Disentangling Latent Space for Unsupervised Semantic Face Editing
Editing facial images created by StyleGAN is a popular research topic with important applications. Through editing the latent vectors, it is possible to control the facial attributes such as smile, age, textit{etc}...
However, facial attributes are entangled in the latent space and this makes it very difficult to independently control a specific attribute without affecting the others. The key to developing neat semantic control is to completely disentangle the latent space and perform image editing in an unsupervised manner. In this paper, we present a new technique termed Structure-Texture Independent Architecture with Weight Decomposition and Orthogonal