Guided Image Inpainting: Replacing an Image Region by Pulling Content from Another Image
This work enables users to directly decide content for image inpainting, offering a domain-specific improvement over automated methods.
The paper tackles the problem of user-guided image inpainting by proposing an end-to-end network that uses a different image to guide content synthesis, addressing inconsistencies between guidance and context. The results show more realistic inpainting outcomes compared to seven baselines across four studies.
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.