Recent advances in text-to-motion generation using diffusion and
autoregressive models have shown promising results. However, these models often
suffer from a trade-off between real-time performance, high fidelity, and
motion editability. To address this gap, we introduce MMM, a novel yet simple
motion generation paradigm based on Masked Motion Model. MMM consists of two
key components: (1) a motion tokenizer that transforms 3D human motion into a
sequence of discrete tokens in latent space, and (2) a conditional masked
motion transformer that learns to predict randomly masked motion tokens,
conditioned on the pre-computed text tokens. By attending to motion and text
tokens in all directions, MMM explicitly captures inherent dependency among
motion tokens and semantic mapping between motion and text tokens. During
inference, this allows parallel and iterative decoding of multiple motion
tokens that are highly consistent with fine-grained text descriptions,
therefore simultaneously achieving high-fidelity and high-speed motion
generation. In addition, MMM has innate motion editability. By simply placing
mask tokens in the place that needs editing, MMM automatically fills the gaps
while guaranteeing smooth transitions between editing and non-editing parts.
Extensive experiments on the HumanML3D and KIT-ML datasets demonstrate that MMM
surpasses current leading methods in generating high-quality motion (evidenced
by superior FID scores of 0.08 and 0.429), while offering advanced editing
features such as body-part modification, motion in-betweening, and the
synthesis of long motion sequences. In addition, MMM is two orders of magnitude
faster on a single mid-range GPU than editable motion diffusion models. Our
project page is available at https://exitudio.github.io/MMM-page.

基于掩码动作模型的新颖且简单的 MMM 运动生成范例旨在解决实时性、高保真度和动作可编辑性之间的权衡。通过将 3D 人体动作转换成潜空间中的离散标记序列以及根据预计算的文本标记条件预测随机掩码动作标记的条件掩码动画变换器，MMM 明确捕捉动作标记之间的内在依赖性和动作标记与文本标记之间的语义映射，并实现了与细粒度文本描述高度一致的多个动作标记的并行和迭代解码，从而同时实现了高保真度和高速度的动作生成。此外，MMM 具有固有的动作可编辑性，只需在需要编辑的位置放置掩码标记，即可自动填充空白部分并保证编辑和非编辑部分之间的平滑过渡。对 HumanML3D 和 KIT-ML 数据集进行的大量实验表明 MMM 在生成高质量动画方面超过了当前领先的方法（FID 得分分别为 0.08 和 0.429），同时还提供了诸如身体部位修改、动作插值和长动作序列合成等先进的编辑功能。此外，与可编辑动作扩散模型相比，MMM 在单个中档 GPU 上的速度提高了两个数量级。