Dyn-HaMR: Recovering 4D Interacting Hand Motion from a Dynamic Camera
Zhengdi Yu, Stefanos Zafeiriou, Tolga Birdal
2025年份
8顶会引用
摘要
Input video Dyn-HaMR (Ours) HaMeR Camera motion Hand motion Figure 1. Dyn-HaMR as a remedy for the motion entanglement in the wild. The green and red arrows represent the direction of the hand motion. Dyn-HaMR (Ours) can disentangle the camera and object poses to recover the 4D global hand motion in the real world whilst state-of-the-art 3D hand reconstruction methods like HaMeR [37], IntagHand [25] and ACR [51] fail to do so since they cannot disentangle the sources of motion.
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引用它的顶会 Paper8
- Vision-Language-Action Pretraining from Large-Scale Human VideosHao Luo, Yicheng Feng, Wanpeng Zhang, Sipeng Zheng 等ICML 2026 · 被引用 104 次
- Geometric Neural Distance Fields for Learning Human Motion PriorsZhengdi Yu, Simone Foti, Linguang Zhang, Amy Zhao 等CVPR 2026 · 被引用 8 次
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- HandX: Scaling Bimanual Motion and Interaction GenerationZimu Zhang, Yucheng Zhang, Xiyan Xu, Ziyin Wang 等CVPR 2026 · 被引用 2 次
- UniHand: A Unified Model for Diverse Controlled 4D Hand Motion ModelingZhihao Sun, Tong Wu, Ruirui Tu, Daoguo Dong 等ICLR 2026 · 被引用 2 次
它引用的顶会 Paper28
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- Deep Patch Visual OdometryZachary Teed, Lahav Lipson, Jia DengNeurIPS 2023 · 被引用 323 次
- H2O: Two Hands Manipulating Objects for First Person Interaction RecognitionTaein Kwon, Bugra Tekin, Jan Stühmer, Federica Bogo 等ICCV 2021 · 被引用 271 次
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