UV unwrapping flattens 3D surfaces to 2D with minimal distortion, often requiring the complex surface to be decomposed into multiple charts. Although extensively studied, existing UV unwrapping methods frequently struggle with AI-generated meshes, which are typically noisy, bumpy, and poorly conditioned. These methods often produce highly fragmented charts and suboptimal boundaries, introducing artifacts and hindering downstream tasks. We introduce PartUV, a part-based UV unwrapping pipeline that generates significantly fewer, part-aligned charts while maintaining low distortion. Built on top of a recent learning-based part decomposition method PartField, PartUV combines high-level semantic part decomposition with novel geometric heuristics in a top-down recursive framework. It ensures each chart's distortion remains below a user-specified threshold while minimizing the total number of charts. The pipeline integrates and extends parameterization and packing algorithms, incorporates dedicated handling of non-manifold and degenerate meshes, and is extensively parallelized for efficiency. Evaluated across four diverse datasets, including man-made, CAD, AI-generated, and Common Shapes, PartUV outperforms existing tools and recent neural methods in chart count and seam length, achieves comparable distortion, exhibits high success rates on challenging meshes, and enables new applications like part-specific multi-tiles packing. Our project page is at https://www.zhaoningwang.com/PartUV.

翻译：UV展开旨在以最小失真将三维表面展平至二维平面，通常需要将复杂表面分解为多个贴图块。尽管该领域已得到广泛研究，但现有UV展开方法在处理AI生成的网格时仍面临诸多挑战，此类网格通常存在噪声、凹凸不平且条件较差。现有方法常产生高度碎片化的贴图块和次优边界，导致伪影并影响下游任务。本文提出PartUV，一种基于部件分割的UV展开流程，能够在保持低失真的同时生成数量显著减少且与部件对齐的贴图块。该方法基于近期基于学习的部件分割方法PartField构建，在自上而下的递归框架中结合了高层语义部件分割与新颖的几何启发式策略。该流程确保每个贴图块的失真度低于用户指定阈值，同时最小化贴图块总数。系统集成并扩展了参数化与排布算法，包含对非流形与退化网格的专门处理，并进行了大规模并行化以提高效率。在涵盖人造物体、CAD模型、AI生成网格及通用形状的四个多样化数据集上的评估表明，PartUV在贴图块数量与接缝长度方面优于现有工具及近期神经方法，达到可比的失真度，在挑战性网格上表现出高成功率，并支持部件级多贴图排布等新应用。项目页面位于https://www.zhaoningwang.com/PartUV。