  Text-to-motion generation is an emerging and challenging problem, which aims
to synthesize motion with the same semantics as the input text. However, due to
the lack of diverse labeled training data, most approaches either limit to
specific types of text annotations or require online optimizations to cater to
the texts during inference at the cost of efficiency and stability. In this
paper, we investigate offline open-vocabulary text-to-motion generation in a
zero-shot learning manner that neither requires paired training data nor extra
online optimization to adapt for unseen texts. Inspired by the prompt learning
in NLP, we pretrain a motion generator that learns to reconstruct the full
motion from the masked motion. During inference, instead of changing the motion
generator, our method reformulates the input text into a masked motion as the
prompt for the motion generator to ``reconstruct'' the motion. In constructing
the prompt, the unmasked poses of the prompt are synthesized by a text-to-pose
generator. To supervise the optimization of the text-to-pose generator, we
propose the first text-pose alignment model for measuring the alignment between
texts and 3D poses. And to prevent the pose generator from overfitting to
limited training texts, we further propose a novel wordless training mechanism
that optimizes the text-to-pose generator without any training texts. The
comprehensive experimental results show that our method obtains a significant
improvement against the baseline methods. The code is available at
https://github.com/junfanlin/oohmg.
