UniHands: Unifying Various Wild-Collected Keypoints for Personalized Hand Reconstruction
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917840676716544 |
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| author | Zhang, Menghe Kim, Joonyeoup Liang, Yangwen Wang, Shuangquan Song, Kee-Bong |
| author_facet | Zhang, Menghe Kim, Joonyeoup Liang, Yangwen Wang, Shuangquan Song, Kee-Bong |
| contents | Accurate hand motion capture and standardized 3D representation are essential for various hand-related tasks. Collecting keypoints-only data, while efficient and cost-effective, results in low-fidelity representations and lacks surface information. Furthermore, data inconsistencies across sources challenge their integration and use. We present UniHands, a novel method for creating standardized yet personalized hand models from wild-collected keypoints from diverse sources. Unlike existing neural implicit representation methods, UniHands uses the widely-adopted parametric models MANO and NIMBLE, providing a more scalable and versatile solution. It also derives unified hand joints from the meshes, which facilitates seamless integration into various hand-related tasks. Experiments on the FreiHAND and InterHand2.6M datasets demonstrate its ability to precisely reconstruct hand mesh vertices and keypoints, effectively capturing high-degree articulation motions. Empirical studies involving nine participants show a clear preference for our unified joints over existing configurations for accuracy and naturalism (p-value 0.016). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11845 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UniHands: Unifying Various Wild-Collected Keypoints for Personalized Hand Reconstruction Zhang, Menghe Kim, Joonyeoup Liang, Yangwen Wang, Shuangquan Song, Kee-Bong Computer Vision and Pattern Recognition Human-Computer Interaction Accurate hand motion capture and standardized 3D representation are essential for various hand-related tasks. Collecting keypoints-only data, while efficient and cost-effective, results in low-fidelity representations and lacks surface information. Furthermore, data inconsistencies across sources challenge their integration and use. We present UniHands, a novel method for creating standardized yet personalized hand models from wild-collected keypoints from diverse sources. Unlike existing neural implicit representation methods, UniHands uses the widely-adopted parametric models MANO and NIMBLE, providing a more scalable and versatile solution. It also derives unified hand joints from the meshes, which facilitates seamless integration into various hand-related tasks. Experiments on the FreiHAND and InterHand2.6M datasets demonstrate its ability to precisely reconstruct hand mesh vertices and keypoints, effectively capturing high-degree articulation motions. Empirical studies involving nine participants show a clear preference for our unified joints over existing configurations for accuracy and naturalism (p-value 0.016). |
| title | UniHands: Unifying Various Wild-Collected Keypoints for Personalized Hand Reconstruction |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2411.11845 |