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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 h 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 addbiomechanics.org/download/_data.html .

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AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale

  • 会议论文

  • 发表日期：

    2025年

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  • Keenon Werling,
  • Janelle Kaneda,
  • Tian 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

摘要

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 h 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 addbiomechanics.org/download/_data.html .