Style subspaces for character animation

In this paper, we present a novel method for editing stylistic human motions. We represent styles as differences between stylistic motions and introduced neutral motions, including timing differences and spatial differences. Timing differences are defined as time alignment curves, while spatial differences are found by a machine learning technique: Independent Feature Subspaces Analysis, which is the combination of Multidimensional Independent Component Analysis and Invariant Feature Subspaces. This technique is used to decompose two motions into several subspaces. One of these subspaces can be defined as style subspace that describes the style aspects of the stylistic motion. In order to find the style subspace, we compare norms of the projections of two motions on each subspace. Once the time alignment curves and style subspaces of several motion clips are obtained, animators can tune, transfer and merge the style subspaces to synthesize new motion clips with various styles. Our method is easy to use since manual manipulations and large training data sets are not necessary.