Summary of Implicit Neural Representation Of Tileable Material Textures, by Hallison Paz et al.
Implicit Neural Representation of Tileable Material Textures
by Hallison Paz, Tiago Novello, Luiz Velho
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); 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 paper explores sinusoidal neural networks for representing periodic tileable textures, leveraging the Fourier series to initialize the first layer with integer frequencies. The approach ensures that the network learns a continuous representation of periodic patterns, allowing direct evaluation at any spatial coordinate without interpolation. A regularization term based on the Poisson equation is added to the loss function to enforce tileability. The proposed neural implicit representation is compact and enables efficient reconstruction of high-resolution textures with high visual fidelity and sharpness across multiple levels of detail. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special kinds of computer networks to make images look like natural patterns, like wood grain or stone texture. They use a clever way of representing these patterns using math called the Fourier series. This lets them create very detailed and realistic images that can be used in movies or video games. The new approach also helps remove blurry edges, making the pictures look even more real. |
Keywords
* Artificial intelligence * Loss function * Regularization
