  In this paper, we present an integrated system for automatically generating
and editing face images through face swapping, attribute-based editing, and
random face parts synthesis. The proposed system is based on a deep neural
network that variationally learns the face and hair regions with large-scale
face image datasets. Different from conventional variational methods, the
proposed network represents the latent spaces individually for faces and hairs.
We refer to the proposed network as region-separative generative adversarial
network (RSGAN). The proposed network independently handles face and hair
appearances in the latent spaces, and then, face swapping is achieved by
replacing the latent-space representations of the faces, and reconstruct the
entire face image with them. This approach in the latent space robustly
performs face swapping even for images which the previous methods result in
failure due to inappropriate fitting or the 3D morphable models. In addition,
the proposed system can further edit face-swapped images with the same network
by manipulating visual attributes or by composing them with randomly generated
face or hair parts.
