Mask-aware Photorealistic Face Attribute Manipulation

Abstract

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.

arXiv comment7 pages, 4 figures. Computational Visual Media(2021)

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Cited by in corpus (7)

• RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes
• Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
• SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images
• FaceController: Controllable Attribute Editing for Face in the Wild
• A Survey of Deep Facial Attribute Analysis
• Texture Deformation Based Generative Adversarial Networks for Face Editing
• Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneously