  Facial attributes in StyleGAN generated images are entangled in the latent
space which makes it very difficult to independently control a specific
attribute without affecting the others. Supervised attribute editing requires
annotated training data which is difficult to obtain and limits the editable
attributes to those with labels. Therefore, unsupervised attribute editing in
an disentangled latent space is key to performing neat and versatile semantic
face editing. In this paper, we present a new technique termed
Structure-Texture Independent Architecture with Weight Decomposition and
Orthogonal Regularization (STIA-WO) to disentangle the latent space for
unsupervised semantic face editing. By applying STIA-WO to GAN, we have
developed a StyleGAN termed STGAN-WO which performs weight decomposition
through utilizing the style vector to construct a fully controllable weight
matrix to regulate image synthesis, and employs orthogonal regularization to
ensure each entry of the style vector only controls one independent feature
matrix. To further disentangle the facial attributes, STGAN-WO introduces a
structure-texture independent architecture which utilizes two independently and
identically distributed (i.i.d.) latent vectors to control the synthesis of the
texture and structure components in a disentangled way. Unsupervised semantic
editing is achieved by moving the latent code in the coarse layers along its
orthogonal directions to change texture related attributes or changing the
latent code in the fine layers to manipulate structure related ones. We present
experimental results which show that our new STGAN-WO can achieve better
attribute editing than state of the art methods.
