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Data-driven Inverse Dynamics for Human Motion
SIGGRAPH Asia 2016

Xiaolei Lv 1,2  Jinxiang Chai 3  Shihong Xia 1

1 Institute of Computing Technology,Chinese Academy of Sciences 
2 University of Chinese Academy of Sciences 
3 Texas A&M University

Abstract
Inverse dynamics is an important and challenging problem in human motion modeling, synthesis and simulation, as well as in robotics and biomechanics. Previous solutions to inverse dynamics are often noisy and ambiguous particularly when double stances occur. In this paper, we present a novel inverse dynamics method that accurately reconstructs biomechanically valid contact information, including center of pressure, contact forces, torsional torques and internal joint torques from input kinematic human motion data. Our key idea is to apply statistical modeling techniques to a set of preprocessed human kinematic and dynamic motion data captured by a combination of an optical motion capture system, pressure insoles and force plates. We formulate the data-driven inverse dynamics problem in a maximum a posteriori (MAP) framework by estimating the most likely contact information and internal joint torques that are consistent with input kinematic motion data. We construct a low-dimensional data-driven prior model for contact information and internal joint torques to reduce ambiguity of inverse dynamics for human motion. We demonstrate the accuracy of our method on a wide variety of human movements including walking, jumping, running, turning and hopping and achieve stateof- the-art accuracy in our comparison against alternative methods. In addition, we discuss how to extend the data-driven inverse dynamics framework to motion editing, filtering and motion control.
Video
Obtaining the data

On request, we make all the algorithm relative data available for scientific purposes. To obtain a copy,please send an email to XSH AT ICT DOT AC DOT CN, stating
    1.name, title or position, and institution or affiliation;
    2.intended use of the data, further information about you and your work;
    Please understand that we can only provide the data to you if you are a senior project manager or senior researcher at your institution.
    We would also like to ask you to acknowledge the origin of the data by citing the above paper in any publication using the human motion capture data.Please note that you are not allowed to pass the data to a third party without prior permission.

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Data-driven Inverse Dynamics for Human Motion. SIGGRAPH Asia 2016. [PDF 29,446KB] [Main Video 55,781KB] [Supplementary Video 37,360KB]