Language-Free Compositional Action Generation via Decoupling Refinement
Xiao Liu, Guangyi Chen, Yansong Tang, Guangrun Wang, Xiao-Ping Steven Zhang, Ser-Nam Lim · 2024
Composing simple actions into complex actions is crucial yet challenging. Existing methods largely rely on language annotations to discern composable latent semantics, which is costly and labor-intensive. In this study, we introduce a novel framework to generate compositional actions without language auxiliaries. Our approach consists of three components: Action Coupling, Conditional Action Generation, and Decoupling Refinement. Action Coupling integrates two sub-actions to generate pseudo-training examples. Then, a conditional generative model, CVAE is employed to facilitate the diverse generation. Decoupling Refinement leverages a self-supervised pre-trained model MAE to ensure semantic consistency between sub-actions and compositional actions. Due to the lack of existing datasets containing both sub-actions and compositional actions, we create two new datasets, named HumanAct-C and UESTC-C. Both qualitative and quantitative assessments are conducted to show our efficacy.1