  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/
