  Text2Motion aims to generate human motions from texts. Existing datasets rely
on the assumption that texts include action labels (such as "walk, bend, and
pick up"), which is not flexible for practical scenarios. This paper redefines
this problem with a more realistic assumption that the texts are arbitrary.
Specifically, arbitrary texts include existing action texts composed of action
labels (e.g., A person walks and bends to pick up something), and introduce
scene texts without explicit action labels (e.g., A person notices his wallet
on the ground ahead).
  To bridge the gaps between this realistic setting and existing datasets, we
expand the action texts on the HumanML3D dataset to more scene texts, thereby
creating a new HumanML3D++ dataset including arbitrary texts. In this
challenging dataset, we benchmark existing state-of-the-art methods and propose
a novel two-stage framework to extract action labels from arbitrary texts by
the Large Language Model (LLM) and then generate motions from action labels.
Extensive experiments are conducted under different application scenarios to
validate the effectiveness of the proposed framework on existing and proposed
datasets. The results indicate that Text2Motion in this realistic setting is
very challenging, fostering new research in this practical direction. Our
dataset and code will be released.
