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AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale

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arxiv 2406.18537 v1 pith:DYAWLPA6 submitted 2024-05-16 cs.CV cs.AIcs.GRcs.RO

classification cs.CVcs.AIcs.GRcs.RO
keywords humandatasetaddbiomechanicsmotiondatadynamicsforcephysics
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. COMETH: Convex Optimization for Multiview Estimation and Tracking of Humans

    cs.CV 2025-08 conditional novelty 5.0 of 10

    COMETH uses multi-source convex inverse kinematics with biomechanical constraints and a Kalman filter to fuse 3D skeletons from multiple cameras, improving multi-person tracking accuracy over OpenPTrack and BeFine.

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