  We present GlassesGAN, a novel image editing framework for custom design of
glasses, that sets a new standard in terms of image quality, edit realism, and
continuous multi-style edit capability. To facilitate the editing process with
GlassesGAN, we propose a Targeted Subspace Modelling (TSM) procedure that,
based on a novel mechanism for (synthetic) appearance discovery in the latent
space of a pre-trained GAN generator, constructs an eyeglasses-specific
(latent) subspace that the editing framework can utilize. Additionally, we also
introduce an appearance-constrained subspace initialization (SI) technique that
centers the latent representation of the given input image in the well-defined
part of the constructed subspace to improve the reliability of the learned
edits. We test GlassesGAN on two (diverse) high-resolution datasets (CelebA-HQ
and SiblingsDB-HQf) and compare it to three state-of-the-art competitors, i.e.,
InterfaceGAN, GANSpace, and MaskGAN. The reported results show that GlassesGAN
convincingly outperforms all competing techniques, while offering additional
functionality (e.g., fine-grained multi-style editing) not available with any
of the competitors. The source code will be made freely available.
