  Manipulating latent code in generative adversarial networks (GANs) for facial
image synthesis mainly focuses on continuous attribute synthesis (e.g., age,
pose and emotion), while discrete attribute synthesis (like face mask and
eyeglasses) receives less attention. Directly applying existing works to facial
discrete attributes may cause inaccurate results. In this work, we propose an
innovative framework to tackle challenging facial discrete attribute synthesis
via semantic decomposing, dubbed SD-GAN. To be concrete, we explicitly
decompose the discrete attribute representation into two components, i.e. the
semantic prior basis and offset latent representation. The semantic prior basis
shows an initializing direction for manipulating face representation in the
latent space. The offset latent presentation obtained by 3D-aware semantic
fusion network is proposed to adjust prior basis. In addition, the fusion
network integrates 3D embedding for better identity preservation and discrete
attribute synthesis. The combination of prior basis and offset latent
representation enable our method to synthesize photo-realistic face images with
discrete attributes. Notably, we construct a large and valuable dataset MEGN
(Face Mask and Eyeglasses images crawled from Google and Naver) for completing
the lack of discrete attributes in the existing dataset. Extensive qualitative
and quantitative experiments demonstrate the state-of-the-art performance of
our method. Our code is available at: https://github.com/MontaEllis/SD-GAN.
