GlassesGAN: Eyewear Personalization using Synthetic Appearance Discovery and Targeted Subspace Modeling

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. To improve the reliability of our learned edits, we also introduce an appearance-constrained subspace initialization (SI) technique able to center the latent representation of a given input image in the well-defined part of the constructed subspace. We test GlassesGAN on three diverse datasets (CelebA-HQ, SiblingsDB-HQf, and MetFaces) and compare it against three state-of-the-art competitors, i.e., InterfaceGAN, GANSpace, and MaskGAN. Our experimental results show that GlassesGAN achieves photo-realistic, multi-style edits to eyeglasses while comparing favorably to its competitors. The source code is made freely available.