Summary of Meta 3d Texturegen: Fast and Consistent Texture Generation For 3d Objects, by Raphael Bensadoun et al.
Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects
by Raphael Bensadoun, Yanir Kleiman, Idan Azuri, Omri Harosh, Andrea Vedaldi, Natalia Neverova, Oran Gafni
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces Meta 3D TextureGen, a feedforward method for generating high-quality and globally consistent textures for arbitrary geometries in less than 20 seconds. The approach combines text-to-image networks with 3D semantics in 2D space to produce UV texture maps. The method achieves state-of-the-art results in quality and speed, as demonstrated by extensive qualitative and quantitative evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called AI models to create pictures of textures for 3D objects. Right now, these models are really good at making realistic images from words, but they’re not very good at creating patterns on 3D shapes. The scientists in this study want to make a new tool that can do both things well and quickly. They came up with an idea called Meta 3D TextureGen, which uses two special networks to create textures that are both realistic and detailed. This means they can create pictures of textures for any shape, no matter how complicated. |
Keywords
* Artificial intelligence * Semantics
