# Mask-aware Photorealistic Face Attribute Manipulation

arXiv: https://arxiv.org/abs/1804.08882 · HTML: https://mlsanity.com/paper/1804.08882/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 4 of 4)

- [FaceController: Controllable Attribute Editing for Face in the Wild](https://mlsanity.com/paper/2102.11464/) (2021)
- [A comprehensive survey on semantic facial attribute editing using generative adversarial networks](https://mlsanity.com/paper/2205.10587/) (2022)
- [Can GAN-induced Attribute Manipulations Impact Face Recognition?](https://mlsanity.com/paper/2209.02941/) (2022)
- [Mitigating the Impact of Attribute Editing on Face Recognition](https://mlsanity.com/paper/2403.08092/) (2024)

## Abstract (arXiv)

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.