  Facial attribute editing aims to manipulate attributes on the human face,
e.g., adding a mustache or changing the hair color. Existing approaches suffer
from a serious compromise between correct attribute generation and preservation
of the other information such as identity and background, because they edit the
attributes in the imprecise area. To resolve this dilemma, we propose a
progressive attention GAN (PA-GAN) for facial attribute editing. In our
approach, the editing is progressively conducted from high to low feature level
while being constrained inside a proper attribute area by an attention mask at
each level. This manner prevents undesired modifications to the irrelevant
regions from the beginning, and then the network can focus more on correctly
generating the attributes within a proper boundary at each level. As a result,
our approach achieves correct attribute editing with irrelevant details much
better preserved compared with the state-of-the-arts. Codes are released at
https://github.com/LynnHo/PA-GAN-Tensorflow.
