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Leveraging depth cameras and wearable pressure sensors for full-body kinematics and dynamics capture

Authors:

Jinxiang ChaiAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 33, Issue 6

Article No.: 221, Pages 1 - 14

https://doi.org/10.1145/2661229.2661286

Published: 19 November 2014 Publication History

Abstract

We present a new method for full-body motion capture that uses input data captured by three depth cameras and a pair of pressure-sensing shoes. Our system is appealing because it is low-cost, non-intrusive and fully automatic, and can accurately reconstruct both full-body kinematics and dynamics data. We first introduce a novel tracking process that automatically reconstructs 3D skeletal poses using input data captured by three Kinect cameras and wearable pressure sensors. We formulate the problem in an optimization framework and incrementally update 3D skeletal poses with observed depth data and pressure data via iterative linear solvers. The system is highly accurate because we integrate depth data from multiple depth cameras, foot pressure data, detailed full-body geometry, and environmental contact constraints into a unified framework. In addition, we develop an efficient physics-based motion reconstruction algorithm for solving internal joint torques and contact forces in the quadratic programming framework. During reconstruction, we leverage Newtonian physics, friction cone constraints, contact pressure information, and 3D kinematic poses obtained from the kinematic tracking process to reconstruct full-body dynamics data. We demonstrate the power of our approach by capturing a wide range of human movements and achieve state-of-the-art accuracy in our comparison against alternative systems.

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Cited By

Wang SDeng YDeng MYu HZhou JChen DKomura TWu JZhu B(2024)An Eulerian Vortex Method on Flow MapsACM Transactions on Graphics10.1145/368799643:6(1-14)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687996
Smith IScheme EBateman S(2024)Mapping Real World Locomotion Speed to the Virtual World in Large Field of View Virtual EnvironmentsProceedings of the 2024 ACM Symposium on Spatial User Interaction10.1145/3677386.3682077(1-12)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3677386.3682077
Zhang HRen SYuan HZhao JLi FSun SLiang ZYu TShen QCao X(2024)MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensors2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02063(21842-21852)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02063
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Index Terms

Leveraging depth cameras and wearable pressure sensors for full-body kinematics and dynamics capture
1. Computing methodologies
  1. Computer graphics
    1. Animation

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 33, Issue 6

November 2014

704 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2661229

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Copyright © 2014 ACM.

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Publication History

Published: 19 November 2014

Published in TOG Volume 33, Issue 6

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Cited By

Wang SDeng YDeng MYu HZhou JChen DKomura TWu JZhu B(2024)An Eulerian Vortex Method on Flow MapsACM Transactions on Graphics10.1145/368799643:6(1-14)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687996
Smith IScheme EBateman S(2024)Mapping Real World Locomotion Speed to the Virtual World in Large Field of View Virtual EnvironmentsProceedings of the 2024 ACM Symposium on Spatial User Interaction10.1145/3677386.3682077(1-12)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3677386.3682077
Zhang HRen SYuan HZhao JLi FSun SLiang ZYu TShen QCao X(2024)MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensors2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02063(21842-21852)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02063
Tao RZhang XRen HDong YYang X(2024)A unified particle method for fluid simulation in ship fire scenarioOcean Engineering10.1016/j.oceaneng.2024.119266312(119266)Online publication date: Nov-2024
https://doi.org/10.1016/j.oceaneng.2024.119266
Liu JZhang TSun S(2024)Review of deep learning algorithms in molecular simulations and perspective applications on petroleum engineeringGeoscience Frontiers10.1016/j.gsf.2023.10173515:2(101735)Online publication date: Mar-2024
https://doi.org/10.1016/j.gsf.2023.101735
Tao RZhang XRen HYang XZhou Y(2024)Wall-bounded flow simulation on vortex dynamicsComputers & Graphics10.1016/j.cag.2024.103990122(103990)Online publication date: Aug-2024
https://doi.org/10.1016/j.cag.2024.103990
YANG WLI YXING SCAI JWANG X(2023)Lightweight multi-person motion capture system in the wildSCIENTIA SINICA Informationis10.1360/SSI-2022-039753:11(2230)Online publication date: 31-Oct-2023
https://doi.org/10.1360/SSI-2022-0397
Mourot LHoyet LClerc FHellier P(2023)UnderPressure: Deep Learning for Foot Contact Detection, Ground Reaction Force Estimation and Footskate CleanupComputer Graphics Forum10.1111/cgf.1463541:8(195-206)Online publication date: 20-Mar-2023
https://doi.org/10.1111/cgf.14635
Keyvanian SJohnson MFigueroa N(2023)Learning Realistic Joint Space Boundaries for Range of Motion Analysis of Healthy and Impaired Human Arms2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids)10.1109/Humanoids57100.2023.10375147(1-8)Online publication date: 12-Dec-2023
https://doi.org/10.1109/Humanoids57100.2023.10375147
Tao RRen HZhou Y(2022)A Ship Firefighting Training Simulator with Physics-Based SmokeJournal of Marine Science and Engineering10.3390/jmse1008114010:8(1140)Online publication date: 18-Aug-2022
https://doi.org/10.3390/jmse10081140
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