Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?
This work addresses the challenge of applying GAN-based editing to real-world images for users in computer vision and graphics, though it is incremental as it builds on existing StyleGAN methods.
The authors tackled the problem of embedding arbitrary images into the StyleGAN latent space, enabling semantic editing like morphing and style transfer on photographs, with results demonstrated on the FFHQ dataset.
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.