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。