Mesh Silksong: Auto-Regressive Mesh Generation as Weaving Silk
- URL: http://arxiv.org/abs/2507.02477v2
- Date: Fri, 04 Jul 2025 13:59:35 GMT
- Title: Mesh Silksong: Auto-Regressive Mesh Generation as Weaving Silk
- Authors: Gaochao Song, Zibo Zhao, Haohan Weng, Jingbo Zeng, Rongfei Jia, Shenghua Gao,
- Abstract summary: Mesh Silksong is a compact and efficient mesh representation tailored to generate the polygon mesh in an auto-regressive manner akin to silk weaving.<n>We show that Mesh Silksong produces polygon meshes with superior geometric properties, including manifold topology, watertight detection, and consistent face normals.
- Score: 25.158463603708245
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce Mesh Silksong, a compact and efficient mesh representation tailored to generate the polygon mesh in an auto-regressive manner akin to silk weaving. Existing mesh tokenization methods always produce token sequences with repeated vertex tokens, wasting the network capability. Therefore, our approach tokenizes mesh vertices by accessing each mesh vertice only once, reduces the token sequence's redundancy by 50\%, and achieves a state-of-the-art compression rate of approximately 22\%. Furthermore, Mesh Silksong produces polygon meshes with superior geometric properties, including manifold topology, watertight detection, and consistent face normals, which are critical for practical applications. Experimental results demonstrate the effectiveness of our approach, showcasing not only intricate mesh generation but also significantly improved geometric integrity.
Related papers
- 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) - TreeMeshGPT: Artistic Mesh Generation with Autoregressive Tree Sequencing [47.919057306538626]
TreeMeshGPT is an autoregressive Transformer designed to generate artistic meshes aligned with input point clouds.<n>Our approach represents each triangular face with two tokens, achieving a compression rate of approximately 22%.<n>Our method generates mesh with strong normal orientation constraints, minimizing flipped normals commonly encountered in previous methods.
arXiv Detail & Related papers (2025-03-14T17:48:06Z) - MeshPad: Interactive Sketch-Conditioned Artist-Designed 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) - Scaling Mesh Generation via Compressive Tokenization [66.05639158028343]
We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT)
BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, reducing their length by approximately 75% compared to the original sequences.
Empowered with the BPT, we have built a foundation mesh generative model training on scaled mesh data to support flexible control for point clouds and images.
arXiv Detail & Related papers (2024-11-11T14:30:35Z) - 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) - Learning Self-Prior for Mesh Inpainting Using Self-Supervised Graph Convolutional Networks [4.424836140281846]
We present a self-prior-based mesh inpainting framework that requires only an incomplete mesh as input.
Our method maintains the polygonal mesh format throughout the inpainting process.
We demonstrate that our method outperforms traditional dataset-independent approaches.
arXiv Detail & Related papers (2023-05-01T02:51:38Z) - 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) - Primal-Dual Mesh Convolutional Neural Networks [62.165239866312334]
We propose a primal-dual framework drawn from the graph-neural-network literature to triangle meshes.
Our method takes features for both edges and faces of a 3D mesh as input and dynamically aggregates them.
We provide theoretical insights of our approach using tools from the mesh-simplification literature.
arXiv Detail & Related papers (2020-10-23T14:49:02Z)
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.