  The task of face attribute manipulation has found increasing applications,
but still remains challenging with the requirement of editing the attributes of
a face image while preserving its unique details. In this paper, we choose to
combine the Variational AutoEncoder (VAE) and Generative Adversarial Network
(GAN) for photorealistic image generation. We propose an effective method to
modify a modest amount of pixels in the feature maps of an encoder, changing
the attribute strength continuously without hindering global information. Our
training objectives of VAE and GAN are reinforced by the supervision of face
recognition loss and cycle consistency loss for faithful preservation of face
details. Moreover, we generate facial masks to enforce background consistency,
which allows our training to focus on manipulating the foreground face rather
than background. Experimental results demonstrate our method, called
Mask-Adversarial AutoEncoder (M-AAE), can generate high-quality images with
changing attributes and outperforms prior methods in detail preservation.
