Continuous Control of Style and Style Transitions through Linear Interpolation in Hidden Markov Model Based Walk Synthesis

  • Joëlle Tilmanne
  • Thierry Dutoit
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7380)


We present a Hidden Markov Model (HMM) based stylistic walk synthesizer, where the synthesized styles are combinations or exaggerations of the walk styles present in the training database. Our synthesizer is also capable of generating walk sequences with controlled style transitions. In a first stage, Hidden Markov Models of eleven different gait styles are trained, using a database of motion capture walk sequences. In a second stage, the probability density functions inside the stylistic models are interpolated or extrapolated in order to synthesize walks with styles or style intensities that were not present in the training database. A continuous model of the style parameter space is thus constructed around the eleven original walk styles. Qualitative user evaluation of the synthesized sequences showed that the naturalness of motions is preserved after linear interpolation between styles and that evaluators are sensitive to the interpolation factor.


HMM gait style control synthesis motion capture 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Joëlle Tilmanne
    • 1
  • Thierry Dutoit
    • 1
  1. 1.Numediart Institute / TCTS Lab.University of Mons (UMons)MonsBelgium

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