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.

文本到动作生成是一项艰巨的任务，本文介绍一种优先级中心的运动分散扩散模型（M2DM），利用基于 Transformer 的 VQ-VAE 得出简洁的、离散的动作表示，通过全局自注意机制和正则化项来抵消代码坍塌。我们还提出了一种运动离散扩散模型，它采用了一种创新的噪声调度方式，根据整个运动序列中每个动作标记的重要性来确定。该方法在逆扩散过程中保留了最显著的动作，从而产生更丰富多样的语义动作。在 HumanML3D 和 KIT-ML 数据集上进行的综合实验证实了我们的模型在保真度和多样性方面超过了现有技术，尤其对于复杂的文本描述。