Abstract
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: this https URL
Abstract (translated)
我们提出了一个新颖的角色控制框架，它有效地利用运动扩散概率模型来生成高质量和多样化的角色动画，能够实时响应各种动态用户提供的控制信号。我们方法的核心是基于Transformer的条件自回归运动扩散模型（CAMDM），它接收角色的历史运动，并可以根据高级、粗略的用户控制生成一系列可能的未来运动。为了满足实时控制器对多样性、可控性和计算效率的需求，我们包括几个关键的算法设计。这些设计包括分开的条件标记、无分类指导过去运动和启发式未来轨迹扩展，所有这些设计都是为了应对驯化运动扩散概率模型的挑战而设计的。因此，我们的工作代表了第一个基于用户交互控制生成高质量、多样化角色动画的模型，支持使用单一模型动画化角色多种风格。我们在多样化的运动技能集上评估我们的方法，证明了我们的方法在现有角色控制器上的优越性。项目页面和源代码：https:// this URL
URL
https://arxiv.org/abs/2404.15121
