$S^2$-Flow: Joint Semantic and Style Editing of Facial Images

TL;DR

This paper introduces $S^2$-Flow, a novel framework that disentangles a GAN's latent space into semantic and style components, enabling independent and precise editing of facial images.

Contribution

The paper presents a new method to separate semantic and style spaces in GANs, allowing for controlled, independent edits within a unified framework.

Findings

Effective disentanglement of semantic and style spaces demonstrated

Enables independent semantic and style editing of face images

Qualitative and quantitative validation of editing capabilities

Abstract

The high-quality images yielded by generative adversarial networks (GANs) have motivated investigations into their application for image editing. However, GANs are often limited in the control they provide for performing specific edits. One of the principal challenges is the entangled latent space of GANs, which is not directly suitable for performing independent and detailed edits. Recent editing methods allow for either controlled style edits or controlled semantic edits. In addition, methods that use semantic masks to edit images have difficulty preserving the identity and are unable to perform controlled style edits. We propose a method to disentangle a GAN$\text{'}$s latent space into semantic and style spaces, enabling controlled semantic and style edits for face images independently within the same framework. To achieve this, we design an encoder-decoder based network architecture ($S^2$-Flow), which incorporates two proposed inductive biases. We show the suitability of $S^2$-Flow quantitatively and qualitatively by performing various semantic and style edits.