Recent 3D generative models produce high-quality textures for 3D mesh objects. However, they commonly rely on the heavy assumption that input 3D meshes are accompanied by manual mesh parameterization (UV mapping), a manual task that requires both technical precision and artistic judgment. Industry surveys show that this process often accounts for a significant share of asset creation, creating a major bottleneck for 3D content creators. Moreover, existing automatic methods often ignore two perceptually important criteria: (1) semantic awareness (UV charts should align semantically similar 3D parts across shapes) and (2) visibility awareness (cutting seams should lie in regions unlikely to be seen). To overcome these shortcomings and to automate the mesh parameterization process, we present an unsupervised differentiable framework that augments standard geometry-preserving UV learning with semantic- and visibility-aware objectives. For semantic-awareness, our pipeline (i) segments the mesh into semantic 3D parts, (ii) applies an unsupervised learned per-part UV-parameterization backbone, and (iii) aggregates per-part charts into a unified UV atlas. For visibility-awareness, we use ambient occlusion (AO) as an exposure proxy and back-propagate a soft differentiable AO-weighted seam objective to steer cutting seams toward occluded regions. By conducting qualitative and quantitative evaluations against state-of-the-art methods, we show that the proposed method produces UV atlases that better support texture generation and reduce perceptible seam artifacts compared to recent baselines. Our implementation code is publicly available at: https://github.com/AHHHZ975/Semantic-Visibility-UV-Param.

翻译：近期三维生成模型能够为三维网格对象生成高质量纹理。然而，这些模型通常严重依赖于输入三维网格附带人工网格参数化（UV映射）这一强假设，而该人工任务既需要技术精度又依赖艺术判断。行业调查表明，该流程通常占据资产创建工作的相当大部分，成为三维内容创作者的主要瓶颈。此外，现有自动化方法往往忽略两个感知上重要的标准：（1）语义感知（UV图表应在不同形状间对齐语义相似的三维部件）；（2）可见性感知（切割接缝应位于不易被观察到的区域）。为克服这些缺陷并实现网格参数化流程的自动化，我们提出一种无监督可微分框架，该框架通过语义与可见性感知目标增强了标准几何保持型UV学习。针对语义感知，我们的流程（i）将网格分割为语义三维部件，（ii）应用无监督学习的按部件UV参数化主干网络，（iii）将按部件图表聚合为统一UV图集。针对可见性感知，我们使用环境光遮蔽作为曝光代理，并通过反向传播软可微分AO加权接缝目标，引导切割接缝朝向遮蔽区域。通过对最先进方法进行定性与定量评估，我们证明所提方法生成的UV图集相比近期基线方法能更好地支持纹理生成并减少可感知的接缝伪影。我们的实现代码已公开于：https://github.com/AHHHZ975/Semantic-Visibility-UV-Param。