AUTHOR=Yang Shuai , Qiao Kai , Qin Ruoxi , Xie Pengfei , Shi Shuhao , Liang Ningning , Wang Linyuan , Chen Jian , Hu Guoen , Yan Bin 

TITLE=ShapeEditor: A StyleGAN Encoder for Stable and High Fidelity Face Swapping

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 15 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2021.785808

DOI=10.3389/fnbot.2021.785808

ISSN=1662-5218

ABSTRACT=In this paper, we propose a novel encoder, called ShapeEditor, for one-shot, realistic and high-fidelity face exchange. First of all, in order to ensure sufficient clarity and authenticity, our key idea is to use an advanced pretrained high-quality random face image generator, i.e. StyleGAN, as backbone. Secondly, we design ShapeEditor, a two-step encoder, to make the swapped face integrate the identity and attribute of the input faces. In the first step, we extract the identity vector of the source image and the attribute vector of the target image respectively; in the second step, we map the concatenation of identity vector and attribute vector into the $\mathcal{W+}$ potential space. In addition, for learning to map into the latent space of StyleGAN, we propose a set of self-supervised loss functions with which the training data do not need to be labeled manually. Extensive experiments on the test dataset show that the results of our method not only have a great advantage in clarity and authenticity than other state-of-the-art methods, but also reflect the sufficient integration of identity and attribute.