Mask-aware Photorealistic Face Attribute Manipulation
This work addresses the problem of realistic face editing for applications in image processing, though it appears incremental as it builds on existing VAE-GAN frameworks.
The paper tackles the challenge of photorealistic face attribute manipulation while preserving unique facial details, proposing a method that combines VAE and GAN with face recognition and cycle consistency losses, and demonstrates high-quality image generation that outperforms prior methods in detail preservation.
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.