Bidirectional long short-term memory networks and sparse hierarchical modeling for scalable educational learning of dance choreographies

Recently, several educational game platforms have been proposed in the literature for choreographic training. However, their main limitation is that they fail to provide a quantitative assessment framework of a performing choreography against a groundtruth one. In this paper, we address this issue by proposing a machine learning framework exploiting deep learning paradigms. In particular, we introduce a long short-term memory network with the main capability of analyzing 3D captured skeleton feature joints of a dancer into predefined choreographic postures. This pose identification procedure is capable of providing a detailed (fine) evaluation score of a performing dance. In addition, the paper proposes a choreographic summarization architecture based on sparse modeling representative selection (SMRS) in order to abstractly represent the performing choreography through a set of key choreographic primitives. We have modified the SMRS algorithm in a way to extract hierarchies of key representatives. Choreographic summarization provides an efficient tool for a coarse quantitative evaluation of a dance. Moreover, hierarchical representation scheme allows for a scalable assessment of a choreography. The serious game platform supports advanced visualization toolkits using Labanotation in order to deliver the performing sequence in a formal documentation.