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Expressive Gait Synthesis Using PCA and Gaussian Modeling

  • Joëlle Tilmanne
  • Thierry Dutoit
Conference paper
  • 1.1k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6459)

Abstract

In this paper we analyze walking sequences of an actor performing walk under eleven different states of mind. These walk sequences captured with an inertial motion capture system are used as training data to model walk in a reduced dimension space through principal component analysis (PCA). In that reduced PC space, the variability of walk cycles for each emotion and the length of each cycle are modeled using Gaussian distributions. Using this modeling, new sequences of walk can be synthesized for each expression, taking into account the variability of walk cycles over time in a continuous sequence.

Keywords

Motion synthesis expressivity PCA variability 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Joëlle Tilmanne
    • 1
  • Thierry Dutoit
    • 1
  1. 1.TCTS LabUniversity of MonsMonsBelgium

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