用图神经网络揭示双人舞者间的隐性互动关系。
Invisible Strings: Revealing Latent Dancer-to-Dancer Interactions with Graph Neural Networks
- 用图神经网络建模双人舞者的空间与动量关联。
- 通过3D姿态追踪提取数据，预测舞者间动态连接权重。
- 为编舞创作提供可解释的协作动力学新视角。
双人舞需要高度感知搭档的空间位置、运动趋势及相互作用力。尽管舞者在实践中具备即时的身体认知，但传统舞蹈记录难以捕捉这些微妙的关系。本文与舞者合作，利用图神经网络（GNN）分析当代双人舞视频，通过视频到3D姿态的提取流程获取运动数据，并进行专项预处理以提升重建质量。训练后的GNN能预测两名舞者之间的加权连接关系。通过可视化和解读这些预测关系，展示了基于图的方法在构建双人协作动力学模型方面的潜力。最后，提出若干策略，将这些洞察应用于生成式与共创型创作实践。
原文摘要 · Abstract (English)
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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