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Text-guided motion synthesis aims to generate 3D human motion that not onlyprecisely reflects the textual description but reveals the motion details asmuch as possible. Pioneering methods explore the diffusion model fortext-to-motion synthesis and obtain significant superiority. However, thesemethods conduct diffusion processes either on the raw data distribution or thelow-dimensional latent space, which typically suffer from the problem ofmodality inconsistency or detail-scarce. To tackle this problem, we propose anovel Basic-to-Advanced Hierarchical Diffusion Model, named B2A-HDM, tocollaboratively exploit low-dimensional and high-dimensional diffusion modelsfor high quality detailed motion synthesis. Specifically, the basic diffusionmodel in low-dimensional latent space provides the intermediate denoisingresult that to be consistent with the textual description, while the advanceddiffusion model in high-dimensional latent space focuses on the followingdetail-enhancing denoising process. Besides, we introduce a multi-denoiserframework for the advanced diffusion model to ease the learning ofhigh-dimensional model and fully explore the generative potential of thediffusion model. Quantitative and qualitative experiment results on twotext-to-motion benchmarks (HumanML3D and KIT-ML) demonstrate that B2A-HDM canoutperform existing state-of-the-art methods in terms of fidelity, modalityconsistency, and diversity.

Towards Detailed Text-to-Motion Synthesis via Basic-to-Advanced Hierarchical Diffusion Model