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- W3032079991 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.this http URL." @default.
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- W3032079991 date "2019-07-11" @default.
- W3032079991 modified "2023-09-28" @default.
- W3032079991 title "Beyond Imitation: Generative and Variational Choreography via Machine Learning" @default.
- W3032079991 hasPublicationYear "2019" @default.
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