  Face editing is a popular research topic in the computer vision community
that aims to edit a specific characteristic of a face image. Recent proposed
methods are based on either training a conditional encoder-decoder Generative
Adversarial Network (GAN) in an end-to-end fashion or on defining an operation
in the latent space of a pre-trained vanilla GAN generator model. However,
these methods exhibit a certain degree of visual degradation and lack
disentanglement properties in the edited images. Moreover, they usually operate
on lower image resolution. In this paper, we propose a GAN embedding
optimization procedure with spatial and semantic constraints. We optimize a
latent code of a GAN, pre-trained on face dataset, to embed a fixed region of
the image, while imposing constraints on the inpainted regions with face
parsing and attribute classification networks. By latent code optimization, we
constrain the result to follow an image probability distribution, as defined by
the GAN model. We use such framework to produce high image quality face edits.
Due to the spatial constraints introduced, the edited images exhibit higher
degree of disentanglement between the desired facial attributes and the rest of
the image than other methods. The approach is validated in experiments on three
datasets and in comparison with four state-of-the-art approaches. The results
demonstrate that the proposed approach is able to edit face images with respect
to several facial attributes with unprecedented image quality, while
disentangling the undesired factors of variation. Code will be made available.
