  While GANs can produce photo-realistic images in ideal conditions for certain
domains, the generation of full-body human images remains difficult due to the
diversity of identities, hairstyles, clothing, and the variance in pose.
Instead of modeling this complex domain with a single GAN, 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, shoes) 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. We demonstrate the setup by combining a full body GAN with a dedicated
high-quality face GAN to produce plausible-looking humans. We evaluate our
results with quantitative metrics and user studies.
