# Image2StyleGAN++: How to Edit the Embedded Images?

**Authors:** Rameen Abdal, Yipeng Qin, Peter Wonka  
**arXiv:** [1911.11544](https://arxiv.org/abs/1911.11544)  
**Date:** 2019-11-26  
**Summary page:** https://www.summarizepaper.com/en/paper/1911.11544v2/

## Key points

- Image2StyleGAN++ is a flexible image editing framework with various applications
- Incorporates noise optimization to restore high-frequency features and improve image quality
- Extends global latent space embedding to enable local embeddings for high-quality local edits
- Supports advanced image editing applications such as image reconstruction, inpainting, crossover, style transfer, editing using scribbles, and attribute level feature transfer
- Examples of edited images provided throughout the paper to demonstrate effectiveness
- User scribbles can be converted into photo-realistic edits by embedding them into the $W^+$ space
- Utilizes masks and performs masked style transfer and masked noise optimization for high-quality results
- Powerful framework combining noise optimization, latent space embedding, and activation tensor manipulation
- Supports both global and local edits on images
- Potential impact in fields like video generation.

## Summary

Image2StyleGAN++ is a flexible image editing framework that offers various applications. It builds upon the previous Image2StyleGAN model and introduces three key enhancements. Firstly, it incorporates noise optimization as a complement to the latent space embedding, specifically the $W^+$ embedding. This noise optimization technique effectively restores high-frequency features in images, resulting in significantly improved image quality with a substantial increase in peak signal-to-noise ratio (PSNR) from 20 dB to 45 dB for reconstructed images. Secondly, Image2StyleGAN++ extends the global $W^+$ latent space embedding to enable local embeddings. By allowing local embeddings, this framework facilitates high-quality local edits alongside global semantic edits on images. This capability opens up numerous possibilities for advanced image editing applications such as image reconstruction, image inpainting, image crossover, local style transfer, image editing using scribbles and attribute level feature transfer. To demonstrate the effectiveness of these enhancements examples of edited images are provided throughout the paper for visual inspection. In this application simple user scribbles are converted into photo-realistic edits by embedding them into the first few layers of the $W^+$ space. The framework utilizes masks and performs masked style transfer along with masked noise optimization to achieve high-quality results. In conclusion, Image2StyleGAN++ presents a powerful image editing framework that combines noise optimization with latent space embedding and activation tensor manipulation enabling both global and local edits on images which supports various high-quality image editing applications demonstrated through examples and its potential impact highlighted in fields like video generation.