The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Designing an encoder for StyleGAN image manipulation
Abstract
Recently, there has been a surge of diverse methods for performing image editing by employing pre-trained unconditional generators. Applying these methods on real images, however, remains a challenge, as it necessarily requires the inversion of the images into their latent space. To successfully invert a real image, one needs to find a latent code that reconstructs the input image accurately, and more importantly, allows for its meaningful manipulation. In this paper, we carefully study the latent space of StyleGAN, the state-of-the-art unconditional generator. We identify and analyze the existence of a distortion-editability tradeoff and a distortion-perception tradeoff within the StyleGAN latent space. We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on. We present an encoder based on our two principles that is specifically designed for facilitating editing on real images by balancing these tradeoffs. By evaluating its performance qualitatively and quantitatively on numerous challenging domains, including cars and horses, we show that our inversion method, followed by common editing techniques, achieves superior real-image editing quality, with only a small reconstruction accuracy drop.
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Cites
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.08500 · Cited by 8,365
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
Analyzing and Improving the Image Quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00813 · Cited by 6,332
3D Object Representations for Fine-Grained Categorization
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Improved Baselines with Momentum Contrastive Learning
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Proceedings of the 26th International Conference on Neural Information Processing Systems
Burges, C. J. C., Bottou, L., Welling, M., Zoubin Ghahramani, A., & Weinberger, K. Q. (2013) · Cited by 1,323
Generative Visual Manipulation on the Natural Image Manifold
Zhu, J.-Y., Krähenbühl, P., Shechtman, E., & Efros, A. A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_36 · Cited by 1,171
Cited by
Instructpix2pix: Learning to follow image editing instructions
Brooks, T., Holynski, A., & Efros, A. A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01764 · Cited by 1,466
Null-text Inversion for Editing Real Images using Guided Diffusion Models
Mokady, R., Hertz, A., Aberman, K., Pritch, Y., & Cohen-Or, D. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00585 · Cited by 747
Self-citationPlug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation
Tumanyan, N., Geyer, M., Bagon, S., & Dekel, T. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00191 · Cited by 609
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation
Kim, G., Kwon, T., & Ye, J. C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00246 · Cited by 570
StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets
Sauer, A., Schwarz, K., & Geiger, A. (2022) · Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings · doi:10.1145/3528233.3530738 · Cited by 353
Metadata
- Authors
- Tov, O., Alaluf, Y., Nitzan, Y., Patashnik, O., & Cohen-Or, D.
- Venue
- ACM Transactions on Graphics
- Published
- 2021-07-19
- Volume
- 40
- Issue
- 4
- Pages
- 1-14
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W3178406257
- Cited by
- 686
- References
- 27
- FWCI
- 45.68
- Type
- journal article
- Language
- en
- Open access
- closed
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