Key-styling: learning motion style for real-time synthesis of 3D animation

In this paper, we present a novel real-time motion synthesis approach that can generate 3D character animationwith required style. The effectiveness of our approach comes from learning captured 3D human motion as a self-organizing mixture network (SOMN); of parametric Gaussians.The learnedmodel describes themotion under the control of a vector variable called style variable, and acts as a probabilistic mapping from the low-dimensional style values to the high-dimensional 3D poses. We design a pose synthesis algorithm to allow the user to generate poses by specifying new style values. We also propose a novel motion synthesis method, the key-styling, which accepts a sparse sequence of key style values and interpolates a dense sequence of style values to synthesize an animation. Key-styling is able to produce animations that are more realistic and natural-looking than those synthesized with the traditional key-keyframing technique. Copyright # 2006 John Wiley & Sons, Ltd.