  Real-time character control is an essential component for interactive
experiences, with a broad range of applications, including physics simulations,
video games, and virtual reality. The success of diffusion models for image
synthesis has led to the use of these models for motion synthesis. However, the
majority of these motion diffusion models are primarily designed for offline
applications, where space-time models are used to synthesize an entire sequence
of frames simultaneously with a pre-specified length. To enable real-time
motion synthesis with diffusion model that allows time-varying controls, we
propose A-MDM (Auto-regressive Motion Diffusion Model). Our conditional
diffusion model takes an initial pose as input, and auto-regressively generates
successive motion frames conditioned on the previous frame. Despite its
streamlined network architecture, which uses simple MLPs, our framework is
capable of generating diverse, long-horizon, and high-fidelity motion
sequences. Furthermore, we introduce a suite of techniques for incorporating
interactive controls into A-MDM, such as task-oriented sampling, in-painting,
and hierarchical reinforcement learning. These techniques enable a pre-trained
A-MDM to be efficiently adapted for a variety of new downstream tasks. We
conduct a comprehensive suite of experiments to demonstrate the effectiveness
of A-MDM, and compare its performance against state-of-the-art auto-regressive
methods.
