AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909232255729664 |
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| author | Werling, Keenon Kaneda, Janelle Tan, Alan Agarwal, Rishi Skov, Six Van Wouwe, Tom Uhlrich, Scott Bianco, Nicholas Ong, Carmichael Falisse, Antoine Sapkota, Shardul Chandra, Aidan Carter, Joshua Preatoni, Ezio Fregly, Benjamin Hicks, Jennifer Delp, Scott Liu, C. Karen |
| author_facet | Werling, Keenon Kaneda, Janelle Tan, Alan Agarwal, Rishi Skov, Six Van Wouwe, Tom Uhlrich, Scott Bianco, Nicholas Ong, Carmichael Falisse, Antoine Sapkota, Shardul Chandra, Aidan Carter, Joshua Preatoni, Ezio Fregly, Benjamin Hicks, Jennifer Delp, Scott Liu, C. Karen |
| contents | While reconstructing human poses in 3D from inexpensive sensors has advanced significantly in recent years, quantifying the dynamics of human motion, including the muscle-generated joint torques and external forces, remains a challenge. Prior attempts to estimate physics from reconstructed human poses have been hampered by a lack of datasets with high-quality pose and force data for a variety of movements. We present the AddBiomechanics Dataset 1.0, which includes physically accurate human dynamics of 273 human subjects, over 70 hours of motion and force plate data, totaling more than 24 million frames. To construct this dataset, novel analytical methods were required, which are also reported here. We propose a benchmark for estimating human dynamics from motion using this dataset, and present several baseline results. The AddBiomechanics Dataset is publicly available at https://addbiomechanics.org/download_data.html. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18537 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale Werling, Keenon Kaneda, Janelle Tan, Alan Agarwal, Rishi Skov, Six Van Wouwe, Tom Uhlrich, Scott Bianco, Nicholas Ong, Carmichael Falisse, Antoine Sapkota, Shardul Chandra, Aidan Carter, Joshua Preatoni, Ezio Fregly, Benjamin Hicks, Jennifer Delp, Scott Liu, C. Karen Computer Vision and Pattern Recognition Artificial Intelligence Graphics Robotics While reconstructing human poses in 3D from inexpensive sensors has advanced significantly in recent years, quantifying the dynamics of human motion, including the muscle-generated joint torques and external forces, remains a challenge. Prior attempts to estimate physics from reconstructed human poses have been hampered by a lack of datasets with high-quality pose and force data for a variety of movements. We present the AddBiomechanics Dataset 1.0, which includes physically accurate human dynamics of 273 human subjects, over 70 hours of motion and force plate data, totaling more than 24 million frames. To construct this dataset, novel analytical methods were required, which are also reported here. We propose a benchmark for estimating human dynamics from motion using this dataset, and present several baseline results. The AddBiomechanics Dataset is publicly available at https://addbiomechanics.org/download_data.html. |
| title | AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics Robotics |
| url | https://arxiv.org/abs/2406.18537 |