Human Motion for Virtual People

While computer animation is currently widely used to create characters in games, films, and various other applications, techniques such as motion capture and keyframing are still relatively expensive. Automatic acquisition of secondary motion and/or motion prototyping using machine learning might be a solution to this problem. Our paper presents an application of the Q-learning algorithms to generate action sequences for animated characters. The techniques can be used in both deterministic and nondeterministic environments to generate actions which can later be incorporated into more complex animation sequences. The paper presents an application of both deterministic and non-deterministic updates of the Q-learning algorithm to automatic acquisition of motion. Results obtained from the learning system are also compared to human motion and conclusions are drawn.