DiverseMotion: Towards Diverse Human Motion Generation via Discrete
  Diffusion
Yunhong Lou Linchao Zhu Yaxiong Wang Xiaohan Wang Yi Yang
We present DiverseMotion, a new approach for synthesizing high-quality human
motions conditioned on textual descriptions while preserving motion
diversity.Despite the recent significant process in text-based human motion
generation,existing methods often prioritize fitting training motions at the
expense of action diversity. Consequently, striking a balance between motion
quality and diversity remains an unresolved challenge. This problem is
compounded by two key factors: 1) the lack of diversity in motion-caption pairs
in existing benchmarks and 2) the unilateral and biased semantic understanding
of the text prompt, focusing primarily on the verb component while neglecting
the nuanced distinctions indicated by other words.In response to the first
issue, we construct a large-scale Wild Motion-Caption dataset (WMC) to extend
the restricted action boundary of existing well-annotated datasets, enabling
the learning of diverse motions through a more extensive range of actions. To
this end, a motion BLIP is trained upon a pretrained vision-language model,
then we automatically generate diverse motion captions for the collected motion
sequences. As a result, we finally build a dataset comprising 8,888 motions
coupled with 141k text.To comprehensively understand the text command, we
propose a Hierarchical Semantic Aggregation (HSA) module to capture the
fine-grained semantics.Finally,we involve the above two designs into an
effective Motion Discrete Diffusion (MDD) framework to strike a balance between
motion quality and diversity. Extensive experiments on HumanML3D and KIT-ML
show that our DiverseMotion achieves the state-of-the-art motion quality and
competitive motion diversity. Dataset, code, and pretrained models will be
released to reproduce all of our results.