Improved StyleGAN Embedding: Where are the Good Latents?
This work is incrementally improving StyleGAN embedding for researchers and practitioners who need editable image reconstructions.
The paper addresses the challenge of finding StyleGAN embeddings that both accurately reconstruct images and are robust to editing operations. They introduce a normalized space to analyze latent code quality and propose an improved embedding algorithm with a novel regularization method, achieving a better trade-off between reconstruction and editing quality compared to state-of-the-art methods.
StyleGAN is able to produce photorealistic images that are almost indistinguishable from real photos. The reverse problem of finding an embedding for a given image poses a challenge. Embeddings that reconstruct an image well are not always robust to editing operations. In this paper, we address the problem of finding an embedding that both reconstructs images and also supports image editing tasks. First, we introduce a new normalized space to analyze the diversity and the quality of the reconstructed latent codes. This space can help answer the question of where good latent codes are located in latent space. Second, we propose an improved embedding algorithm using a novel regularization method based on our analysis. Finally, we analyze the quality of different embedding algorithms. We compare our results with the current state-of-the-art methods and achieve a better trade-off between reconstruction quality and editing quality.