Summary of 3d-consistent Image Inpainting with Diffusion Models, by Leonid Antsfeld and Boris Chidlovskii
3D-Consistent Image Inpainting with Diffusion Models
by Leonid Antsfeld, Boris Chidlovskii
First submitted to arxiv on: 8 Dec 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 The proposed generative model uses image pairs from the same scene to achieve 3D-consistent and semantically coherent image inpainting. By incorporating an alternative point of view into the denoising process, the model creates an inductive bias that allows it to recover 3D priors without explicit 3D supervision. Training unconditional diffusion models with additional images as guidance harmonizes masked and non-masked regions while repainting and ensures consistency. The method outperforms state-of-the-art methods on one synthetic and three real-world datasets, generating semantically coherent and consistent inpaintings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a machine that can fill in missing parts of an image to make it look more realistic. This is called “image inpainting.” Normally, this process doesn’t work well when the missing parts are 3D objects, like people or buildings. A team of researchers developed a new way to do this using special computer models. They use two images taken from different angles of the same scene to make sure the filled-in parts look correct and realistic. This method is better than others at filling in missing parts and making the image look real. |
Keywords
» Artificial intelligence » Generative model » Image inpainting
