Northeastern U & Microsoft Expand StyleGAN’s Latent Space to Surpass the SOTA on Real Face Semantic Editing

Open-sourced by Nvidia three years ago, StyleGAN has wowed the Internet with its stunning human face synthesis capabilities. More recently, the powerful generative network has also shown its talents in semantic image editing, where it can modify a subject’s age, expression, gender, etc., in high-quality images. Performing such edits on real face images however introduces a number of challenges. The input images often contain out-of-distribution identities, hairstyles, lighting conditions etc., and it is difficult to find the StyleGAN latent variables that will best preserve these characteristics to produce realistic manipulations. Moreover, previous studies have revealed entanglement problems, where modifying one attribute will also affect other facial features.

To address these issues, a research team from Northeastern University and Microsoft has proposed a novel two-branch approach that expands the latent space of StyleGAN to enable identity-preserving and disentangled-attribute editing for real face images. Introduced in the new paper Expanding the Latent Space of StyleGAN for Real Face Editing, the method achieves both qualitative and quantitative improvements over state-of-the-art methods.