We present MeshTailor, the first mesh-native generative framework for synthesizing edge-aligned seams on 3D surfaces. Unlike prior optimization-based or extrinsic learning-based methods, MeshTailor operates directly on the mesh graph, eliminating projection artifacts and fragile snapping heuristics. We introduce ChainingSeams, a hierarchical serialization of the seam graph that prioritizes global structural cuts before local details in a coarse-to-fine manner, and a dual-stream encoder that fuses topological and geometric context. Leveraging this hierarchical representation and enriched vertex embeddings, our MeshTailor Transformer utilizes an autoregressive pointer layer to trace seams vertex-by-vertex within local neighborhoods, ensuring projection-free, edge-aligned seams. Extensive evaluations show that MeshTailor produces more coherent, professional-quality seam layouts compared to recent optimization-based and learning-based baselines.

翻译：我们提出MeshTailor，首个原生网格生成框架，用于在三维表面上合成边缘对齐的接缝。与先前基于优化或基于外部分类学习的方法不同，MeshTailor直接在网格图上操作，消除了投影伪影和脆弱的快照启发式方法。我们引入ChainingSeams，一种接缝图的层次化序列化方法，采用从粗到细的方式优先关注全局结构切割，再处理局部细节；以及一个融合拓扑与几何上下文的双流编码器。凭借这种层次化表示和增强的顶点嵌入，我们的MeshTailor Transformer利用自回归指针层在局部邻域内逐顶点追踪接缝，确保无投影、边缘对齐的结果。大量评估表明，与近期基于优化和基于学习的基线方法相比，MeshTailor能生成更连贯、专业品质的接缝布局。