The Devil is in the Details 😈

So essentially,

AI Model StyleFeatureEditor gives highly detailed image editing results!

Paper: The Devil is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN Inversion and High Quality Image Editing (18 Pages)

Researchers from HSE University, AIRI, and Constructor University, Bremen want to make better quality image editing AI. The researchers introduce StyleFeatureEditor, a novel approach for real image editing that utilizes StyleGAN inversion.

Source: https://lexica.art/prompt/49b90311-df71-40b4-9c9a-dd62ab098849

Hmm..What’s the background?

Generative Adversarial Networks (GANs) have shown impressive results in image generation, and one of the most successful models, StyleGAN, not only generates high-quality images but also has a semantically rich latent space. The challenge lies in applying these editing techniques to real images, which requires finding their corresponding representation in the StyleGAN latent space – a problem referred to as GAN inversion. Existing GAN inversion approaches face challenges in simultaneously achieving high-quality reconstruction, good edit ability, and fast inference.

Ok, So what is proposed in the research paper?

1. Researchers introduce StyleFeatureEditor, a novel method that combines W-latent and F-latent editing, enabling the reconstruction of finer image details and their preservation during editing.

2. The method uses a two-phase training strategy

   1. The first phase trains an encoder to predict a latent code in high-resolution Fk space for high-quality image reconstruction

   2. The second phase introduces a Feature Editor module trained to modify the feature tensor Fk for editing

3. Quantitative and qualitative evaluations were conducted using the FFHQ and CelebA-HQ datasets for the face domain and the Stanford Cars dataset for the car domain.

StyleFeatureEditor outperforms previous methods in reconstructing and preserving fine details during image editing.

Source: https://huggingface.co/papers/2406.10601

What’s next?

The paper highlights that StyleFeatureEditor, while effective, still inherits artifacts from the additional encoder (E) used for editing information, which would be evolved. Additionally, the authors propose a masking technique to address artifacts occurring outside the face zone during editing but acknowledge its limitations in scenarios where edits extend beyond facial features.

So essentially,

AI Model StyleFeatureEditor gives highly detailed image editing results!