On-line Motion Blending for Real-time Locomotion Generation

Abstract

In this thesis, we present an integrated framework of on-line motion blending for locomotion generation. We first provide a novel scheme for incremental timewarping, which always guarantees that the time goes forward. We also provide a novel scheme for blending joint angles based on quaternion algebra to guarantee their consistent representations. Combining the idea of motion blending with that of posture rearrangement, we introduce a motion transition graph to address on-line motion blending and transition simultaneously. Guided by a stream of motion specifications, our motion synthesis scheme moves from node to node in an on-line manner while blending a motion at a node and generating a transition motion at an edge. For smooth on-line motion transition, we also attach a set of example transition motions to an edge. Finally we show how to adapt the blended motion to the target character and the environment in an on-line, real-time manner.