# Eye In-Painting with Exemplar Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1712.03999 · HTML: https://mlsanity.com/paper/1712.03999/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2017.

## Built on by (most cited first, 18 of 18)

- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [MyStyle: A Personalized Generative Prior](https://mlsanity.com/paper/2203.17272/) (2022)
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- [Adversarial Score Distillation: When score distillation meets GAN](https://mlsanity.com/paper/2312.00739/) (2023)
- [Personalized Face Inpainting with Diffusion Models by Parallel Visual Attention](https://mlsanity.com/paper/2312.03556/) (2023)
- [Reference-Guided Large-Scale Face Inpainting with Identity and Texture Control](https://mlsanity.com/paper/2303.07014/) (2023)
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- [Enhanced Generative Structure Prior for Chinese Text Image Super-resolution](https://mlsanity.com/paper/2508.07537/) (2025)
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- [Pluralistic Image Completion with Probabilistic Mixture-of-Experts](https://mlsanity.com/paper/2205.09086/) (2022)
- [Robust Text-driven Image Editing Method that Adaptively Explores Directions in Latent Spaces of StyleGAN and CLIP](https://mlsanity.com/paper/2304.00964/) (2023)
- [Combining Generative and Geometry Priors for Wide-Angle Portrait Correction](https://mlsanity.com/paper/2410.09911/) (2024)
- [Learning Contour-Guided 3D Face Reconstruction with Occlusions](https://mlsanity.com/paper/2503.12494/) (2025)

## Abstract (arXiv)

This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize exemplar information to produce high-quality, personalized in painting results. We propose using exemplar information in the form of a reference image of the region to in-paint, or a perceptual code describing that object. Unlike previous conditional GAN formulations, this extra information can be inserted at multiple points within the adversarial network, thus increasing its descriptive power. We show that ExGANs can produce photo-realistic personalized in-painting results that are both perceptually and semantically plausible by applying them to the task of closed to-open eye in-painting in natural pictures. A new benchmark dataset is also introduced for the task of eye in-painting for future comparisons.