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。

MMM：生成遮蔽动作模型

MMM: Generative Masked Motion Model

In this paper, we introduce Masked Multi-Step Multivariate Forecasting
(MMMF), a novel and general self-supervised learning framework for time series
forecasting with known future information. In many real-world forecasting
scenarios, some future information is known, e.g., the weather information when
making a short-to-mid-term electricity demand forecast, or the oil price
forecasts when making an airplane departure forecast. Existing machine learning
forecasting frameworks can be categorized into (1) sample-based approaches
where each forecast is made independently, and (2) time series regression
approaches where the future information is not fully incorporated. To overcome
the limitations of existing approaches, we propose MMMF, a framework to train
any neural network model capable of generating a sequence of outputs, that
combines both the temporal information from the past and the known information
about the future to make better predictions. Experiments are performed on two
real-world datasets for (1) mid-term electricity demand forecasting, and (2)
two-month ahead flight departures forecasting. They show that the proposed MMMF
framework outperforms not only sample-based methods but also existing time
series forecasting models with the exact same base models. Furthermore, once a
neural network model is trained with MMMF, its inference speed is similar to
that of the same model trained with traditional regression formulations, thus
making MMMF a better alternative to existing regression-trained time series
forecasting models if there is some available future information.

本文介绍了一种名为 MMM 的新型神经网络的自主学习框架，能够结合过去的时间信息和未来的已知信息进行时间序列预测，实验表明这种方法不仅胜过传统的基于样本或回归的方法，而且训练出的神经网络的推理速度与传统回归训练的时间序列预测模型相当，因此是一种比现有回归训练的时间序列预测模型更好的选择。