We introduce Efficient Motion Diffusion Model (EMDM) for fast and
high-quality human motion generation. Although previous motion diffusion models
have shown impressive results, they struggle to achieve fast generation while
maintaining high-quality human motions. Motion latent diffusion has been
proposed for efficient motion generation. However, effectively learning a
latent space can be non-trivial in such a two-stage manner. Meanwhile,
accelerating motion sampling by increasing the step size, e.g., DDIM, typically
leads to a decline in motion quality due to the inapproximation of complex data
distributions when naively increasing the step size. In this paper, we propose
EMDM that allows for much fewer sample steps for fast motion generation by
modeling the complex denoising distribution during multiple sampling steps.
Specifically, we develop a Conditional Denoising Diffusion GAN to capture
multimodal data distributions conditioned on both control signals, i.e.,
textual description and denoising time step. By modeling the complex data
distribution, a larger sampling step size and fewer steps are achieved during
motion synthesis, significantly accelerating the generation process. To
effectively capture the human dynamics and reduce undesired artifacts, we
employ motion geometric loss during network training, which improves the motion
quality and training efficiency. As a result, EMDM achieves a remarkable
speed-up at the generation stage while maintaining high-quality motion
generation in terms of fidelity and diversity.

引入高效动作扩散模型（EMDM）用于快速高质量的人体动作生成，通过建模复杂的去噪分布在多个采样步骤中实现更大的采样步长和较少的步骤，加速生成过程并保持高质量的运动生成。