InsetGAN for Full-Body Image Generation
we propose a novel method to combine multiple pretrained GANs where one GAN generates a global canvas (e.g., human body) and a set of specialized GANs, or insets, focus on different parts (e.g., faces, hands) that can be seamlessly inserted onto the global canvas.
We model the problem as jointly exploring the respective latent spaces such that the generated images can be combined, by inserting the parts from the specialized generators onto the global canvas, without introducing seams.
InsetGAN for Full-Body Image Generation