SMGDiff: Soccer Motion Generation using Diffusion Probabilistic Models

Soccer is a globally renowned sport with significant applications in video games and VR/AR. However, generating realistic soccer motions remains challenging due to the intricate interactions between the player and the ball. In this paper, we introduce SMGDiff, a novel two-stage framework for generating real-time and user-controllable soccer motions. Our key idea is to integrate real-time character animation with a powerful diffusion-based generative model. Specifically, we first map coarse user control to intricate character trajectories. Then, we employ a transformer-based autoregressive diffusion model to generate soccer motions based on trajectory conditioning. For further physical realism, we integrate a contact guidance module during inference to refine precise ball-foot interactions.Additionally, we contribute a large-scale soccer motion dataset consisting of over 1.08 million frames of diverse soccer motions. Extensive experiments demonstrate that our SMGDiff significantly outperforms existing methods in terms of motion quality and condition alignment.