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Beyond Imitation: Generative and Variational Choreography via Machine Learning

Mariel Pettee, Chase Shimmin, Douglas Duhaime, Ilya Vidrin

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: //

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