The generation of natural human motion interactions is a hot topic in
computer vision and computer animation. It is a challenging task due to the
diversity of possible human motion interactions. Diffusion models, which have
already shown remarkable generative capabilities in other domains, are a good
candidate for this task. In this paper, we introduce a novel bipartite graph
diffusion method (BiGraphDiff) to generate human motion interactions between
two persons. Specifically, bipartite node sets are constructed to model the
inherent geometric constraints between skeleton nodes during interactions. The
interaction graph diffusion model is transformer-based, combining some
state-of-the-art motion methods. We show that the proposed achieves new
state-of-the-art results on leading benchmarks for the human interaction
generation task.

本文介绍了一种新的双分图扩散方法（BiGraphDiff），用于生成两个人之间的自然人体动作交互，通过模拟骨架节点之间的几何约束，利用基于 Transformer 的交互图扩散模型与先进的动作方法相结合。结果表明，该方法在人体交互生成任务的主要基准测试上实现了最新的最优结果。