Motion-motif graphs

Abstract

We present a technique to automatically distill a motion-motif graph from an arbitrary collection of motion capture data. Motion motifs represent clusters of similar motions and together with their encompassing motion graph they lend understandable structure to the contents and connectivity of large motion datasets. They can be used in support of motion compression, the removal of redundant motions, and the creation of blend spaces. This paper develops a string-based motif-finding algorithm which allows for a user-controlled compromise between motif length and the number of motions in a motif. It allows for time warps within motifs and assigns the majority of the input data to relevant motifs. Results are demonstrated for large datasets (more than 100,000 frames) with computation times of tens of minutes.