Generative Choreograpy

Generative Choreography using Deep Learning, prepreprint article for ICCC16.

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. We have in collaboration with The Lulu Art group developed  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 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. 

Process

Results

After 10 minutes of training , movements are more or less random.

After 6 hours of training, the RNN knows how the joints are related and it makes its first, careful and somewhat wobbly attempts at dancing:

After 48 hours it has become an accomplished dancer, making up the choreography as it goes:

Deep Dreaming

An artistic-scientific experiment combining choreography and artifical intelligence. A deep neural network interprets the images and movements and shows what it thinks it sees. The choreographer in turn interprets the result and develops the piece and the process is repeated.

This is the first in line of a number of experiments exploring an artistic collaboration between a human and a computer. Deep neural networks are an artificial intelligence technology that is inspired by the workings of the human brain but implemented in software. 

Choreography & performance: Louise Crnkovic-Friis
Music: Etude E Minor, Francisco Tarrega