Style-based Image Manipulation Using the StyleGAN2-Ada Architecture
Yuhong Lu · Applied and Computational Engineering · 2023
Style-based image manipulation is to fuse the types of two arbitrary images, which is a popular task in computer vision. StyleGAN is a sophisticated architecture for generating images of high qualities. The framework allows the generator to operate on a latent space that is disentangled and allows us to do scale-specific manipulation on the semantic information of the generated images. In this paper, the author managed to fuse the styles of two given images on a controllable degree. The resultant images have natural appearances approximating real human portraits. Our method provides qualitative results for style-fusion of two given images, which achieves satisfy. Since StyleGAN offers an unraveled latent space representing disentangled semantics, the author hopes to use it on tasks like GAN inversion and manipulate images in a fine-grained control, which is the future work.