2019/07/11 by M. Pettee, Chase Owen Shimmin, Pettee, Mariel +5 · 2 citations
Computer Science · Engineering · #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Human Motion and Animation #Human Pose and Action Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multimedia (cs.MM) #Music Technology and Sound Studies
paper · pdf · doi:10.48550/arxiv.1907.05297
openalex publication_date 2019/07/11 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
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.