Human motion generation aims to produce plausible human motion sequences according to various conditional inputs, such as text or audio. Despite the feasibility of existing methods in generating motion based on short prompts and simple motion patterns, they encounter difficulties when dealing with long prompts or complex motions. The challenges are two-fold: 1) the scarcity of human motion-captured data for long prompts and complex motions. 2) the high diversity of human motions in the temporal domain and the substantial divergence of distributions from conditional modalities, leading to a many-to-many mapping problem when generating motion with complex and long texts. In this work, we address these gaps by 1) elaborating the first dataset pairing long textual descriptions and 3D complex motions (HumanLong3D), and 2) proposing an autoregressive motion diffusion model (AMD). Specifically, AMD integrates the text prompt at the current timestep with the text prompt and action sequences at the previous timestep as conditional information to predict the current action sequences in an iterative manner. Furthermore, we present its generalization for X-to-Motion with "No Modality Left Behind", enabling the generation of high-definition and high-fidelity human motions based on user-defined modality input.

翻译：人体运动生成旨在根据文本或音频等各类条件输入生成合理的人体运动序列。尽管现有方法在基于简短提示和简单运动模式生成动作方面具有可行性，但在处理长提示或复杂运动时仍面临困难。这些挑战体现在两方面：1）针对长提示和复杂运动的人体运动捕捉数据稀缺；2）人体运动在时间维度上的高度多样性以及与条件模态分布的显著差异，导致在生成复杂长文本运动时出现多对多映射问题。本研究通过以下方式填补上述空白：1）构建首个包含长文本描述与3D复杂运动配对的数据集（HumanLong3D）；2）提出自回归运动扩散模型（AMD）。具体而言，AMD将当前时间步的文本提示与前一时刻的文本提示及动作序列整合为条件信息，以迭代方式预测当前时刻的动作序列。此外，我们提出"不遗漏任何模态"的X到运动泛化框架，支持基于用户自定义模态输入生成高清晰度、高保真度的人体运动。