Interactive Evolutionary Approach to Character Animation

We describe in this paper an approach to the animation of articulated figure using a combination of forward kinematics, neural net and genetic algorithms. A set of kinematics motion data for an articulated model is evolved to generate a set of varied motion that performs according to an objective function set by the user. Neural net is used to constrain the search space of possible motions. User interaction with the process ensures a certain style evolves from the myriad of possibilities. We show how our technique makes it possible to create libraries of reusable motions from a small number of example motions.