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:
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通过对网络架构、训练策略和推理过程的深入分析，我们根据研究结果为高质量的人体运动生成定制了每个组件，最终提出了稳定的人体运动生成框架 StableMoFusion，并通过大量实验证明其在与当前最先进方法的比较中表现出色。