Style machines

We approach the problem of stylistic motion synthesis by learning motion patterns from a highly varied set of motion capture sequences. Each sequence may have a distinct choreography, performed in a distinct style. Learning identifies common choreographic elements across sequences, the different styles in which each element is performed, and a small number of stylistic degrees of freedom which span the many variations in the dataset. The learned model can synthesize novel motion data in any interpolation or extrapolation of styles. For example, it can convert novice ballet motions into the more graceful modern dance of an expert. The model can also be driven by video, by scripts, or even by noise to generate new choreography and synthesize virtual motion-capture in many styles. In Proceedings of SIGGRAPH 2000, July 23-28, 2000. New Orleans, Louisiana, USA. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part ...

• Type: Conference
• Year: 2000
• Where: SIGGRAPH
• Authors: Matthew Brand, Aaron Hertzmann
• Keywords: Computer Graphics, Electric Information Technology, Mitsubishi Electric Information, SIGGRAPH 2000, Technology Center America