Bipartite Graph Diffusion Model for Human Interaction Generation

• URL: http://arxiv.org/abs/2301.10134v2
• Date: Fri, 3 Nov 2023 21:42:54 GMT
• Title: Bipartite Graph Diffusion Model for Human Interaction Generation
• Authors: Baptiste Chopin, Hao Tang, Mohamed Daoudi
• Abstract summary: We introduce a novel bipartite graph diffusion method (BiGraphDiff) to generate human motion interactions between two persons. We show that the proposed achieves new state-of-the-art results on leading benchmarks for the human interaction generation task.
• Score: 11.732108478773196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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