Current approaches for 3D human motion synthesis can generate high-quality 3D animations of digital humans performing a wide variety of actions and gestures. However, there is still a notable technological gap in addressing the complex dynamics of multi-human interactions within this paradigm. In this work, we introduce ReMoS, a denoising diffusion-based probabilistic model for reactive motion synthesis that explores two-person interactions. Given the motion of one person, we synthesize the reactive motion of the second person to complete the interactions between the two. In addition to synthesizing the full-body motions, we also synthesize plausible hand interactions. We show the performance of ReMoS under a wide range of challenging two-person scenarios including pair-dancing, Ninjutsu, kickboxing, and acrobatics, where one person's movements have complex and diverse influences on the motions of the other. We further propose the ReMoCap dataset for two-person interactions consisting of full-body and hand motions. We evaluate our approach through multiple quantitative metrics, qualitative visualizations, and a user study. Our results are usable in interactive applications while also providing an adequate amount of control for animators.

翻译：当前3D人体运动合成方法能够生成执行各种动作与手势的数字人的高质量3D动画。然而，在该范式下处理多人交互的复杂动力学仍存在显著的技术空白。本文提出ReMoS——一种基于去噪扩散概率模型的响应式运动合成方法，专注于探索双人交互。给定其中一人的运动数据，我们合成第二人的响应式运动以完成两者间的交互。除全身运动合成外，我们同时生成合理的手部交互动作。我们展示了ReMoS在包括双人舞蹈、忍术、踢拳及杂技等具有挑战性的双人场景中的性能表现，其中一人的动作对另一人的运动产生复杂而多样的影响。进一步，我们提出了包含全身与手部动作的ReMoCap双人交互数据集。通过多项定量指标、定性可视化及用户研究评估我们的方法。实验结果不仅可为交互式应用所采用，同时为动画师提供了充分的控制能力。