  Our team of dance artists, physicists, and machine learning researchers has
collectively developed several original, configurable machine-learning tools to
generate novel sequences of choreography as well as tunable variations on input
choreographic sequences. We use recurrent neural network and autoencoder
architectures from a training dataset of movements captured as 53
three-dimensional points at each timestep. Sample animations of generated
sequences and an interactive version of our model can be found at http:
//www.beyondimitation.com.
