Abstract
In this thesis, we describe a new framework for synthesizing believable motion from high-level linguistic descriptions of human movement. The framework bridges the gap between the “word space” and the “motion space”. Word space consists of elements that are phrases relevant to movements and operators like concatenation. Motion space consists of elements and operators as well. However, elements in motion space are difficult to determine. We present techniques for extracting elements in motion space and for piecing them together. At first, we analyze the existent phrases relative to the movements and construct the syntax of animation script language. The animation script language that defines the syntactic and semantic attributes for describing human movement is based on the XML Schema structures. Furthermore, we develop animation script translator to connect the linguistic descriptions with underlying motions. Next, a large repertoire of character motions must be made available. One common solution to this problem is motion capture. However, on-line motion generation using motion capture data is difficult since the data is relatively unstructured. We present techniques for analyzing motion capture data to extract primitive motions. The inferred primitive motions constitute a probabilistic finite state automaton (PFSA). We annotate each primitive motion using our animation script language. After these steps, we transfer the low-level, frame-based motion capture data into a semantic structure appropriate for real-time motion synthesis. Motion synthesizer takes charge of seamlessly stitching the primitive motion into a smooth continuously streams of motion. Motion can then be simply generated by using linguistic descriptions of human movement. The motions are generated in real-time so that we can author novel, complex motions interactively. Our approach is demonstrated by many synthesized sequences of visual compelling Tai-Chi Chuang motion.