English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent advances like StyleGAN have promoted the growth of controllable facialediting. To address its core challenge of attribute decoupling in a singlelatent space, attempts have been made to adopt dual-space GAN for betterdisentanglement of style and content representations. Nonetheless, thesemethods are still incompetent to obtain plausible editing results with highcontrollability, especially for complicated attributes. In this study, wehighlight the importance of interaction in a dual-space GAN for morecontrollable editing. We propose TransEditor, a novel Transformer-basedframework to enhance such interaction. Besides, we develop a new dual-spaceediting and inversion strategy to provide additional editing flexibility.Extensive experiments demonstrate the superiority of the proposed framework inimage quality and editing capability, suggesting the effectiveness ofTransEditor for highly controllable facial editing.

TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing