  Image synthesis has witnessed substantial progress due to the increasing
power of generative model. This paper we propose a novel generative approach
for exemplar based facial editing in the form of the region inpainting. Our
method first masks the facial editing region to eliminates the pixel
constraints of the original image, then exemplar based facial editing can be
achieved by learning the corresponding information from the reference image to
complete the masked region. In additional, we impose the attribute labels
constraint to model disentangled encodings in order to avoid undesired
information being transferred from the exemplar to the original image editing
region. Experimental results demonstrate our method can produce diverse and
personalized face editing results and provide far more user control flexibility
than nearly all existing methods.
