  We have recently seen tremendous progress in realistic text-to-motion
generation. Yet, the existing methods often fail or produce implausible motions
with unseen text inputs, which limits the applications. In this paper, we
present OMG, a novel framework, which enables compelling motion generation from
zero-shot open-vocabulary text prompts. Our key idea is to carefully tailor the
pretrain-then-finetune paradigm into the text-to-motion generation. At the
pre-training stage, our model improves the generation ability by learning the
rich out-of-domain inherent motion traits. To this end, we scale up a large
unconditional diffusion model up to 1B parameters, so as to utilize the massive
unlabeled motion data up to over 20M motion instances. At the subsequent
fine-tuning stage, we introduce motion ControlNet, which incorporates text
prompts as conditioning information, through a trainable copy of the
pre-trained model and the proposed novel Mixture-of-Controllers (MoC) block.
MoC block adaptively recognizes various ranges of the sub-motions with a
cross-attention mechanism and processes them separately with the
text-token-specific experts. Such a design effectively aligns the CLIP token
embeddings of text prompts to various ranges of compact and expressive motion
features. Extensive experiments demonstrate that our OMG achieves significant
improvements over the state-of-the-art methods on zero-shot text-to-motion
generation. Project page: https://tr3e.github.io/omg-page.

Computer Vision and Pattern Recognition