  Text-guided motion synthesis aims to generate 3D human motion that not only
precisely reflects the textual description but reveals the motion details as
much as possible. Pioneering methods explore the diffusion model for
text-to-motion synthesis and obtain significant superiority. However, these
methods conduct diffusion processes either on the raw data distribution or the
low-dimensional latent space, which typically suffer from the problem of
modality inconsistency or detail-scarce. To tackle this problem, we propose a
novel Basic-to-Advanced Hierarchical Diffusion Model, named B2A-HDM, to
collaboratively exploit low-dimensional and high-dimensional diffusion models
for high quality detailed motion synthesis. Specifically, the basic diffusion
model in low-dimensional latent space provides the intermediate denoising
result that to be consistent with the textual description, while the advanced
diffusion model in high-dimensional latent space focuses on the following
detail-enhancing denoising process. Besides, we introduce a multi-denoiser
framework for the advanced diffusion model to ease the learning of
high-dimensional model and fully explore the generative potential of the
diffusion model. Quantitative and qualitative experiment results on two
text-to-motion benchmarks (HumanML3D and KIT-ML) demonstrate that B2A-HDM can
outperform existing state-of-the-art methods in terms of fidelity, modality
consistency, and diversity.
