  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.
