# Guided Image Inpainting: Replacing an Image Region by Pulling Content from Another Image

arXiv: https://arxiv.org/abs/1803.08435 · HTML: https://mlsanity.com/paper/1803.08435/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2018.

## Built on by (most cited first, 5 of 5)

- [TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations](https://mlsanity.com/paper/2103.15982/) (2021)
- [Deep Image Matting: A Comprehensive Survey](https://mlsanity.com/paper/2304.04672/) (2023)
- [Shape-guided Object Inpainting](https://mlsanity.com/paper/2204.07845/) (2022)
- [FACEMUG: A Multimodal Generative and Fusion Framework for Local Facial Editing](https://mlsanity.com/paper/2412.19009/) (2024)
- [Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars](https://mlsanity.com/paper/2202.06358/) (2022)

## Abstract (arXiv)

Deep generative models have shown success in automatically synthesizing missing image regions using surrounding context. However, users cannot directly decide what content to synthesize with such approaches. We propose an end-to-end network for image inpainting that uses a different image to guide the synthesis of new content to fill the hole. A key challenge addressed by our approach is synthesizing new content in regions where the guidance image and the context of the original image are inconsistent. We conduct four studies that demonstrate our results yield more realistic image inpainting results over seven baselines.