用扩散模型生成逼真足球动作，支持实时用户控制
SMGDiff: Soccer Motion Generation using diffusion probabilistic models
- 分两阶段生成：先定轨迹，再用扩散模型细化动作
- 在108万帧数据上训练，生成动作更真实多样
- 适合游戏与VR/AR中的角色动画实时生成
足球是一项全球知名的运动，在视频游戏和虚拟现实/增强现实中有广泛应用。然而，由于球员与球之间复杂的交互关系，生成逼真足球动作仍具挑战性。本文提出SMGDiff，一种新型两阶段框架，用于实时、可用户控制的足球动作生成。核心思想是将实时角色控制与强大的基于扩散的概率模型结合，确保输出动作质量高且多样化。第一阶段将粗略的用户控制即时转化为角色的多样化全局轨迹；第二阶段采用基于Transformer的自回归扩散模型，根据轨迹条件生成足球动作。推理过程中还引入接触引导模块，优化球与脚的交互细节以实现更真实的触球效果。此外，我们构建了一个包含超过108万帧多样化足球动作的大规模数据集。大量实验表明，SMGDiff在动作质量和条件对齐方面显著优于现有方法。
原文摘要 · Abstract (English)
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 human 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 control with a powerful diffusion-based generative model, ensuring high-quality and diverse output motion. In the first stage, we instantly transform coarse user controls into diverse global trajectories of the character. In the second stage, we employ a transformer-based autoregressive diffusion model to generate soccer motions based on trajectory conditioning. We further incorporate a contact guidance module during inference to optimize the contact details for realistic ball-foot interactions. Moreover, 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.
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