Related papers: Invisible Strings: Revealing Latent Dancer-to-Dancer Interactions with Graph Neural Networks

Invisible Strings: Revealing Latent Dancer-to-Dancer Interactions with Graph Neural Networks

URL: http://arxiv.org/abs/2503.04816v1
Date: Tue, 04 Mar 2025 20:08:31 GMT
Title: Invisible Strings: Revealing Latent Dancer-to-Dancer Interactions with Graph Neural Networks
Authors: Luis Vitor Zerkowski, Zixuan Wang, Ilya Vidrin, Mariel Pettee,
Abstract summary: We use Graph Neural Networks to highlight and interpret the intricate connections shared by two dancers.<n>We demonstrate the potential for graph-based methods to construct alternate models of the collaborative dynamics of duets.
Score: 6.67162793750123
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: 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.

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