  Interactive motion synthesis is essential in creating immersive experiences
in entertainment applications, such as video games and virtual reality.
However, generating animations that are both high-quality and contextually
responsive remains a challenge. Traditional techniques in the game industry can
produce high-fidelity animations but suffer from high computational costs and
poor scalability. Trained neural network models alleviate the memory and speed
issues, yet fall short on generating diverse motions. Diffusion models offer
diverse motion synthesis with low memory usage, but require expensive reverse
diffusion processes. This paper introduces the Accelerated Auto-regressive
Motion Diffusion Model (AAMDM), a novel motion synthesis framework designed to
achieve quality, diversity, and efficiency all together. AAMDM integrates
Denoising Diffusion GANs as a fast Generation Module, and an Auto-regressive
Diffusion Model as a Polishing Module. Furthermore, AAMDM operates in a
lower-dimensional embedded space rather than the full-dimensional pose space,
which reduces the training complexity as well as further improves the
performance. We show that AAMDM outperforms existing methods in motion quality,
diversity, and runtime efficiency, through comprehensive quantitative analyses
and visual comparisons. We also demonstrate the effectiveness of each
algorithmic component through ablation studies.
