(An) importance-based approach for computer puppetry

Abstract

Computer puppetry maps the movements of a performer to an animated character in real-time. In this thesis, we provide a comprehensive solution to the problem of transferring the observations of the motion capture sensors to an animated character whose size and shape may be different from the performer``s. Our goal is to map as many of the important aspects of the motion to the target character as possible, while meeting the online, real-time demands of computer puppetry. We adopt a Kalman filter scheme that ad-dresses motion capture noise issues in this setting. We provide the notion of importances that allow determining which aspects of the performance must be kept in the resulting motion based on interaction of the performer with its environment and self-interaction among the performer``s segments. We intro-duce a novel inverse kinematics solver that realizes these important aspects formulated with geometric constraints within tight real-time restriction. Our approach is demonstrated by its application to broadcast television performances.