Bidirectional long short-term memory networks and sparse hierarchical modeling for scalable educational learning of dance choreographies - Rallis, I.; Bakalos, N.; Doulamis, N.; Doulamis, A.; Voulodimos, A. (The Visual Computer, 2021)
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 aground truth 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 capableof 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.