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arxiv: 2407.02430 · v1 · pith:V537DS36 · submitted 2024-07-02 · cs.CV · cs.AI· cs.GR· cs.LG

Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects

Raphael Bensadoun , Yanir Kleiman , Idan Azuri , Omri Harosh , Andrea Vedaldi , Natalia Neverova , Oran Gafni This is my paper

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classification cs.CV cs.AIcs.GRcs.LG
keywords texturegenerationtext-to-imagearbitraryconsistentfasthigh-qualityintroduce
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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.

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Cited by 6 Pith papers

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