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arXiv · 1907.05297

Beyond Imitation: Generative and Variational Choreography via Machine Learning

Mariel Pettee·Chase Shimmin·Douglas Duhaime·Ilya Vidrin
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Abstract

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.

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Mariel Pettee, Chase Shimmin, Douglas Duhaime, Ilya Vidrin. 2019-07-11. Beyond Imitation: Generative and Variational Choreography via Machine Learning. https://arxiv.org/abs/1907.05297

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