  Composing simple elements into complex concepts is crucial yet challenging,
especially for 3D action generation. Existing methods largely rely on extensive
neural language annotations to discern composable latent semantics, a process
that is often costly and labor-intensive. In this study, we introduce a novel
framework to generate compositional actions without reliance on language
auxiliaries. Our approach consists of three main components: Action Coupling,
Conditional Action Generation, and Decoupling Refinement. Action Coupling
utilizes an energy model to extract the attention masks of each sub-action,
subsequently integrating two actions using these attentions to generate
pseudo-training examples. Then, we employ a conditional generative model, CVAE,
to learn a latent space, facilitating the diverse generation. Finally, we
propose Decoupling Refinement, which leverages a self-supervised pre-trained
model MAE to ensure semantic consistency between the sub-actions and
compositional actions. This refinement process involves rendering generated 3D
actions into 2D space, decoupling these images into two sub-segments, using the
MAE model to restore the complete image from sub-segments, and constraining the
recovered images to match images rendered from raw sub-actions. Due to the lack
of existing datasets containing both sub-actions and compositional actions, we
created two new datasets, named HumanAct-C and UESTC-C, and present a
corresponding evaluation metric. Both qualitative and quantitative assessments
are conducted to show our efficacy.
