# Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?

## TL;DR

This paper introduces an efficient method to embed images into StyleGAN's latent space, enabling semantic editing and providing insights into the structure of the latent space.

## Contribution

It presents a novel algorithm for image embedding into StyleGAN's latent space, facilitating semantic editing and analysis of the latent space structure.

## Key findings

- Successful image embedding enables semantic editing like morphing and style transfer.
- Insights into the structure and properties of StyleGAN's latent space.
- Evaluation of embedding quality across different image classes.

## Abstract

We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.