Language-guided human motion synthesis has been a challenging task due to the inherent complexity and diversity of human behaviors. Previous methods face limitations in generalization to novel actions, often resulting in unrealistic or incoherent motion sequences. In this paper, we propose ATOM (ATomic mOtion Modeling) to mitigate this problem, by decomposing actions into atomic actions, and employing a curriculum learning strategy to learn atomic action composition. First, we disentangle complex human motions into a set of atomic actions during learning, and then assemble novel actions using the learned atomic actions, which offers better adaptability to new actions. Moreover, we introduce a curriculum learning training strategy that leverages masked motion modeling with a gradual increase in the mask ratio, and thus facilitates atomic action assembly. This approach mitigates the overfitting problem commonly encountered in previous methods while enforcing the model to learn better motion representations. We demonstrate the effectiveness of ATOM through extensive experiments, including text-to-motion and action-to-motion synthesis tasks. We further illustrate its superiority in synthesizing plausible and coherent text-guided human motion sequences.

ATOM通过将动作分解为原子动作，并采用课程学习策略来组合原子动作，从而更好地适应新动作。同时引入了渐进式的蒙版运动建模的课程学习训练策略，有效缓解了过拟合问题，并提高了运动表示的学习质量。通过广泛的实验，包括文本到运动和动作到运动合成任务，证明了ATOM的有效性，并展示了它在合成可行和连贯的文本指导人体动作序列方面的优势。

带有原子动作的语言引导的人体运动合成