Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910510179418112 |
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| author | Bensadoun, Raphael Kleiman, Yanir Azuri, Idan Harosh, Omri Vedaldi, Andrea Neverova, Natalia Gafni, Oran |
| author_facet | Bensadoun, Raphael Kleiman, Yanir Azuri, Idan Harosh, Omri Vedaldi, Andrea Neverova, Natalia Gafni, Oran |
| contents | The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and fast generation capabilities, one of which is texture generation for 3D objects. Although recent texture generation methods achieve impressive results by using text-to-image networks, the combination of global consistency, quality, and speed, which is crucial for advancing texture generation to real-world applications, remains elusive. To that end, we introduce Meta 3D TextureGen: a new feedforward method comprised of two sequential networks aimed at generating high-quality and globally consistent textures for arbitrary geometries of any complexity degree in less than 20 seconds. Our method achieves state-of-the-art results in quality and speed by conditioning a text-to-image model on 3D semantics in 2D space and fusing them into a complete and high-resolution UV texture map, as demonstrated by extensive qualitative and quantitative evaluations. In addition, we introduce a texture enhancement network that is capable of up-scaling any texture by an arbitrary ratio, producing 4k pixel resolution textures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02430 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects Bensadoun, Raphael Kleiman, Yanir Azuri, Idan Harosh, Omri Vedaldi, Andrea Neverova, Natalia Gafni, Oran Computer Vision and Pattern Recognition Artificial Intelligence Graphics Machine Learning The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and fast generation capabilities, one of which is texture generation for 3D objects. Although recent texture generation methods achieve impressive results by using text-to-image networks, the combination of global consistency, quality, and speed, which is crucial for advancing texture generation to real-world applications, remains elusive. To that end, we introduce Meta 3D TextureGen: a new feedforward method comprised of two sequential networks aimed at generating high-quality and globally consistent textures for arbitrary geometries of any complexity degree in less than 20 seconds. Our method achieves state-of-the-art results in quality and speed by conditioning a text-to-image model on 3D semantics in 2D space and fusing them into a complete and high-resolution UV texture map, as demonstrated by extensive qualitative and quantitative evaluations. In addition, we introduce a texture enhancement network that is capable of up-scaling any texture by an arbitrary ratio, producing 4k pixel resolution textures. |
| title | Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics Machine Learning |
| url | https://arxiv.org/abs/2407.02430 |