Commutative and Nonlinear Image Editing for Deep Generative Model

Abstract

Deep generative models, such as generative adversarial networks (GANs), can generate high-quality images. However, these models often do not have an inherent way to edit generated images semantically. In order to edit generated images semantically, recent studies have proposed methods to determine linear or nonlinear semantic paths on the latent space and edit images by manipulating latent codes along these paths. However, the quality of the image editing along linear paths is inferior, and the image editing along nonlinear paths is non-commutative. In this study, we propose to discover semantic curvilinear coordinates on the latent space. We experimentally show that the quality of our method's image editing is better than comparison methods, and our method provides commutative image editing.