Real-time synthesis of 3D animations by learning parametric gaussians using self-organizing mixture networks

In this paper, we present a novel real-time approach to synthesizing 3D character animations of required style by adjusting a few parameters or scratching mouse cursor. Our approach regards learning captured 3D human motions as parametric Gaussians by the self-organizing mixture network (SOMN). The learned model describes motions under the control of a vector variable called the style variable, and acts as a probabilistic mapping from the low-dimensional style values to high-dimensional 3D poses. We have designed a pose synthesis algorithm and developed a user friendly graphical interface to allow the users, especially animators, to easily generate poses by giving style values. We have also designed a style-interpolation method, which accepts a sparse sequence of key style values and interpolates it and generates a dense sequence of style values for synthesizing a segment of animation. This key-styling method is able to produce animations that are more realistic and natural-looking than those synthesized by the traditional key-framing technique. © Springer-Verlag Berlin Heidelberg 2006.