GESTURE GENERATION BY IMITATION

In an effort to extend traditional human-computer interfaces research has introduced embodied agents to utilize the modalities of everyday human-human communication, like facial expression, gestures and body postures. However, giving computer agents a human-like body introduces new challenges. Since human users are very sensitive and critical concerning bodily behavior the agents must act naturally and individually in order to be believable. This dissertation focuses on conversational gestures. It shows how to generate conversational gestures for an animated embodied agent based on annotated text input. The central idea is to imitate the gestural behavior of a human individual. Using TV show recordings as empirical data, gestural key parameters are extracted for the generation of natural and individual gestures. The gesture generation task is solved in three stages