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.

MoMask 是一种新颖的掩码建模框架，用于文本驱动的 3D 人体动作生成，通过分层量化方案将人体动作表示为多层离散动作令牌，并使用两个不同的双向转换器进行预测和填补，实验证明在文本到动作生成任务中，MoMask 在 HumanML3D 数据集上的 FID 为 0.045（相较于 T2M-GPT 的 0.141）以及在 KIT-ML 上的 FID 为 0.228（相较于 0.514），同时可以无缝应用于文本引导的时间修补等相关任务。