Generating realistic human motion sequences from text descriptions is a
challenging task that requires capturing the rich expressiveness of both
natural language and human motion.Recent advances in diffusion models have
enabled significant progress in human motion synthesis.However, existing
methods struggle to handle text inputs that describe complex or long motions.In
this paper, we propose the Adaptable Motion Diffusion (AMD) model, which
leverages a Large Language Model (LLM) to parse the input text into a sequence
of concise and interpretable anatomical scripts that correspond to the target
motion.This process exploits the LLM's ability to provide anatomical guidance
for complex motion synthesis.We then devise a two-branch fusion scheme that
balances the influence of the input text and the anatomical scripts on the
inverse diffusion process, which adaptively ensures the semantic fidelity and
diversity of the synthesized motion.Our method can effectively handle texts
with complex or long motion descriptions, where existing methods often fail.
Experiments on datasets with relatively more complex motions, such as CLCD1 and
CLCD2, demonstrate that our AMD significantly outperforms existing
state-of-the-art models.

通过利用大型语言模型解析输入的文本为与目标动作相对应的简明易懂的解剖脚本序列，本文提出了适应性运动扩散 (AMD) 模型，以解决现有方法难以处理复杂或长动作描述的问题。通过制定平衡输入文本和解剖脚本对逆扩散过程影响的两支融合方案，本方法能够自适应地确保合成动作的语义保真度和多样性。在包含较为复杂动作的数据集（例如 CLCD1 和 CLCD2）上的实验证明，我们的 AMD 显著优于现有的最先进模型。