Grafting Locomotive Motions

Abstract

The notion of transplanting limbs to enhance a motion capture database is appealing and has been recently introduced [Sha04], [Ike04]. A key difficulty in the process is identifying believable combinations. Not all transplantations are successful; we also need to identify appropriate frames in the different clips that are “cutpasted.” In this paper, we describe motion grafting, a method to synthesize new believable motion using existing motion captured data. In our deterministic scheme designed for locomotive actions, motion grafts increase the number of combinations by mixing independent kinematics chains with a base motion in a given clip. Our scheme uses a cluster graph data structure to establish correlation among grafts so that the result is believable and synchronized.