Optimizing Latent Space Directions for Gan-Based Local Image Editing

Abstract

Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic at-tributes. We thus present a novel objective function to evaluate the locality of an image edit. By introducing the super-vision from a pre-trained segmentation network and optimizing the objective function, our framework, called Locally Effective Latent Space Direction (LELSD), is applicable to any dataset and GAN architecture. Our method is also computationally fast and exhibits a high extent of disentanglement, which allows users to interactively perform a sequence of edits on an image. Our experiments on both GAN-generated and real images qualitatively demonstrate the high quality and advantages of our method.