用自回归模型生成更连贯的网格切割线，提升纹理映射质量。
Auto-Regressive Surface Cutting
- 将切割任务建模为序列预测，逐段生成量化3D坐标切边。
- 在含拓扑复杂与扫描模型的基准上达到领先性能。
- 适合需要高质量分割边界的设计与建模人员使用。
表面切割是计算机图形学中的基础任务，广泛应用于UV参数化、纹理映射和网格分解。然而，现有方法常产生技术上合法但过度碎片化的贴图域，缺乏语义一致性。我们提出SeamGPT，一种自回归模型，通过模仿专业工作流程生成切割缝。核心技术创新在于将表面切割建模为下一标记预测任务：在网格顶点和边上采样点云，编码为形状条件，并采用类似GPT的Transformer模型，依次预测具有量化3D坐标的缝合段。该方法在包含流形与非流形网格的UV展开基准上表现优异，涵盖艺术家创作与3D扫描模型。此外，还能增强现有3D分割工具，为部件分解提供清晰边界。
原文摘要 · Abstract (English)
Surface cutting is a fundamental task in computer graphics, with applications in UV parameterization, texture mapping, and mesh decomposition. However, existing methods often produce technically valid but overly fragmented atlases that lack semantic coherence. We introduce SeamGPT, an auto-regressive model that generates cutting seams by mimicking professional workflows. Our key technical innovation lies in formulating surface cutting as a next token prediction task: sample point clouds on mesh vertices and edges, encode them as shape conditions, and employ a GPT-style transformer to sequentially predict seam segments with quantized 3D coordinates. Our approach achieves exceptional performance on UV unwrapping benchmarks containing both manifold and non-manifold meshes, including artist-created, and 3D-scanned models. In addition, it enhances existing 3D segmentation tools by providing clean boundaries for part decomposition.
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