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 orders chains from global structural cuts down to local details in a coarse-to-fine manner, and a dual-stream encoder that fuses topological and geometric context. Leveraging this hierarchical representation and dual-stream vertex embeddings, our MeshTailor Transformer utilizes an autoregressive pointer layer to trace seams vertex-by-vertex within local neighborhoods. Extensive evaluations show that MeshTailor produces more coherent and structurally regular 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，用于在三维曲面合成边缘对齐的缝线。与现有基于优化或外在学习的方法不同，MeshTailor直接在网格图上操作，消除了投影伪影和脆弱的吸附启发式规则。我们引入ChainingSeams——一种缝线图的分层序列化方法，以从粗到细的方式将链从全局结构切割排序到局部细节，并设计融合拓扑与几何上下文的双流编码器。基于这一分层表示和双流顶点嵌入，我们的MeshTailor Transformer利用自回归指针层在局部邻域内逐顶点追踪缝线。大量评估表明，与近期基于优化和学习的基线方法相比，MeshTailor生成的缝线布局更具连贯性和结构规整性。