We present a novel character control framework that effectively utilizes motion diffusion probabilistic models to generate high-quality and diverse character animations, responding in real-time to a variety of dynamic user-supplied control signals. At the heart of our method lies a transformer-based Conditional Autoregressive Motion Diffusion Model (CAMDM), which takes as input the character's historical motion and can generate a range of diverse potential future motions conditioned on high-level, coarse user control. To meet the demands for diversity, controllability, and computational efficiency required by a real-time controller, we incorporate several key algorithmic designs. These include separate condition tokenization, classifier-free guidance on past motion, and heuristic future trajectory extension, all designed to address the challenges associated with taming motion diffusion probabilistic models for character control. As a result, our work represents the first model that enables real-time generation of high-quality, diverse character animations based on user interactive control, supporting animating the character in multiple styles with a single unified model. We evaluate our method on a diverse set of locomotion skills, demonstrating the merits of our method over existing character controllers. Project page and source codes: https://aiganimation.github.io/CAMDM/

翻译：我们提出了一种新颖的角色控制框架，该框架有效利用运动扩散概率模型生成高质量且多样化的角色动画，并能够实时响应多种动态用户提供的控制信号。本方法的核心是基于Transformer的条件自回归运动扩散模型（CAMDM），该模型以角色的历史运动为输入，能够基于高级粗粒度的用户控制生成一系列多样化的潜在未来运动。为满足实时控制器对多样性、可控性和计算效率的需求，我们融入了多项关键算法设计，包括条件独立分词化、基于历史运动的无分类器引导以及启发式未来轨迹扩展。这些设计旨在应对将运动扩散概率模型应用于角色控制时的挑战。因此，我们的工作是首个实现基于用户交互控制实时生成高质量、多样化角色动画的模型，并支持通过单一统一模型以多种风格驱动角色动画。我们在多种运动技能场景下评估了该方法，证明了其相较于现有角色控制器的优势。项目页面及源代码：https://aiganimation.github.io/CAMDM/