Abstract
Animation of human motion is one of the most challenging topics in computer graphics. This is due to the large number of degrees of freedom of the body and to our ability to detect unnatural motion. Keyframing and interpolation remains the form of animation that is preferred by most animators because of the control and flexibility it provides. However this is a labour intensive process that requires skills that take years to acquire. Human motion capture techniques provide accurate measurement of the motion of a performer that can be mapped onto an animated character to provide strikingly natural animation. This raises the problem of how to allow an animator to modify captured movement to produce a desired animation whilst preserving the natural quality. This thesis introduces a new approach to the animation of human motion based on combining the flexibility of keyframing with the visual quality of motion capture data. In particular it addresses the problem of synthesising natural inbetween motion for sparse keyframes. This thesis proposes to obtain this motion by sampling high quality human motion capture data. The problem of keyframe interpolation is formulated as a search problem in a graph. This presents two difficulties: The complexity of the search makes it impractical for the large databases of motion capture required to model human motion. The second difficulty is that the global temporal structure in the data may not be preserved in the search. To address these difficulties this thesis introduces a layered framework that both reduces the complexity of the search and preserves the global temporal structure of the data. The first layer is a simplification of the graph obtained by clustering methods. This layer enables efficient planning of the search for a path between start and end keyframes. The second layer directly samples segments of the original motion data to synthesise realistic inbetween motion for the keyframes. A number of additional contributions are made including novel representations for human motion, pose similarity cost functions, dynamic programming algorithms for efficient search and quantitative evaluation methods. Results of realistic inbetween motion are presented with databases of up to 120 sequences (35000 frames). Key words: Human Motion Synthesis, Motion Capture, Character Animation, Graph Search, Clustering, Unsupervised Learning, Markov Models, Dynamic Programming.