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

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

ai artificial intelligence face machine learning machine learning & data science microsoft ml research semantic sota space technology