  In this paper, we propose a novel cascaded diffusion-based generative
framework for text-driven human motion synthesis, which exploits a strategy
named GradUally Enriching SyntheSis (GUESS as its abbreviation). The strategy
sets up generation objectives by grouping body joints of detailed skeletons in
close semantic proximity together and then replacing each of such joint group
with a single body-part node. Such an operation recursively abstracts a human
pose to coarser and coarser skeletons at multiple granularity levels. With
gradually increasing the abstraction level, human motion becomes more and more
concise and stable, significantly benefiting the cross-modal motion synthesis
task. The whole text-driven human motion synthesis problem is then divided into
multiple abstraction levels and solved with a multi-stage generation framework
with a cascaded latent diffusion model: an initial generator first generates
the coarsest human motion guess from a given text description; then, a series
of successive generators gradually enrich the motion details based on the
textual description and the previous synthesized results. Notably, we further
integrate GUESS with the proposed dynamic multi-condition fusion mechanism to
dynamically balance the cooperative effects of the given textual condition and
synthesized coarse motion prompt in different generation stages. Extensive
experiments on large-scale datasets verify that GUESS outperforms existing
state-of-the-art methods by large margins in terms of accuracy, realisticness,
and diversity. Code is available at https://github.com/Xuehao-Gao/GUESS.
