Summary of Paint by Inpaint: Learning to Add Image Objects By Removing Them First, By Navve Wasserman et al.
Paint by Inpaint: Learning to Add Image Objects by Removing Them First
by Navve Wasserman, Noam Rotstein, Roy Ganz, Ron Kimmel
First submitted to arxiv on: 28 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of seamlessly adding objects to images based on textual instructions without requiring user-provided input masks. By leveraging the insight that removing objects (inpainting) is significantly simpler than its inverse process of adding them, the authors curate a large-scale image dataset containing pairs of images and their corresponding object-removed versions. They train a diffusion model using this dataset to inverse the inpainting process, effectively adding objects into images. The model surpasses existing models in both object addition and general editing tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to add objects to pictures just by telling a computer what you want. This is what the researchers in this paper have achieved. They made a big dataset of images with and without certain objects, then used that data to train a special kind of AI model that can remove or add objects based on what you tell it. The results are impressive, showing that their approach works better than others at adding objects to pictures. You can find more information about the project, including the dataset and trained models, on the researchers’ website. |
Keywords
» Artificial intelligence » Diffusion model
