  Portrait editing is a popular subject in photo manipulation. The Generative
Adversarial Network (GAN) advances the generating of realistic faces and allows
more face editing. In this paper, we argue about three issues in existing
techniques: diversity, quality, and controllability for portrait synthesis and
editing. To address these issues, we propose a novel end-to-end learning
framework that leverages conditional GANs guided by provided face masks for
generating faces. The framework learns feature embeddings for every face
component (e.g., mouth, hair, eye), separately, contributing to better
correspondences for image translation, and local face editing. With the mask,
our network is available to many applications, like face synthesis driven by
mask, face Swap+ (including hair in swapping), and local manipulation. It can
also boost the performance of face parsing a bit as an option of data
augmentation.
