The Devil is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN Inversion and High Quality Image Editing
Denis Bobkov, Vadim Titov, Aibek Alanov, Dmitry Vetrov
2024-06-21
Summary
This paper introduces a new method called StyleFeatureEditor that improves how we edit images using a technique called StyleGAN inversion, allowing for high-quality image manipulation while preserving fine details.
What's the problem?
Editing real images using StyleGAN has been challenging because previous methods either did a good job of editing but lost important details or managed to keep details but struggled with effective editing. This means that when you wanted to change something in an image, like a person's hair color or facial expression, the results often didn't look as good as they could have.
What's the solution?
The researchers developed StyleFeatureEditor, which allows for editing in both low-dimensional (w-latents) and high-dimensional (F-latents) spaces. This method helps maintain the fine details of images while making edits. They also created a new training process to help the model learn how to edit these high-dimensional features effectively. Their approach outperforms existing methods in terms of both detail preservation and editing quality, even when working with complex images.
Why it matters?
This research is significant because it enhances the capabilities of image editing tools, making them more effective for applications like graphic design, video game development, and virtual reality. By improving how images can be manipulated without losing detail, it opens up new possibilities for creativity and innovation in visual media.
Abstract
The task of manipulating real image attributes through StyleGAN inversion has been extensively researched. This process involves searching latent variables from a well-trained StyleGAN generator that can synthesize a real image, modifying these latent variables, and then synthesizing an image with the desired edits. A balance must be struck between the quality of the reconstruction and the ability to edit. Earlier studies utilized the low-dimensional W-space for latent search, which facilitated effective editing but struggled with reconstructing intricate details. More recent research has turned to the high-dimensional feature space F, which successfully inverses the input image but loses much of the detail during editing. In this paper, we introduce StyleFeatureEditor -- a novel method that enables editing in both w-latents and F-latents. This technique not only allows for the reconstruction of finer image details but also ensures their preservation during editing. We also present a new training pipeline specifically designed to train our model to accurately edit F-latents. Our method is compared with state-of-the-art encoding approaches, demonstrating that our model excels in terms of reconstruction quality and is capable of editing even challenging out-of-domain examples. Code is available at https://github.com/AIRI-Institute/StyleFeatureEditor.