Editing in Style: Uncovering the Local Semantics of GANs
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.
Code (2)
Tasks
DisentanglementImage GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing
Recent studies have shown that StyleGANs provide promising prior models for downstream tasks on image synthesis and editing. However, since the latent codes of StyleGANs are designed to control global styles, it is hard …
DisentanglementFacial EditingImage GenerationTransfer LearningSemantic StyleGAN
SemanticStyleGAN presents a method where a generator is trained to model local semantic parts separately and synthesizes images in a compositional way. Experimental results demonstrate that Semantic StyleGAN model pro…
DisentanglementExploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing
Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time…
Image ManipulationvalidA StyleMap-Based Generator for Real-Time Image Projection and Local Editing
Generative adversarial networks (GANs) have been successful in synthesizing and manipulating synthetic but realistic images from latent vectors. However, it is still challenging for GANs to manipulate real images, especi…
Image ManipulationLatents2Semantics: Leveraging the Latent Space of Generative Models for Localized Style Manipulation of Face Images
With the metaverse slowly becoming a reality and given the rapid pace of developments toward the creation of digital humans, the need for a principled style editing pipeline for human faces is bound to increase manifold.…