GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909572463067136 |
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| author | Fu, Rao Zhang, Dingxi Jiang, Alex Fu, Wanjia Funk, Austin Ritchie, Daniel Sridhar, Srinath |
| author_facet | Fu, Rao Zhang, Dingxi Jiang, Alex Fu, Wanjia Funk, Austin Ritchie, Daniel Sridhar, Srinath |
| contents | Understanding bimanual human hand activities is a critical problem in AI and robotics. We cannot build large models of bimanual activities because existing datasets lack the scale, coverage of diverse hand activities, and detailed annotations. We introduce GigaHands, a massive annotated dataset capturing 34 hours of bimanual hand activities from 56 subjects and 417 objects, totaling 14k motion clips derived from 183 million frames paired with 84k text annotations. Our markerless capture setup and data acquisition protocol enable fully automatic 3D hand and object estimation while minimizing the effort required for text annotation. The scale and diversity of GigaHands enable broad applications, including text-driven action synthesis, hand motion captioning, and dynamic radiance field reconstruction. Our website are avaliable at https://ivl.cs.brown.edu/research/gigahands.html . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04244 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities Fu, Rao Zhang, Dingxi Jiang, Alex Fu, Wanjia Funk, Austin Ritchie, Daniel Sridhar, Srinath Computer Vision and Pattern Recognition Understanding bimanual human hand activities is a critical problem in AI and robotics. We cannot build large models of bimanual activities because existing datasets lack the scale, coverage of diverse hand activities, and detailed annotations. We introduce GigaHands, a massive annotated dataset capturing 34 hours of bimanual hand activities from 56 subjects and 417 objects, totaling 14k motion clips derived from 183 million frames paired with 84k text annotations. Our markerless capture setup and data acquisition protocol enable fully automatic 3D hand and object estimation while minimizing the effort required for text annotation. The scale and diversity of GigaHands enable broad applications, including text-driven action synthesis, hand motion captioning, and dynamic radiance field reconstruction. Our website are avaliable at https://ivl.cs.brown.edu/research/gigahands.html . |
| title | GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.04244 |