  Recent advances like StyleGAN have promoted the growth of controllable facial
editing. To address its core challenge of attribute decoupling in a single
latent space, attempts have been made to adopt dual-space GAN for better
disentanglement of style and content representations. Nonetheless, these
methods are still incompetent to obtain plausible editing results with high
controllability, especially for complicated attributes. In this study, we
highlight the importance of interaction in a dual-space GAN for more
controllable editing. We propose TransEditor, a novel Transformer-based
framework to enhance such interaction. Besides, we develop a new dual-space
editing and inversion strategy to provide additional editing flexibility.
Extensive experiments demonstrate the superiority of the proposed framework in
image quality and editing capability, suggesting the effectiveness of
TransEditor for highly controllable facial editing.
