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

Abstract

In this work, 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. In a first stage, Hidden Markov Models of eleven different styles of gait 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. An informal user evaluation of the synthesized sequences showed that the naturalness of motions is preserved after linear interpolation.