Cloning with Gesture Expressivity

Virtual environments allow human beings to be represented by virtual humans or avatars. Users can share a sense of virtual presence is the avatar looks like the real human it represents. This classically involves turning the avatar into a clone with the real human¿s appearance and voice. However, the possibility of cloning the gesture expressivity of a real person has received little attention so far. Gesture expressivity combines the style and mood of a person. Expressivity parameters have been defined in earlier works for animating embodied conversational agents. In this work, we focus on expressivity in wrist motion. First, we propose algorithms to estimate three expressivity parameters from captured wrist 3D trajectories: repetition, spatial extent and temporal extent. Then, we conducted perceptual study through a user survey the relevance of expressivity for recognizing individual human. We have animated a virtual agent using the expressivity estimated from individual humans, and users have been asked whether they can recognize the individual human behind each animation.