2016/05/23 by Luka Crnkovic-Friis, Crnkovic-Friis, Luka, Louise Crnkovic-Friis +1 · 4 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Human Motion and Animation #Human Pose and Action Recognition #Machine Learning (cs.LG) #Multimedia (cs.MM) #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.1605.06921
openalex publication_date 2016/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.