  Recent studies have shown remarkable success in face image generations.
However, most of the existing methods only generate face images from random
noise, and cannot generate face images according to the specific attributes. In
this paper, we focus on the problem of face synthesis from attributes, which
aims at generating faces with specific characteristics corresponding to the
given attributes. To this end, we propose a novel attributes aware face image
generator method with generative adversarial networks called AFGAN.
Specifically, we firstly propose a two-path embedding layer and self-attention
mechanism to convert binary attribute vector to rich attribute features. Then
three stacked generators generate $64 \times 64$, $128 \times 128$ and $256
\times 256$ resolution face images respectively by taking the attribute
features as input. In addition, an image-attribute matching loss is proposed to
enhance the correlation between the generated images and input attributes.
Extensive experiments on CelebA demonstrate the superiority of our AFGAN in
terms of both qualitative and quantitative evaluations.
