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. Source code is available at https://github.com/naver-ai/StyleMapGAN.
Original language | English |
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Title of host publication | Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 |
Publisher | IEEE Computer Society |
Pages | 852-861 |
Number of pages | 10 |
ISBN (Electronic) | 9781665445092 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 - Virtual, Online, United States Duration: 2021 Jun 19 → 2021 Jun 25 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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ISSN (Print) | 1063-6919 |
Conference
Conference | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 21/6/19 → 21/6/25 |
Bibliographical note
Funding Information:All experiments were conducted on NAVER Smart Machine Learning (NSML) [27, 46].
Publisher Copyright:
© 2021 IEEE
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition