Human motion is the composite consequence of multiple elements, including the action performed and a motion signature that captures the distinctive pattern of movement of a particular individual. We develop a new algorithm that is capable of extracting these motion elements and recombining them in novel ways. The algorithm analyzes motion data spanning multiple subjects performing different actions. The analysis yields a generative motion model that can synthesize new motions in the distinctive styles of these individuals. Our algorithms can also recognize people and actions from new motions by comparing motion signatures and action parameters.