Realistic synthesis of novel human movements from a database of motion capture examples

Abstract

Presents a system that can synthesize novel motion sequences from a database of motion capture examples. This is achieved through learning a statistical model from the captured data which enables the realistic synthesis of new movements by sampling the original captured sequences. New movements are synthesized by specifying the start and end keyframes. The statistical model identifies segments of the original motion capture data to generate novel motion sequences between the keyframes. The advantage of this approach is that it combines the flexibility of keyframe animation with the realism of motion capture data.