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.
KeywordsMixture Model Style Variable Motion Synthesis Character Animation User Friendly Graphical Interface
Unable to display preview. Download preview PDF.
- 1.Li, Y., Wang, T., Shum, H.Y.: Motion texture: A two-level statistical model for character motion synthesis. In: Proc. ACM SIGGRAPH, pp. 465–472 (2002)Google Scholar
- 2.Grochow, K., Martin, S.L., Hertzmann, A., Popović, Z.: Style-based inverse kinematics. In: Proc. ACM SIGGRAPH, pp. 522–531 (2004)Google Scholar
- 3.Brand, M., Hertzmann, A.: Style machines. In: Proc. ACM SIGGRAPH, pp. 183–192 (2000)Google Scholar
- 5.Gales, M., Young, S.: The theory of segmental hidden Markov models. Technical report, Cambridge Univ. Eng. Dept (1993)Google Scholar
- 6.Lawrence, N.D.: Gaussian process latent variable models for visualisation of high dimensional data. In: Proc. 16th NIPS (2004)Google Scholar
- 8.Brand, M.: Pattern discovery via entropy minimization. In: Heckerman, D., Whittaker, C. (eds.) Artificial Intelligence and Statistics, vol. 7, Morgan Kaufmann, Los Altos (1999)Google Scholar