MeshTailor: Cutting Seams via Generative Mesh Traversal

Abstract

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.

Figures (21)

• Figure 1: MeshTailor.Top: MeshTailor generates seams (colored lines) directly on 3D meshes, producing clean, semantically aligned cuts that respect natural shape structure. Bottom: The resulting seams partition surfaces into coherent UV charts, which are flattened into 2D layouts with minimal fragmentation. Right: These high-quality UV maps facilitate seamless texture application, as demonstrated by the final textured character model.
• Figure 2: Overview of MeshTailor.Left: The dual-stream encoder. The input mesh is processed in parallel: the top stream extracts topological connectivity features $\mathcal{H}$ via a Graph Encoder on the mesh topology $\{V, E\}$, while the bottom stream samples surface points to extract global shape semantics tokens $Z$ using a pretrained point-cloud encoder (frozen during training). These representations are fused via cross-attention within Transformer Blocks. Right: The autoregressive decoder. At each step, the MeshTailor Transformer conditions on the previously generated sequence ("Seq") to produce a decoder query. A pointer layer attends to the enhanced vertex embeddings $\tilde{\mathcal{H}}$ to select the next vertex (green box), which is appended to the sequence. The resulting seam chains partition the mesh into UV charts, visualized here with checkerboard texturing to show low distortion, color-coded charts, and the final 2D UV layout.
• Figure 3: Canonical ordering of seam chains (coarse-to-fine). We serialize an unordered seam set into a deterministic sequence for autoregressive training/inference with a loops-first, balance-first, large-patch-first strategy: we prioritize loop cuts over open chains, repeatedly select the largest remaining surface patch, and within that patch choose the loop cut that best balances the two resulting sub-patch areas (see Supplemental Material \ref{['sec:supp_ordering']} for details). The example shows how primary loop cuts (e.g., around the neck) are placed first to decompose the shape, followed by finer loops on smaller parts (legs, tail, ears), yielding the final ordered chain list and token sequence $\boldsymbol{\tau}$.
• Figure 4: Step-by-step seam generation as mesh traversal. Our autoregressive decoder traverses the input mesh connectivity, selecting the next vertex from the current 1-ring neighborhood. This local constraint makes the predicted seams mesh-native and avoids invalid jumps across the surface.
• Figure 5: Seam layout and area distortion comparison on GarmentCodeData GarmentCodeData. For each method, we show the predicted seams on the 3D mesh (left) and the corresponding area distortion heatmap on the UV layout (right). While prior methods often produce fragmented or jagged cuts that lead to irregular UV islands, MeshTailor generates cleaner, garment-aligned seam structures with coherent chains and loops, resulting in more regular, compact UV charts while maintaining competitive distortion. Different colors in our seam visualizations indicate distinct seam chains (and loop cuts), highlighting the structured and editable output of our representation.