Invisible Strings: Revealing Latent Dancer-to-Dancer Interactions with Graph Neural Networks
This work addresses a domain-specific problem for dance artists and researchers interested in analyzing and enhancing partnered dance practices, though it is incremental as it applies existing GNN methods to a new application area.
The authors tackled the problem of capturing subtle dancer-to-dancer interactions in duets, which are often missed in traditional documentation, by using Graph Neural Networks to predict and visualize weighted connections between dancers from 3D pose data, demonstrating the method's potential for modeling collaborative dynamics.
Dancing in a duet often requires a heightened attunement to one's partner: their orientation in space, their momentum, and the forces they exert on you. Dance artists who work in partnered settings might have a strong embodied understanding in the moment of how their movements relate to their partner's, but typical documentation of dance fails to capture these varied and subtle relationships. Working closely with dance artists interested in deepening their understanding of partnering, we leverage Graph Neural Networks (GNNs) to highlight and interpret the intricate connections shared by two dancers. Using a video-to-3D-pose extraction pipeline, we extract 3D movements from curated videos of contemporary dance duets, apply a dedicated pre-processing to improve the reconstruction, and train a GNN to predict weighted connections between the dancers. By visualizing and interpreting the predicted relationships between the two movers, we demonstrate the potential for graph-based methods to construct alternate models of the collaborative dynamics of duets. Finally, we offer some example strategies for how to use these insights to inform a generative and co-creative studio practice.