  While the quality of GAN image synthesis has improved tremendously in recent
years, our ability to control and condition the output is still limited.
Focusing on StyleGAN, we introduce a simple and effective method for making
local, semantically-aware edits to a target output image. This is accomplished
by borrowing elements from a source image, also a GAN output, via a novel
manipulation of style vectors. Our method requires neither supervision from an
external model, nor involves complex spatial morphing operations. Instead, it
relies on the emergent disentanglement of semantic objects that is learned by
StyleGAN during its training. Semantic editing is demonstrated on GANs
producing human faces, indoor scenes, cats, and cars. We measure the locality
and photorealism of the edits produced by our method, and find that it
accomplishes both.
