Summary of Faithfill: Faithful Inpainting For Object Completion Using a Single Reference Image, by Rupayan Mallick et al.
FaithFill: Faithful Inpainting for Object Completion Using a Single Reference Image
by Rupayan Mallick, Amr Abdalla, Sarah Adel Bargal
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 research presents FaithFill, a novel approach for generating realistic object parts by filling in missing sections using only a single reference image. Unlike traditional methods that require multiple images, FaithFill uses a pipeline to generate multiple views of the object from a single input, allowing it to faithfully preserve shape, texture, color, and background while completing the missing parts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re looking at a picture of a cat, but some parts are missing. This paper shows how to fill in those gaps using only one image of the cat, without changing its original appearance. The method is called FaithFill, and it uses a special technique to generate multiple views of the object from just one reference image. This allows for very realistic results that preserve the original details. |
