Learning silhouette features for control of human motion
People, even without training, can move in expressive ways to play charades, pantomime a winning touchdown, or act out a story for a child. The goal of our research is to provide untrained users with a vision-based interface for interactively controlling complex human motions. The insight behind our system is that the domain knowledge from a pre-recorded motion database allows plausible movement to be inferred from the incomplete information obtained from simple vision processing. The experimental scenario we use in this research is a single user animating two people swing dancing. We present a low cost camera-based system (one PC and three video cameras) that allows an untrained user to “drive” the dance of the couple interactively (Figure 1). The user can change the high-level structure of the dance, introducing a different sequence of steps, rotations, or movements. The final sequence retains the quality of previously captured dance yet reflects the user’s higher-level goals and spontaneity. Our approach yields results for more complex behaviors and longer sequences than have been demonstrated in previous silhouette-based systems.