MeshTailor: Cutting Seams via Generative Mesh Traversal
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914581189754880 |
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| author | Ma, Xueqi Yan, Xingguang Zhang, Congyue Huang, Hui |
| author_facet | Ma, Xueqi Yan, Xingguang Zhang, Congyue Huang, Hui |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_27309 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MeshTailor: Cutting Seams via Generative Mesh Traversal Ma, Xueqi Yan, Xingguang Zhang, Congyue Huang, Hui Graphics Computer Vision and Pattern Recognition 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. |
| title | MeshTailor: Cutting Seams via Generative Mesh Traversal |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.27309 |