Language-guided Human Motion Synthesis with Atomic Actions
Yuanhao Zhai Mingzhen Huang Tianyu Luan Lu Dong Ifeoma Nwogu Siwei Lyu David Doermann Junsong Yuan
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.