


The authenticity is shocking! Google and Cornell University launch real-life image completion technology RealFill
Getting a great photo is getting easier and easier.
When traveling during the holidays, taking photos is indispensable. However, most of the photos taken in scenic spots are more or less regretful. Either there is something extra in the background, or there is something missing.
Obtaining a "perfect" image is one of the goals that CV researchers have long strived for. Recently, researchers from Google Research and Cornell University collaborated to propose an “Authentic Image Completion” technology—RealFill, a generative model for image completion.
The advantage of the RealFill model is that it can be personalized with a small number of scene reference images. These reference images do not need to be aligned with the target image, and can even be modified in terms of viewing angle, lighting conditions, camera aperture or image style. Big difference. Once personalization is complete, RealFill can complement the target image with visually engaging content in a way that is true to the original scene.
Please click the following link to view the paper: https://arxiv.org/abs/2309.16668
Project page link: https://realfill.github.io/

The output effect of RealFill model. Given a reference image on the left, RealFill can expand the corresponding target image on the right. The areas inside the white box are provided to the network as known pixels, while the areas outside the white box are generated. The results show that RealFill can generate high-quality images that are faithful to the reference image even if there are huge differences between the reference image and the target image, including viewpoint, aperture, lighting, image style, and object motion. Source: Paper
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