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.
翻译:人类自然运动互动的产生是计算机视觉和计算机动画中的一个热题。由于人类运动互动可能的多样性,这是一项具有挑战性的任务。 在其他领域已经表现出非凡的基因化能力的传播模型是完成这项任务的好选择。在本文中,我们引入了一种新型的双方图形扩散方法(BiGraphDiff),以在两个人之间产生人类运动互动。具体地说,双方节点的构造可以模拟互动期间骨干节点之间固有的几何限制。互动图传播模型以变压器为基础,结合一些最先进的运动方法。我们显示,拟议的模型在人类互动生成任务的主要基准上取得了新的最新成果。