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Motion capture data segmentation using Riemannian manifold learning

Wang Bin,Weibin Liu,Weiwei Xing

Published 2019 in Comput. Animat. Virtual Worlds

ABSTRACT

Due to the inherent nonlinear nature of data, traditional linear methods have some limitations in finding the intrinsic dimensions of motion capture (Mo‐cap) data. Mo‐cap data are more in line with the characteristics of the manifold. Assuming that the data are initially a low‐dimensional manifold and uniformly sampled in high‐dimensional Euclidean space, manifold learning recovers low‐dimensional manifold structures from high‐dimensional sampled data. This paper proposes an automatic segmentation method based on geodesics by introducing a Riemannian manifold. We convert Mo‐cap data from Euler angles into quaternions, calculate the intrinsic mean of the motion sequence, hemispherize quaternions, and use logarithmic and exponential mapping to calculate geodesic distances instead of quaternions. The experimental results show that the algorithms can achieve automatic segmentation and have a better segmentation effect.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    Comput. Animat. Virtual Worlds

  • Publication date

    2019-06-10

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.1002/cav.1885
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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