StyleMapGAN: Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing

CVPR 2021

StyleMapGAN: Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing

Apr 25, 2021
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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) time-consuming 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 optimization-based methods while maintaining the properties of GANs. Experimental results demonstrate that our method significantly outperforms state-of-the-art models in various image manipulation tasks such as local editing and image interpolation. Last but not least, conventional editing methods on GANs are still valid on our StyleMapGAN. Authors: Hyunsu Kim, Yunjey Choi, Junho Kim, Sungjoo Yoo, Youngjung Uh (NAVER AI Lab, Seoul National University, Yonsei University)

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