  The field has made significant progress in synthesizing realistic human
motion driven by various modalities. Yet, the need for different methods to
animate various body parts according to different control signals limits the
scalability of these techniques in practical scenarios. In this paper, we
introduce a cohesive and scalable approach that consolidates multimodal (text,
music, speech) and multi-part (hand, torso) human motion generation. Our
methodology unfolds in several steps: We begin by quantizing the motions of
diverse body parts into separate codebooks tailored to their respective
domains. Next, we harness the robust capabilities of pre-trained models to
transcode multimodal signals into a shared latent space. We then translate
these signals into discrete motion tokens by iteratively predicting subsequent
tokens to form a complete sequence. Finally, we reconstruct the continuous
actual motion from this tokenized sequence. Our method frames the multimodal
motion generation challenge as a token prediction task, drawing from
specialized codebooks based on the modality of the control signal. This
approach is inherently scalable, allowing for the easy integration of new
modalities. Extensive experiments demonstrated the effectiveness of our design,
emphasizing its potential for broad application.

Computer Vision and Pattern Recognition