Face image synthesis from facial parts

Abstract

Recently, inspired by the growing power of deep convolutional neural networks (CNNs) and generative adversarial networks (GANs), facial image editing has received increasing attention and has produced a series of wide-ranging applications. In this paper, we propose a new and effective approach to a challenging task: synthesizing face images based on key facial parts. The proposed approach is a novel deep generative network that can automatically align facial parts with the precise positions in a face image and then output an entire facial image conditioned on the well-aligned parts. Specifically, three loss functions are introduced in this approach, which are the key to making the synthesized realistic facial image: a reconstruction loss to generate image content in an unknown region, a perceptual loss to enhance the network's ability to model high-level semantic structures and an adversarial loss to ensure that the synthesized images are visually realistic. In this approach, the three components cooperate well to form an effective framework for parts-based high-quality facial image synthesis. Finally, extensive experiments demonstrate the superior performance of this method to existing solutions.