Generating human motion from text has been dominated by denoising motion
models either through diffusion or generative masking process. However, these
models face great limitations in usability by requiring prior knowledge of the
motion length. Conversely, autoregressive motion models address this limitation
by adaptively predicting motion endpoints, at the cost of degraded generation
quality and editing capabilities. To address these challenges, we propose
Bidirectional Autoregressive Motion Model (BAMM), a novel text-to-motion
generation framework. BAMM consists of two key components: (1) a motion
tokenizer that transforms 3D human motion into discrete tokens in latent space,
and (2) a masked self-attention transformer that autoregressively predicts
randomly masked tokens via a hybrid attention masking strategy. By unifying
generative masked modeling and autoregressive modeling, BAMM captures rich and
bidirectional dependencies among motion tokens, while learning the
probabilistic mapping from textual inputs to motion outputs with
dynamically-adjusted motion sequence length. This feature enables BAMM to
simultaneously achieving high-quality motion generation with enhanced usability
and built-in motion editability. Extensive experiments on HumanML3D and KIT-ML
datasets demonstrate that BAMM surpasses current state-of-the-art methods in
both qualitative and quantitative measures.

提出了一种新的文本到动作生成框架：Bidirectional Autoregressive Motion Model (BAMM)，它由两个关键组件组成：将 3D 人体动作转换为潜在空间中的离散标记的动作分词器和通过混合注意掩蔽策略自回归预测随机屏蔽标记的掩蔽自注意力变换器。通过统一生成掩蔽建模和自回归建模，BAMM 捕捉了动作标记之间丰富而双向的依赖关系，同时学习了从文本输入到动作输出的概率映射，并具有动态调整的动作序列长度。大量在 HumanML3D 和 KIT-ML 数据集上的实验证明，BAMM 在定性和定量指标上均超过了当前最先进的方法。

BAMM: 双向自回归运动模型

BAMM: Bidirectional Autoregressive Motion Model

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。