Automated choreography synthesis using a Gaussian process leveraging consumer-generated dance motions

We propose a novel method of automatically generating dance choreography using machine learning. In a typical approach to automatic choreography, a dance is constructed by concatenating segments of existing dances which are maximally correlated to the target audio features with connectivity constraints. However, researchers using this approach are unable to produce dances with much variety, since the set of examples used in these experiments (usually motion-capture of existing choreographies) is limited and costly to produce. To solve this issue, we propose a probabilistic model which maps beat structures to dance movements using a Gaussian process, trained with a large amount of consumer-generated dance motion obtained from the web. The main contribution of our work is the combination of two approaches: the previously mentioned correlation based approach which seeks for relationships between music and dance, and a machine learning approach which is based on human motion modeling. Inspection of the generated dances proves that our method can generate choreographies with different characters by switching the training dataset, and highlights opportunities in training with further dance motions on the web to generate more expressive dance choreography.