Stylistic gait synthesis based on hidden Markov models

Joëlle Tilmanne,Thierry Dutoit,Alexis Moinet

doi:10.1186/1687-6180-2012-72

Joëlle Tilmanne, Thierry Dutoit + Show 1 more

Open Access

https://doi.org/10.1186/1687-6180-2012-72

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Abstract

In this work we present an expressive gait synthesis system based on hidden Markov models (HMMs), following and modifying a procedure originally developed for speaking style adaptation, in speech synthesis. A large database of neutral motion capture walk sequences was used to train an HMM of average walk. The model was then used for automatic adaptation to a particular style of walk using only a small amount of training data from the target style. The open source toolkit that we adapted for motion modeling also enabled us to take into account the dynamics of the data and to model accurately the duration of each HMM state. We also address the assessment issue and propose a procedure for qualitative user evaluation of the synthesized sequences. Our tests show that the style of these sequences can easily be recognized and look natural to the evaluators.

Highlights

Human motion is a very complex field of study
While its use is currently mostly limited to the entertainment industry, with 3D movies, video games, virtual agents, etc., other domains could benefit from a realistic automatic motion synthesis, in the same way as they already benefit from motion capture [2]
5.2 Results 5.2.1 Neutral walk modeling For our hidden Markov models (HMMs) training and synthesis, we followed the method explained in Section 5.1 and adapted the functions originally implemented for speech within the HMM-based speech synthesis system (HTS) to our procedure

Summary

Introduction

Human motion is a very complex field of study. The components of our behaviors, which are so natural to the human eye, can hardly be separated into physiological processes, the personal style of every human, or some kind of additional “style” or “mood” that influences the final motion, as presented in many works (see for instance [1]). Automatically extracting that information is a very difficult task, as stylistic variation is intrinsically merged with the basic motion, the individuality of the subject and the time-variability of the gesture (two motions by the same subject will never be exactly the same). A broad field of applications can be found for human motion synthesis. While its use is currently mostly limited to the entertainment industry, with 3D movies, video games, virtual agents, etc., other domains could benefit from a realistic automatic motion synthesis, in the same way as they already benefit from motion capture [2]. Medical applications could use it for instance to control active prostheses, or try to detect and understand the motion of motor impaired individuals [3]. New applications in the field of the animation of virtual characters in 3D could

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