Meta 3D TextureGen：面向3D对象的快速且一致的纹理生成
Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects
AI总结：
本文提出 Meta 3D TextureGen，通过两个顺序网络将3D语义注入文本到图像模型并融合为高分辨率UV纹理图，可在20秒内生成全局一致纹理，还支持任意比例增强至4K。
摘要：
近期，文本到图像模型的可用性与适应性在许多相关领域开启了一个新时代，这些领域受益于所学得的文本先验以及高质量、快速的生成能力，其中之一便是3D对象的纹理生成。尽管近期的纹理生成方法通过使用文本到图像网络取得了令人瞩目的结果，但全局一致性、质量与速度的结合对于推动纹理生成走向真实世界应用至关重要，却仍难以实现。为此，我们提出 Meta 3D TextureGen：这是一种新的前馈方法，由两个顺序网络组成，旨在在不到20秒内为任意复杂程度的任意几何结构生成高质量且全局一致的纹理。我们的方法通过在2D空间中以3D语义作为文本到图像模型的条件，并将其融合为完整的高分辨率UV纹理图，在质量和速度上取得了最先进结果；大量定性与定量评估证明了这一点。此外，我们还提出一个纹理增强网络，能够按任意比例放大任何纹理，生成4k像素分辨率的纹理。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
