SalS-GAN: Spatially-Adaptive Latent Space in StyleGAN for Real Image Embedding

Many GAN inversion methods have emerged to embed a given real image into the latent space of GAN for real image editing. These methods usually use a latent space composed of a series of one-dimensional vectors as an optimization space to reconstruct real images such as W+ latent space. However, the reconstructed image of these methods is usually difficult to maintain the rich detailed information in the real image. How to better preserve details in the real image is still a challenge. To solve this problem, we propose a spatially-adaptive latent space, called SA latent space, and adopt it as the optimization latent space in GAN inversion task. In particular, we use the affine transformation parameters of each convolutional layer in the generator to form the SA latent space and change affine transformation parameters from a one-dimensional vector to a spatially-adaptive three-dimensional tensor. With the more expressive latent space, we can better reconstruct the details of the real image. Extensive experiments suggest that the image reconstruction quality can be significantly improved while maintaining the semantic disentanglement ability of latent code. The code is available at https://github.com/zhang-lingyun/SalS-GAN.