AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale

May 16, 2024 · Entered Twilight · 🏛 European Conference on Computer Vision

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Repo contents: .gitignore, .idea, README.md, requirements.txt, src, test, update_dataset.sh

Authors Keenon Werling, Janelle Kaneda, Alan Tan, Rishi Agarwal, Six Skov, Tom Van Wouwe, Scott Uhlrich, Nicholas Bianco, Carmichael Ong, Antoine Falisse, Shardul Sapkota, Aidan Chandra, Joshua Carter, Ezio Preatoni, Benjamin Fregly, Jennifer Hicks, Scott Delp, C. Karen Liu arXiv ID 2406.18537 Category cs.CV: Computer Vision Cross-listed cs.AI, cs.GR, cs.RO Citations 9 Venue European Conference on Computer Vision Repository https://github.com/keenon/InferBiomechanics ⭐ 28 Last Checked 5 days ago

Abstract

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.