Motion generation from discrete quantization offers many advantages over continuous regression, but at the cost of inevitable approximation errors. Previous methods usually quantize the entire body pose into one code, which not only faces the difficulty in encoding all joints within one vector but also loses the spatial relationship between different joints. Differently, in this work we quantize each individual joint into one vector, which i) simplifies the quantization process as the complexity associated with a single joint is markedly lower than that of the entire pose; ii) maintains a spatial-temporal structure that preserves both the spatial relationships among joints and the temporal movement patterns; iii) yields a 2D token map, which enables the application of various 2D operations widely used in 2D images. Grounded in the 2D motion quantization, we build a spatial-temporal modeling framework, where 2D joint VQVAE, temporal-spatial 2D masking technique, and spatial-temporal 2D attention are proposed to take advantage of spatial-temporal signals among the 2D tokens. Extensive experiments demonstrate that our method significantly outperforms previous methods across different datasets, with a $26.6\%$ decrease of FID on HumanML3D and a $29.9\%$ decrease on KIT-ML.

翻译：基于离散量化的运动生成相较于连续回归方法具有诸多优势，但代价是不可避免的近似误差。先前的方法通常将整个身体姿态量化至一个编码中，这不仅面临将所有关节编码至单一向量的困难，还会丢失不同关节间的空间关系。与之不同，本工作中我们将每个独立关节量化为一个向量，这具有以下优势：i) 简化了量化过程，因为单个关节的复杂度显著低于整个姿态；ii) 保持了时空结构，既保留了关节间的空间关系，也维持了时间维度的运动模式；iii) 生成了二维令牌图，从而能够广泛应用在二维图像中常见的各种二维操作。基于此二维运动量化方法，我们构建了一个时空建模框架，其中提出了二维关节VQVAE、时序-空间二维掩码技术以及时空二维注意力机制，以充分利用二维令牌间的时空信号。大量实验表明，我们的方法在不同数据集上均显著优于先前方法，在HumanML3D数据集上FID指标降低了$26.6\%$，在KIT-ML数据集上降低了$29.9\%$。