

Multimodal image synthesis has made significant progress, yet most editing methods still rely on textual instructions, which are less direct than visual guidance. Recently, a new paradigm edits one image using another as reference, enabling more intuitive manipulation through visual exemplars. We formalize this setting as cross-image editing, where a source image is modified under one or more visual references. We propose OrionEdit, a unified framework that regulates editing via symmetric orthogonal subspace disentanglement and reverse-causal attention with information-flow masks enforcing unidirectional latent dependencies. Built on standard diffusion backbones, OrionEdit enables zero-shot multi-reference editing and outperforms open-source baselines, approaching proprietary models in fidelity and disentanglement. The model is available at https://github.com/cityuhkai/OrionEdit.

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:b0c6431da73341d5ac4d708fb47dc200CVF · CVPR 2026

Spatial-Aware VLA Pretraining through Visual-Physical Alignment from Human Videos

Feng, Yicheng, Zhang, Wanpeng, Wang, Ye, Luo, Hao, Yuan, Haoqi, Zheng, Sipeng, Lu, Zongqing

原文摘要

Vision-Language-Action (VLA) models provide a promising paradigm for robot learning by integrating visual perception with language-guided policy learning. However, most existing approaches rely on 2D visual inputs to perform actions in 3D physical environments, creating a significant gap between perception and action grounding. To bridge this gap, we propose a Spatial-Aware VLA Pretraining paradigm that enables models to acquire 3D spatial understanding before robot policy learning. Starting from pretrained vision-language models, we leverage large-scale human demonstration videos to extract 3D visual and 3D action annotations, forming a new source of supervision that aligns 2D visual observations with 3D spatial reasoning. We instantiate this paradigm with VIPA-VLA, a dual-encoder architecture that incorporates a 3D visual encoder to augment semantic visual representations with 3D-aware features, and aligns the two through visual-physical alignment pretraining. When adapted to downstream robot tasks, VIPA-VLA achieves significantly improved grounding between 2D vision and 3D action, resulting in more robust and generalizable robotic policies.

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:dd0c60274173a0d0db8ee38e8abf5c4bCVF · CVPR 2026

VideoFusion: A Spatio-Temporal Collaborative Network for Multi-modal Video Fusion

Tang, Linfeng, Wang, Yeda, Gong, Meiqi, Li, Zizhuo, Deng, Yuxin, Yi, Xunpeng, Li, Chunyu, Xu, Han 等 10 位作者

原文摘要

Compared to images, videos better reflect real-world acquisition and possess valuable temporal cues. However, existing multi-sensor fusion research predominantly integrates complementary context from multiple images rather than videos due to the scarcity of large-scale multi-sensor video datasets, limiting research in video fusion and the inherent difficulty of jointly modeling spatial and temporal dependencies in a unified framework. To this end, we construct M3SVD, a benchmark dataset with 220 temporally synchronized and spatially registered infrared-visible videos comprising 153,797 frames, bridging the data gap. Secondly, we propose VideoFusion, a multi-modal video fusion model that exploits cross-modal complementarity and temporal dynamics to generate spatio-temporally coherent videos from multi-modal inputs. Specifically, 1) a differential reinforcement module is developed for cross-modal information interaction and enhancement, 2) a complete modality-guided fusion strategy is employed to adaptively integrate multi-modal features, and 3) a bi-temporal co-attention mechanism is devised to dynamically aggregate forward-backward temporal contexts to reinforce cross-frame feature representations. Experiments reveal that VideoFusion outperforms existing image-oriented fusion paradigms in sequences, effectively mitigating temporal inconsistency and interference. Project and M3SVD: https://github.com/Linfeng-Tang/VideoFusion.

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:f116a6253ee6bd7d091ccf4924b04ce7CVF · CVPR 2026

Joint-Aligned Latent Action: Towards Scalable VLA Pretraining in the Wild

Luo, Hao, Wang, Ye, Zhang, Wanpeng, Yuan, Haoqi, Feng, Yicheng, Xu, Haiweng, Zheng, Sipeng, Lu, Zongqing

原文摘要

Despite progress, Vision-Language-Action models (VLAs) are limited by a scarcity of large-scale, diverse robot data. While human manipulation videos offer a rich alternative, existing methods are forced to choose between small, precisely-labeled datasets and vast in-the-wild footage with unreliable hand tracking labels. We present JALA, a pretraining framework that learns Jointly-Aligned Latent Actions. JALA bypasses full visual dynamic reconstruction, instead learns a predictive action embedding aligned with both inverse dynamics and real actions. This yields a transition-aware, behavior-centric latent space for learning from heterogeneous human data. We scale this approach with UniHand-Mix, a 7.5M video corpus (>2,000 hours) blending laboratory and in-the-wild footage. Experiments demonstrate that JALA generates more realistic hand motions in both controlled and unconstrained scenarios, significantly improving downstream robot manipulation performance in both simulation and real-world tasks. These results indicate that jointly-aligned latent actions offer a scalable pathway for VLA pretraining from human data.

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 ↗