Learning Choreographic Primitives Through A Bayesian Optimized Bi-Directional LSTM Model

Performing arts is an essential aspect of Intangible Cultural Heritage (ICH), requiring tools for its modelling. In this paper, we introduce a Bayesian Optimized Bi-directional LSTM model, called BOBi-LSTM, that automatically estimates dancers' poses through 3D skeleton data processing. Bi-directionality models non-causal relationships occurred in a dance performance, in the sense that future dancer's steps depend on previous/current steps. Additionally, long-range dependence correlates choreographic primitives on a long time (memory) window. To model the aforementioned principles, we modify the conventional LSTM networks under a Bayesian Optimized framework in order to define the best network structure. Experimental results and comparisons for different types of dances are given to showcase how the proposed BOBi-LSTM out-performs traditional classifiers.