Beyond Imitation: Generative and Variational Choreography via Machine Learning

07/11/2019
by   Mariel Pettee, et al.
4

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset