Bipartite Graph Diffusion Model for Human Interaction Generation
This addresses the challenge of diverse human motion interactions for applications in computer vision and animation, representing an incremental improvement over existing methods.
The paper tackles the problem of generating natural human motion interactions between two persons by introducing a bipartite graph diffusion method (BiGraphDiff) that models geometric constraints between skeleton nodes, achieving new state-of-the-art results on leading benchmarks.
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.