Precomputed Motion Maps for Unstructured Motion Capture

Abstract

We present in this paper a solution for extracting high-quality motions from unstructured motion capture databases at interactive rates. The proposed solution is based on automatically-built motion graphs, and offers two key contributions. First, we show how precomputed expansion trees (or motion maps) coupled with new heuristics and backtracking techniques are able to significantly improve the time taken to search for motions satisfying user constraints. Second, we show that when feature-based transitions are employed for constructing the underlying motion graph, the connectivity of motion maps is greatly increased, allowing the overall method to perform search and synthesis at interactive frame rates. We demonstrate the effectiveness of our approach with the problem of extracting path-following motions around obstacles from a motion graph structure at interactive performances.