  Face manipulation has shown remarkable advances with the flourish of
Generative Adversarial Networks. However, due to the difficulties of
controlling structures and textures, it is challenging to model poses and
expressions simultaneously, especially for the extreme manipulation at
high-resolution. In this paper, we propose a novel framework that simplifies
face manipulation into two correlated stages: a boundary prediction stage and a
disentangled face synthesis stage. The first stage models poses and expressions
jointly via boundary images. Specifically, a conditional encoder-decoder
network is employed to predict the boundary image of the target face in a
semi-supervised way. Pose and expression estimators are introduced to improve
the prediction performance. In the second stage, the predicted boundary image
and the input face image are encoded into the structure and the texture latent
space by two encoder networks, respectively. A proxy network and a feature
threshold loss are further imposed to disentangle the latent space.
Furthermore, due to the lack of high-resolution face manipulation databases to
verify the effectiveness of our method, we collect a new high-quality
Multi-View Face (MVF-HQ) database. It contains 120,283 images at 6000x4000
resolution from 479 identities with diverse poses, expressions, and
illuminations. MVF-HQ is much larger in scale and much higher in resolution
than publicly available high-resolution face manipulation databases. We will
release MVF-HQ soon to push forward the advance of face manipulation.
Qualitative and quantitative experiments on four databases show that our method
dramatically improves the synthesis quality.
