Abstract
While reconstructing human poses in 3D from inexpen- sive 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 70h of motion and force plate data, totaling more than 24 mil- lion frames. To construct this dataset, novel analytical methods were required, which are also reported here. We propose a benchmark for esti- mating human dynamics from motion using this dataset, and present several baseline results. The AddBiomechanics Dataset is publicly avail- able at addbiomechanics.org/download/ data.html.
Original language | English |
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Title of host publication | Lecture Notes in Computer Science |
Subtitle of host publication | Computer Vision – ECCV 2024. 18th European Conference Milan, Italy, September 29–October 4, 2024 Proceedings, Part LXXXVIII |
Editors | Ales Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol |
Place of Publication | Switzerland |
Publisher | Springer Nature Switzerland AG |
Pages | 490-508 |
ISBN (Electronic) | 9783031732232 |
ISBN (Print) | 9783031732225 |
DOIs | |
Publication status | Acceptance date - 3 Jul 2024 |
Event | The 18th European Conference on Computer Vision ECCV 2024 - Milan, Italy Duration: 1 Oct 2024 → 4 Oct 2024 Conference number: 18 https://eccv.ecva.net/ |
Publication series
Name | Lecture Notes in Computer Science |
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ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 161103349 |
Conference
Conference | The 18th European Conference on Computer Vision ECCV 2024 |
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Abbreviated title | ECCV 2024 |
Country/Territory | Italy |
City | Milan |
Period | 1/10/24 → 4/10/24 |
Internet address |
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Dive into the research topics of 'AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale'. Together they form a unique fingerprint.Datasets
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Full Body Kinematics and Ground Reaction Forces of Fifty Heterogeneous Runners Completing Treadmill Running at Various Speeds and Gradients
Carter, J. (Creator), Chen, X. (Creator), Cazzola, D. (Creator), Trewartha, G. (Creator) & Preatoni, E. (Creator), University of Bath, 30 May 2024
DOI: 10.15125/BATH-01341
Dataset