  Face attribute editing aims to generate faces with one or multiple desired
face attributes manipulated while other details are preserved. Unlike prior
works such as GAN inversion, which has an expensive reverse mapping process, we
propose a simple feed-forward network to generate high-fidelity manipulated
faces. By simply employing some existing and easy-obtainable prior information,
our method can control, transfer, and edit diverse attributes of faces in the
wild. The proposed method can consequently be applied to various applications
such as face swapping, face relighting, and makeup transfer. In our method, we
decouple identity, expression, pose, and illumination using 3D priors; separate
texture and colors by using region-wise style codes. All the information is
embedded into adversarial learning by our identity-style normalization module.
Disentanglement losses are proposed to enhance the generator to extract
information independently from each attribute. Comprehensive quantitative and
qualitative evaluations have been conducted. In a single framework, our method
achieves the best or competitive scores on a variety of face applications.
