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 animation with 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 learned model describes the motion 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.