Quality (philosophy)

Control (management)

Linguistics

Philosophy

Locality

Literature

Archaeology

Data mining

Computer science

Simple (philosophy)

Semantics (computer science)

Natural language processing

Image (mathematics)

Style (visual arts)

Epistemology

Artificial intelligence

Morphing

Measure (data warehouse)

Information retrieval

Programming language

Human–computer interaction

Computer vision

History

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.

Editing in Style: Uncovering the Local Semantics of GANs