In this paper, we present a novel method for editing stylistic human motions. We represent styles as differences between stylistic 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.

To generate human motions with various specific attributes is a difficult task because of high dimensionality and complexity of human motions. This paper presents a novel human motion model for generating and editing motions with multiple factors. A set of motions performed by several actors with various styles was captured for constructing a well-structured motion database. Subsequently, MICA (multilinear independent component analysis) model that combines ICA and conventional multilinear framework was adopted for the construction of a multifactor model. With this model, new motions can be synthesized by interpolation and through solving optimization problems for the specific factors. Our method offers a practical solution to edit stylistic human motions in a parametric space learnt with MICA model. We demonstrated the power of our method by generating and editing sideways stepping, reaching, and striding over obstructions using different actors with various styles. The experimental results show that our method can be used for interactive stylistic motion synthesis and editing.

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