Ballroom dancing is a structured yet expressive motion category. Its highly diverse movement and complex interactions between leader and follower dancers make the understanding and synthesis challenging. We demonstrate that the three-point trajectory available from a virtual reality (VR) device can effectively serve as a dancer's motion descriptor, simplifying the modeling and synthesis of interplay between dancers' full-body motions down to sparse trajectories. Thanks to the low dimensionality, we can employ an efficient MLP network to predict the follower's three-point trajectory directly from the leader's three-point input for certain types of ballroom dancing, addressing the challenge of modeling high-dimensional full-body interaction. It also prevents our method from overfitting thanks to its compact yet explicit representation. By leveraging the inherent structure of the movements and carefully planning the autoregressive procedure, we show a deterministic neural network is able to translate three-point trajectories into a virtual embodied avatar, which is typically considered under-constrained and requires generative models for common motions. In addition, we demonstrate this deterministic approach generalizes beyond small, structured datasets like ballroom dancing, and performs robustly on larger, more diverse datasets such as LaFAN. Our method provides a computationally- and data-efficient solution, opening new possibilities for immersive paired dancing applications. Code and pre-trained models for this paper are available at https://peizhuoli.github.io/dancing-points.

翻译：舞厅舞蹈是一种结构严谨且富有表现力的运动类别。其高度多样化的动作以及领舞者与伴舞者之间复杂的交互作用，使得对其理解与合成颇具挑战性。我们证明，虚拟现实（VR）设备可获取的三点轨迹能够有效作为舞者的运动描述符，将舞者全身运动间相互作用的建模与合成简化为稀疏轨迹的处理。得益于其低维度特性，我们能够采用高效的多层感知机（MLP）网络，针对特定类型的舞厅舞蹈，直接从领舞者的三点输入预测伴舞者的三点轨迹，从而解决了高维全身交互建模的难题。该方法凭借其紧凑而明确的表征，亦有效避免了过拟合问题。通过利用运动固有的结构特性并精心设计自回归流程，我们证明确定性神经网络能够将三点轨迹转换为虚拟具身化形象——这一任务在通常运动合成中常被视为约束不足而需依赖生成模型。此外，我们验证了这种确定性方法能够超越舞厅舞蹈等小型结构化数据集，在LaFAN等规模更大、多样性更强的数据集上依然保持稳健性能。本方法提供了计算与数据高效并重的解决方案，为沉浸式配对舞蹈应用开辟了新的可能性。本文相关代码与预训练模型已发布于 https://peizhuoli.github.io/dancing-points。