A Compact and Semantic Latent Space for Disentangled and Controllable
  Image Editing
Gwilherm Lesné Yann Gousseau Saïd Ladjal Alasdair Newson
Recent advances in the field of generative models and in particular
generative adversarial networks (GANs) have lead to substantial progress for
controlled image editing, especially compared with the pre-deep learning era.
Despite their powerful ability to apply realistic modifications to an image,
these methods often lack properties like disentanglement (the capacity to edit
attributes independently). In this paper, we propose an auto-encoder which
re-organizes the latent space of StyleGAN, so that each attribute which we wish
to edit corresponds to an axis of the new latent space, and furthermore that
the latent axes are decorrelated, encouraging disentanglement. We work in a
compressed version of the latent space, using Principal Component Analysis,
meaning that the parameter complexity of our autoencoder is reduced, leading to
short training times ($\sim$ 45 mins). Qualitative and quantitative results
demonstrate the editing capabilities of our approach, with greater
disentanglement than competing methods, while maintaining fidelity to the
original image with respect to identity. Our autoencoder architecture simple
and straightforward, facilitating implementation.