GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model so that the image can be faithfully reconstructed from the inverted code by the generator. As an emerging technique to bridge the real and fake image domains, GAN inversion plays an essential role in enabling pretrained GAN models, such as StyleGAN and BigGAN, for applications of real image editing. Moreover, GAN inversion interprets GAN's latent space and examines how realistic images can be generated. In this paper, we provide a survey of GAN inversion with a focus on its representative algorithms and its applications in image restoration and image manipulation. We further discuss the trends and challenges for future research. A curated list of GAN inversion methods, datasets, and other related information can be found at https://github.com/weihaox/awesome-gan-inversion.
|Number of pages||18|
|Journal||IEEE transactions on pattern analysis and machine intelligence|
|Publication status||Published - 2023 Mar 1|
Bibliographical noteFunding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61991450 and in part by the Shenzhen Key Laboratory of Marine IntelliSense and Computation under Grant ZDSYS20200811142605016. The work ofMing-Hsuan Yang was supported in part by National Science Foundation CAREER under Grant 1149783.
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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics