Improved StyleGAN Embedding: Where are the Good Latents?

TL;DR

This paper introduces a new analysis space and an improved embedding algorithm for StyleGAN that enhances the balance between image reconstruction fidelity and editing robustness.

Contribution

It proposes a normalized analysis space and a novel regularization method to improve StyleGAN embeddings, balancing reconstruction and editing capabilities.

Findings

Better trade-off between reconstruction and editing quality.

Analysis space reveals where good latent codes are located.

Improved embedding algorithm outperforms state-of-the-art methods.

Abstract

StyleGAN is able to produce photorealistic images that are almost indistinguishable from real photos. The reverse problem of finding an embedding for a given image poses a challenge. Embeddings that reconstruct an image well are not always robust to editing operations. In this paper, we address the problem of finding an embedding that both reconstructs images and also supports image editing tasks. First, we introduce a new normalized space to analyze the diversity and the quality of the reconstructed latent codes. This space can help answer the question of where good latent codes are located in latent space. Second, we propose an improved embedding algorithm using a novel regularization method based on our analysis. Finally, we analyze the quality of different embedding algorithms. We compare our results with the current state-of-the-art methods and achieve a better trade-off between reconstruction quality and editing quality.