MV2UV：利用多视图提示生成高质量 UV 纹理贴图
MV2UV: Generating High-quality UV Texture Maps with Multiview Prompts
把多视图生成的 2D 扩散先验与 UV 空间修复结合，用 UV 空间生成模型同时补全多视图未见区域并消解视图间不一致，为三维资产纹理生成提供了可复用的「多视图提示 + UV 精修」范式。
原文依据 adopt a UV space generative model that simultaneously inpaints unseen parts of multiview images while resolving the inconsistency
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论为三维资产生成高质量纹理是一项具有挑战性的任务。现有的多视图纹理生成方法存在多视图不一致以及未见部分纹理缺失的问题，而 UV 图像修复类纹理方法由于 UV 数据不足而泛化性较差，且无法很好地利用二维图像扩散先验。本文提出一种名为 MV2UV 的新方法，将多视图生成提供的二维生成先验与 UV 精修所具备的修复能力相结合，以获得高质量纹理贴图。其核心思想是采用一个 UV 空间生成模型，在修复多视图图像中未见部分的同时消解多视图图像的不一致性。实验表明，本方法相比现有方法能够取得更好的纹理生成质量，尤其是在被遮挡的未见部分以及多视图不一致的部分。
英文原文摘要 856 字符
Generating high-quality textures for 3D assets is a challenging task. Existing multiview texture generation methods suffer from the multiview inconsistency and missing textures on unseen parts, while UV inpainting texture methods do not generalize well due to insufficient UV data and cannot well utilize 2D image diffusion priors. In this paper, we propose a new method called MV2UV that combines 2D generative priors from multiview generation and the inpainting ability of UV refinement to get high-quality texture maps. Our key idea is to adopt a UV space generative model that simultaneously inpaints unseen parts of multiview images while resolving the inconsistency of multiview images. Experiments show that our method enables a better texture generation quality than existing methods, especially in unseen occluded and multiview-inconsistent parts.