Multi-mode representation of motion data

We investigate the use of multi-linear models to represent human motion data. We show that naturally occurring modes in several classes of motion can be used to efficiently represent the motions for various animation tasks, such as dimensionality reduction or synthesis of new motions by morphing. We show that especially for the approximations of motions by few components the reduction based on a multi-linear model can be considerably better than one obtained by principal component analysis (PCA).