  Thanks to the powerful generative capacity of diffusion models, recent years
have witnessed rapid progress in human motion generation. Existing
diffusion-based methods employ disparate network architectures and training
strategies. The effect of the design of each component is still unclear. In
addition, the iterative denoising process consumes considerable computational
overhead, which is prohibitive for real-time scenarios such as virtual
characters and humanoid robots. For this reason, we first conduct a
comprehensive investigation into network architectures, training strategies,
and inference processs. Based on the profound analysis, we tailor each
component for efficient high-quality human motion generation. Despite the
promising performance, the tailored model still suffers from foot skating which
is an ubiquitous issue in diffusion-based solutions. To eliminate footskate, we
identify foot-ground contact and correct foot motions along the denoising
process. By organically combining these well-designed components together, we
present StableMoFusion, a robust and efficient framework for human motion
generation. Extensive experimental results show that our StableMoFusion
performs favorably against current state-of-the-art methods. Project page:
https://h-y1heng.github.io/StableMoFusion-page/
