  We introduce MoMask, a novel masked modeling framework for text-driven 3D
human motion generation. In MoMask, a hierarchical quantization scheme is
employed to represent human motion as multi-layer discrete motion tokens with
high-fidelity details. Starting at the base layer, with a sequence of motion
tokens obtained by vector quantization, the residual tokens of increasing
orders are derived and stored at the subsequent layers of the hierarchy. This
is consequently followed by two distinct bidirectional transformers. For the
base-layer motion tokens, a Masked Transformer is designated to predict
randomly masked motion tokens conditioned on text input at training stage.
During generation (i.e. inference) stage, starting from an empty sequence, our
Masked Transformer iteratively fills up the missing tokens; Subsequently, a
Residual Transformer learns to progressively predict the next-layer tokens
based on the results from current layer. Extensive experiments demonstrate that
MoMask outperforms the state-of-art methods on the text-to-motion generation
task, with an FID of 0.045 (vs e.g. 0.141 of T2M-GPT) on the HumanML3D dataset,
and 0.228 (vs 0.514) on KIT-ML, respectively. MoMask can also be seamlessly
applied in related tasks without further model fine-tuning, such as text-guided
temporal inpainting.
