  Recent advances in deep learning have enabled the extraction of high-level
features from raw sensor data which has opened up new possibilities in many
different fields, including computer generated choreography. In this paper we
present a system chor-rnn for generating novel choreographic material in the
nuanced choreographic language and style of an individual choreographer. It
also shows promising results in producing a higher level compositional
cohesion, rather than just generating sequences of movement. At the core of
chor-rnn is a deep recurrent neural network trained on raw motion capture data
and that can generate new dance sequences for a solo dancer. Chor-rnn can be
used for collaborative human-machine choreography or as a creative catalyst,
serving as inspiration for a choreographer.
