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We introduce MoMask, a novel masked modeling framework for text-driven 3Dhuman motion generation. In MoMask, a hierarchical quantization scheme isemployed to represent human motion as multi-layer discrete motion tokens withhigh-fidelity details. Starting at the base layer, with a sequence of motiontokens obtained by vector quantization, the residual tokens of increasingorders are derived and stored at the subsequent layers of the hierarchy. Thisis consequently followed by two distinct bidirectional transformers. For thebase-layer motion tokens, a Masked Transformer is designated to predictrandomly masked motion tokens conditioned on text input at training stage.During generation (i.e. inference) stage, starting from an empty sequence, ourMasked Transformer iteratively fills up the missing tokens; Subsequently, aResidual Transformer learns to progressively predict the next-layer tokensbased on the results from current layer. Extensive experiments demonstrate thatMoMask outperforms the state-of-art methods on the text-to-motion generationtask, 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 seamlesslyapplied in related tasks without further model fine-tuning, such as text-guidedtemporal inpainting.

MoMask: Generative Masked Modeling of 3D Human Motions