ShapeEditor: A StyleGAN Encoder for Stable and High Fidelity Face Swapping - Yang, Shuai; Qiao, Kai; Qin, Ruoxi; Xie, Pengfei; Shi, Shuhao; Liang, Ningning; Wang, Linyuan; Chen, Jian; Hu, Guoen; Yan, Bin (Frontiers in Neurorobotics, 2022)
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.