  Text-to-motion generation is a formidable task, aiming to produce human
motions that align with the input text while also adhering to human
capabilities and physical laws. While there have been advancements in diffusion
models, their application in discrete spaces remains underexplored. Current
methods often overlook the varying significance of different motions, treating
them uniformly. It is essential to recognize that not all motions hold the same
relevance to a particular textual description. Some motions, being more salient
and informative, should be given precedence during generation. In response, we
introduce a Priority-Centric Motion Discrete Diffusion Model (M2DM), which
utilizes a Transformer-based VQ-VAE to derive a concise, discrete motion
representation, incorporating a global self-attention mechanism and a
regularization term to counteract code collapse. We also present a motion
discrete diffusion model that employs an innovative noise schedule, determined
by the significance of each motion token within the entire motion sequence.
This approach retains the most salient motions during the reverse diffusion
process, leading to more semantically rich and varied motions. Additionally, we
formulate two strategies to gauge the importance of motion tokens, drawing from
both textual and visual indicators. Comprehensive experiments on the HumanML3D
and KIT-ML datasets confirm that our model surpasses existing techniques in
fidelity and diversity, particularly for intricate textual descriptions.
