Learning Finite-State Machine Controllers From Motion Capture Data

With characters in computer games and interactive media increasingly being based on real actors, the individuality of an actor's performance should not only be reected in the appearance and animation of the character but also in the Articial Intelligence that governs the character's behavior and interactions with the environment. Machine learning methods applied to motion capture data provide a way of doing this. This paper presents a method for learning the parameters of a Finite State Machine controller. The method learns both the transition probabilities of the Finite State Machine and also how to select animations based on the current state.