  Although manipulating facial attributes by Generative Adversarial Networks
(GANs) has been remarkably successful recently, there are still some challenges
in explicit control of features such as pose, expression, lighting, etc. Recent
methods achieve explicit control over 2D images by combining 2D generative
model and 3DMM. However, due to the lack of realism and clarity in texture
reconstruction by 3DMM, there is a domain gap between the synthetic image and
the rendered image of 3DMM. Since rendered 3DMM images contain facial region
only without the background, directly computing the loss between these two
domains is not ideal and the resultant trained model will be biased. In this
study, we propose to explicitly edit the latent space of the pretrained
StyleGAN by controlling the parameters of the 3DMM. To address the domain gap
problem, we propose a noval network called 'Map and edit' and a simple but
effective attribute editing method to avoid direct loss computation between
rendered and synthesized images. Furthermore, since our model can accurately
generate multi-view face images while the identity remains unchanged. As a
by-product, combined with visibility masks, our proposed model can also
generate texture-rich and high-resolution UV facial textures. Our model relies
on pretrained StyleGAN, and the proposed model is trained in a self-supervised
manner without any manual annotations or datasets.
