Style Map GAN: Exploiting Spatial Dimensions of Latent in GAN for Real time Image Editing

The official demo of StyleMapGAN (CVPR21) We recommend watching the video at 1080p. The paper and code are available at the links below. Paper (arXiv): Paper (PDF): Code (GitHub): Authors: Hyunsu Kim Yunjey Choi Junho Kim Sungjoo Yoo Youngjung Uh Naver AI Lab Seoul National University Yonsei University Abstract: 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) timeconsuming optimization for projecting real images to the latent vectors, ii) or inaccurate embedding through an encoder. We propose StyleMapGAN: the intermediate latent space has spatial dimensions, and a spatially variant modulation replaces AdaIN. It makes the embedding through an encoder more accurate than existing optimizationbased met