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.

本研究使用变分自动编码器 (VAE) 和生成式对抗网络 (GAN) 相结合的方法，设计了 M-AAE 算法，在保持面部细节的前提下修改图片特征，通过面部识别损失和循环一致性损失，以及生成面部口罩来强化 VAE 和 GAN 的训练目标，成功生成了高质量、保留细节的图像，并超越了现有方法。