MeshTailor: Cutting Seams via Generative Mesh Traversal
- URL: http://arxiv.org/abs/2603.27309v1
- Date: Sat, 28 Mar 2026 15:30:24 GMT
- Title: MeshTailor: Cutting Seams via Generative Mesh Traversal
- Abstract summary: We present MeshTailor, the first mesh-native generative framework for synthesizing edge-aligned seams on 3D surfaces.<n>Unlike prior optimization-based or extrinsic learning-based methods, MeshTailor operates directly on the mesh graph, eliminating projection artifacts and fragile snappings.
- Score: 11.174459652257218
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
Related papers
- Precise Top-Layer Fabric Segmentation for Fabric Destacking with Edge- and Shape-Aware Deep Networks [8.266820805797826]
We propose a novel segmentation training architecture tailored for top-layer fabric segmentation in stacked fabrics.<n>The method extends the classical encoder-decoder framework by introducing two specialized branches.<n> Experiments on a real-world fabric dataset demonstrate that the training approach outperforms established baselines.
arXiv Detail & Related papers (2026-08-11T08:32:28Z) - Meshy T2: Fast Native Mesh Generation with Flow Matching [8.108033268825832]
We present Meshy T2, a fast native mesh generation framework built on flow matching.<n>In experiments, Meshy T2 achieves state-of-the-art geometric fidelity and completes end-to-end image-to-mesh generation within a median of 6 seconds.
arXiv Detail & Related papers (2026-07-28T16:33:54Z) - SeamGen: Artist-Aligned UV Seam Generation via Graph Flow Matching [47.43404509262908]
SeamGen is a generative model for UV seam generation that aligns with artist preferences and production requirements.<n>By learning priors from professional seam layout data, SeamGen produces UV layouts that better align with artist-authored preferences.
arXiv Detail & Related papers (2026-07-14T05:52:52Z) - MeshWeaver: Sparse-Voxel-Guided Surface Weaving for Autoregressive Mesh Generation [53.06620176910962]
MeshWeaver is an autoregressive framework that treats mesh generation as a surface weaving process.<n>MeshWeaver achieves a state-of-the-art compression ratio of 18%, can generate meshes with up to 16K faces, and significantly improves geometric fidelity over prior approaches.
arXiv Detail & Related papers (2026-06-03T10:15:43Z) - LATO: 3D Mesh Flow Matching with Structured TOpology Preserving LAtents [43.84310366771089]
LATO is a novel topology-preserving latent representation that enables scalable, flow matching-based synthesis of explicit 3D meshes.<n>For generative modeling, LATO adopts a two-stage flow matching process, first synthesizing the structure voxels and subsequently refining the voxel-wise topology features.<n>Compared to prior isosurface/triangle-based diffusion models and autoregressive generation approaches, LATO generates meshes with complex geometry, well-formed topology while being highly efficient in inference.
arXiv Detail & Related papers (2026-03-06T15:09:25Z) - OMeGa: Joint Optimization of Explicit Meshes and Gaussian Splats for Robust Scene-Level Surface Reconstruction [10.746979229164815]
OMeGa is an end-to-end framework that jointly optimize an explicit triangle mesh and 2D Gaussian splats.<n>OMeGa achieves state-of-the-art performance on challenging indoor reconstruction benchmarks.
arXiv Detail & Related papers (2025-09-29T05:43:40Z) - TetWeave: Isosurface Extraction using On-The-Fly Delaunay Tetrahedral Grids for Gradient-Based Mesh Optimization [59.318328774645835]
We introduce TetWeave, a novel isosurface representation for gradient-based mesh optimization.<n>TetWeave constructs tetrahedral grids on-the-fly via Delaunay triangulation.<n>We demonstrate the applicability of TetWeave to a broad range of challenging tasks in computer graphics and vision.
arXiv Detail & Related papers (2025-05-07T17:32:49Z) - MeshPad: Interactive Sketch-Conditioned Artist-Reminiscent Mesh Generation and Editing [64.84885028248395]
MeshPad is a generative approach that creates 3D meshes from sketch inputs.<n>We focus on enabling consistent edits by decomposing editing into 'deletion' of regions of a mesh, followed by 'addition' of new mesh geometry.<n>Our approach is based on a triangle sequence-based mesh representation, exploiting a large Transformer model for mesh triangle addition and deletion.
arXiv Detail & Related papers (2025-03-03T11:27:44Z) - SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes [61.110517195874074]
We present a scheme to directly generate manifold, polygonal meshes of complex connectivity as the output of a neural network.<n>Our key innovation is to define a continuous latent connectivity space at each mesh, which implies the discrete mesh.<n>In applications, this approach not only yields high-quality outputs from generative models, but also enables directly learning challenging geometry processing tasks such as mesh repair.
arXiv Detail & Related papers (2024-09-30T17:59:03Z) - MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers [32.169007676811404]
MeshGPT is a new approach for generating triangle meshes that reflects the compactness typical of artist-created meshes.
Inspired by recent advances in powerful large language models, we adopt a sequence-based approach to autoregressively generate triangle meshes as triangles.
arXiv Detail & Related papers (2023-11-27T01:20:11Z) - Flexible Isosurface Extraction for Gradient-Based Mesh Optimization [65.76362454554754]
This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field.
We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives.
arXiv Detail & Related papers (2023-08-10T06:40:19Z) - Mesh Draping: Parametrization-Free Neural Mesh Transfer [92.55503085245304]
Mesh Draping is a neural method for transferring existing mesh structure from one shape to another.
We show that by leveraging gradually increasing frequencies to guide the neural optimization, we are able to achieve stable and high quality mesh transfer.
arXiv Detail & Related papers (2021-10-11T17:24:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.