PartUV: Part-Based UV Unwrapping of 3D Meshes

2025-11-20

AI Summary

AI-generated meshes often exhibit noise, non-manifold topology, and geometric degeneracies, causing existing UV unwrapping methods to produce highly fragmented charts, excessive seams, and uncontrolled distortion. To address this, we propose the first semantic-aware UV unfolding framework that jointly integrates learning-driven part decomposition with geometry-informed heuristics. Our method employs PartField for top-down recursive part segmentation, followed by parameterization optimization and multi-chart packing—minimizing chart count under user-specified distortion constraints. It robustly handles non-manifold and degenerate inputs and achieves, for the first time, part-level semantic alignment alongside low-fragmentation unfolding. Evaluated on multiple benchmarks, our approach significantly outperforms both classical tools and neural methods: chart count is reduced by 32–57%, seam length by 41–63%, distortion is better controlled, and success rate on complex meshes reaches 98.5%.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Learning with Manifolds
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• UV unwrapping struggles with noisy AI-generated meshes
• Existing methods produce fragmented charts and suboptimal boundaries
• Current approaches introduce artifacts hindering downstream tasks

Innovation

• Part-based UV unwrapping with semantic decomposition
• Top-down recursive framework with geometric heuristics
• Integrated parameterization and packing with parallel processing