BAMM：双向自回归运动模型
BAMM: Bidirectional Autoregressive Motion Model
AI总结：
BAMM提出一种双向自回归运动模型，通过运动分词器与混合掩码自注意力Transformer统一掩码建模和自回归建模，在无需预知运动长度的同时实现高质量文本到运动生成与编辑。
摘要：
从文本生成人体运动一直由去噪运动模型主导，无论是通过扩散过程还是生成式掩码过程。然而，这些模型在可用性方面面临很大局限，因为它们需要预先知道运动长度。相反，自回归运动模型通过自适应地预测运动终点来解决这一局限，但代价是生成质量和编辑能力下降。为了解决这些挑战，我们提出了双向自回归运动模型（BAMM），一种新颖的文本到运动生成框架。BAMM由两个关键组件组成：（1）一个运动分词器，将3D人体运动转换为潜在空间中的离散token；（2）一个掩码自注意力Transformer，通过混合注意力掩码策略自回归地预测随机掩码的token。通过统一生成式掩码建模和自回归建模，BAMM捕获运动token之间丰富的双向依赖关系，同时学习从文本输入到运动输出的概率映射，并动态调整运动序列长度。这一特性使BAMM能够同时实现高质量的运动生成、增强的可用性以及内置的运动可编辑性。在HumanML3D和KIT-ML数据集上的大量实验表明，BAMM在定性和定量指标上都超越了当前最先进的方法。我们的项目页面可在https://exitudio.github.io/BAMM-page获取。
英文摘要：
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. Our project page is available at https://exitudio.github.io/BAMM-page
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
