Text-to-motion generation requires synthesizing physically realistic dynamics that strictly follow complex and long-horizon textual instructions. Existing approaches rely on homogeneous representation spaces that may fail to capture the hierarchical nature of human motion, with diffusion models struggling at compositional semantic reasoning and AR models sacrificing fine-grained physical details due to quantization. To solve it, we introduce DC-Motion, a factorized generative framework designed to explicitly decouple semantics and details via discrete-continuous tokens. A Discrete-Continuous VAE (DC-VAE) first decomposes motion into discrete tokens for semantics and continuous residuals for fine-grained dynamics. Then, a masked AR model predicts the discrete structure from text, and a lightweight residual diffusion model recovers the continuous physical details. Extensive experiments demonstrate that DC-Motion effectively improves the capability to follow complex instructions. By effectively balancing semantic controllability and physical realism, our approach offers a highly adaptable modeling paradigm for human motion generation. On both HumanML3D and KIT-ML datasets, DC-Motion achieves state-of-the-art performance, delivering the best FID for motion realism and R-precision for text alignment.

翻译：文本到运动生成要求合成的物理动态严格遵循复杂且长程的文本指令。现有方法依赖同质表示空间，难以捕捉人体运动的层次特性：扩散模型在组合语义推理方面表现不足，而自回归模型因量化问题牺牲了细粒度的物理细节。为解决这一问题，我们提出DC-Motion——一种通过离散-连续令牌显式解耦语义与细节的分解式生成框架。首先，离散-连续变分自编码器将运动分解为：语义的离散令牌与细粒度动态的连续残差。随后，掩码自回归模型根据文本预测离散结构，轻量级残差扩散模型恢复连续的物理细节。大量实验表明，DC-Motion能有效提升对复杂指令的遵循能力。通过平衡语义可控性与物理真实性，该方法为人体运动生成提供了高度自适应的建模范式。在HumanML3D和KIT-ML数据集上，DC-Motion均达到最优性能，在运动真实感（FID）与文本对齐精度（R-precision）方面取得最佳结果。