Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space? Rameen Abdal, Yipeng Qin, and Peter Wonka propose an efficient algorithm to embed images into the latent space of StyleGAN, enabling semantic image editing. The algorithm maps a given image into the extended latent space W+ of a pre-trained StyleGAN, allowing for image morphing, style transfer, and expression transfer. The study investigates the structure of the StyleGAN latent space, testing what types of images can be embedded, how they are embedded, and whether the embedding is semantically meaningful. The results show that the algorithm can embed human face images and non-face images from different classes, revealing the generality of the learned filters of the generator. The embedding is robust to affine transformations and defects in images, and the quality of the embedding is evaluated using three basic operations on vectors in the latent space: linear interpolation, crossover, and adding a vector and a scaled difference vector. The study also explores the impact of different latent spaces and loss functions on the embedding quality, finding that the extended latent space W+ provides better results. The algorithm is optimized using a perceptual loss and pixel-wise MSE loss, achieving high-quality embeddings. The results show that the embedding is most effective for human faces, but can also be applied to non-face images. The study concludes that embedding works best into the extended latent space W+ and that any type of image can be embedded, although only the embedding of faces is semantically meaningful. The framework has limitations, including image artifacts from pre-trained StyleGAN and the need for optimization time. Future work includes extending the framework to process videos and embeddings into GANs trained on three-dimensional data.Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space? Rameen Abdal, Yipeng Qin, and Peter Wonka propose an efficient algorithm to embed images into the latent space of StyleGAN, enabling semantic image editing. The algorithm maps a given image into the extended latent space W+ of a pre-trained StyleGAN, allowing for image morphing, style transfer, and expression transfer. The study investigates the structure of the StyleGAN latent space, testing what types of images can be embedded, how they are embedded, and whether the embedding is semantically meaningful. The results show that the algorithm can embed human face images and non-face images from different classes, revealing the generality of the learned filters of the generator. The embedding is robust to affine transformations and defects in images, and the quality of the embedding is evaluated using three basic operations on vectors in the latent space: linear interpolation, crossover, and adding a vector and a scaled difference vector. The study also explores the impact of different latent spaces and loss functions on the embedding quality, finding that the extended latent space W+ provides better results. The algorithm is optimized using a perceptual loss and pixel-wise MSE loss, achieving high-quality embeddings. The results show that the embedding is most effective for human faces, but can also be applied to non-face images. The study concludes that embedding works best into the extended latent space W+ and that any type of image can be embedded, although only the embedding of faces is semantically meaningful. The framework has limitations, including image artifacts from pre-trained StyleGAN and the need for optimization time. Future work includes extending the framework to process videos and embeddings into GANs trained on three-dimensional data.