SeCGAN: Parallel Conditional Generative Adversarial Networks for Face Editing via Semantic Consistency. (arXiv:2111.09298v4 [cs.CV] UPDATED)

Semantically guided conditional Generative Adversarial Networks (cGANs) have
become a popular approach for face editing in recent years. However, most
existing methods introduce semantic masks as direct conditional inputs to the
generator and often require the target masks to perform the corresponding
translation in the RGB space. We propose SeCGAN, a novel label-guided cGAN for
editing face images utilising semantic information without the need to specify
target semantic masks. During training, SeCGAN has two branches of generators
and discriminators operating …

arxiv cv face generative adversarial networks networks semantic