  Facial expression editing has attracted increasing attention with the advance
of deep neural networks in recent years. However, most existing methods suffer
from compromised editing fidelity and limited usability as they either ignore
pose variations (unrealistic editing) or require paired training data (not easy
to collect) for pose controls. This paper presents POCE, an innovative
pose-controllable expression editing network that can generate realistic facial
expressions and head poses simultaneously with just unpaired training images.
POCE achieves the more accessible and realistic pose-controllable expression
editing by mapping face images into UV space, where facial expressions and head
poses can be disentangled and edited separately. POCE has two novel designs.
The first is self-supervised UV completion that allows to complete UV maps
sampled under different head poses, which often suffer from self-occlusions and
missing facial texture. The second is weakly-supervised UV editing that allows
to generate new facial expressions with minimal modification of facial
identity, where the synthesized expression could be controlled by either an
expression label or directly transplanted from a reference UV map via feature
transfer. Extensive experiments show that POCE can learn from unpaired face
images effectively, and the learned model can generate realistic and
high-fidelity facial expressions under various new poses.
