Human Motion Synthesis Using Window-Based Local Principal Component Analysis

This paper introduces an approach to performance animation that uses window-based principal component analysis (WLPCA). Our key idea is to construct a series of local models from a prerecorded motion database and utilize them to construct full-body human motion in a maximum a posteriori frame work. We have demonstrated the effectiveness of our approach by synthesizing a variety of human actions. Given an appropriate motion capture database, the results are comparable in quality to the ground truth data. We have also evaluated the performance of our approach by leave-one-out experiments and by comparing to two baseline algorithms.