Dyads: Artist-Centric, AI-Generated Dance Duets
This addresses the need for more artist-centric and interactive dance generation in the AI and dance communities, though it is incremental in combining existing techniques with co-creation.
The paper tackles the lack of AI-generated dance methods that model interactions between dancers and incorporate artist input, resulting in a model that generates choreographic partners for input sequences with enhanced smoothness and coherence.
Existing AI-generated dance methods primarily train on motion capture data from solo dance performances, but a critical feature of dance in nearly any genre is the interaction of two or more bodies in space. Moreover, many works at the intersection of AI and dance fail to incorporate the ideas and needs of the artists themselves into their development process, yielding models that produce far more useful insights for the AI community than for the dance community. This work addresses both needs of the field by proposing an AI method to model the complex interactions between pairs of dancers and detailing how the technical methodology can be shaped by ongoing co-creation with the artistic stakeholders who curated the movement data. Our model is a probability-and-attention-based Variational Autoencoder that generates a choreographic partner conditioned on an input dance sequence. We construct a custom loss function to enhance the smoothness and coherence of the generated choreography. Our code is open-source, and we also document strategies for other interdisciplinary research teams to facilitate collaboration and strong communication between artists and technologists.