Summary of Making Images From Images: Interleaving Denoising and Transformation, by Shumeet Baluja et al.
Making Images from Images: Interleaving Denoising and Transformation
by Shumeet Baluja, David Marwood, Ashwin Baluja
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
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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 paper presents a novel method for generating optical illusions by rearranging image regions. The approach learns parameterized transformations between desired images, allowing any source image to be transformed into a new subject matter. The method formulates this process as a constrained optimization problem and solves it through interleaving image diffusion with energy minimization. The authors demonstrate their approach in both pixel and latent spaces and show that increasing the number of regions makes the problem easier and improves results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to change an old picture into a new one just by rearranging its parts. This is done by learning rules for changing one image into another. Any existing picture can be changed into a new subject, like turning the Mona Lisa into a cat. The method uses math and computers to solve a special problem that makes it easy to get good results when using many regions. |
Keywords
» Artificial intelligence » Diffusion » Optimization
