QuadGPT: Native Quadrilateral Mesh Generation with Autoregressive Models

• URL: http://arxiv.org/abs/2509.21420v1
• Date: Thu, 25 Sep 2025 07:58:36 GMT
• Title: QuadGPT: Native Quadrilateral Mesh Generation with Autoregressive Models
• Abstract summary: We introduce QuadGPT, the first autoregressive framework for generating quadrilateral meshes in an end-to-end manner.<n>Our work establishes a new benchmark for native quad-mesh generation and showcases the power of combining large-scale autoregressive models with topology-aware RL refinement.
• Score: 34.888585681291794
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The generation of quadrilateral-dominant meshes is a cornerstone of professional 3D content creation. However, existing generative models generate quad meshes by first generating triangle meshes and then merging triangles into quadrilaterals with some specific rules, which typically produces quad meshes with poor topology. In this paper, we introduce QuadGPT, the first autoregressive framework for generating quadrilateral meshes in an end-to-end manner. QuadGPT formulates this as a sequence prediction paradigm, distinguished by two key innovations: a unified tokenization method to handle mixed topologies of triangles and quadrilaterals, and a specialized Reinforcement Learning fine-tuning method tDPO for better generation quality. Extensive experiments demonstrate that QuadGPT significantly surpasses previous triangle-to-quad conversion pipelines in both geometric accuracy and topological quality. Our work establishes a new benchmark for native quad-mesh generation and showcases the power of combining large-scale autoregressive models with topology-aware RL refinement for creating structured 3D assets.

Related papers

• Ground4D: Consistency-Aware 4D Reconstruction from Monocular Video [69.94904173314505]
  We propose Ground4D, a geometry-grounded framework built on two stages.<n>First, we reconstruct multi-view-consistent 3D geometry and camera poses from monocular video.<n>Second, we conduct geometry-consistency-aware refinement via dynamic Gaussian Splatting.
  arXiv  Detail & Related papers  (2026-06-27T09:27:42Z)
• TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields [72.76537377865192]
  TriFlow is a new generative approach for producing compact 3D meshes with artist-like triangle topology directly from input geometry conditions such as signed distance fields.<n>Experiments demonstrate that TriFlow achieves stronger generalization and significantly improved topology quality compared to state-of-the-art learning-based approaches.
  arXiv  Detail & Related papers  (2026-06-18T11:55:00Z)
• 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)
• QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning [46.16011060110813]
  The generation of production-ready quad-dominant meshes is a cornerstone of modern 3D content creation.<n>Existing methods are typically limited to producing either pure triangular meshes or pure quadrilateral meshes with isotropic densities.<n>We present QuadLink, a unified framework consisting of three stages for quad-dominant mesh generation by linking points into structured faces.
  arXiv  Detail & Related papers  (2026-05-16T05:04:10Z)
• SQuadGen: Generating Simple Quad Layouts via Chart Distance Fields [19.100132639513454]
  3D shapes from scanning, reconstruction, or AI-generated content often lack simple quad mesh layouts.<n>Existing quad-remeshing techniques produce complex layouts with irregular loops, leading to tedious manual cleanup.<n>We introduce SQuadGen, a diffusion-based generative framework that leverages Chart Distance Fields (CDF) to synthesize simple quad layouts on 3D shapes.
  arXiv  Detail & Related papers  (2026-04-30T02:15:50Z)
• HiDiGen: Hierarchical Diffusion for B-Rep Generation with Explicit Topological Constraints [11.721957326448162]
  Boundary representation (B-rep) is the standard 3D modeling format in CAD systems.<n>HiDiGen is a hierarchical generation framework that decouples geometry modeling into two stages.<n> Experimental results show that HiDiGen achieves strong performance, generating novel, diverse, and topologically sound CAD models.
  arXiv  Detail & Related papers  (2026-04-03T08:06:51Z)
• 4D Driving Scene Generation With Stereo Forcing [62.47705572424127]
  Current generative models struggle to synthesize dynamic 4D driving scenes that simultaneously support temporal extrapolation and spatial novel view synthesis (NVS) without per-scene optimization.<n>We present PhiGenesis, a unified framework for 4D scene generation that extends video generation techniques with geometric and temporal consistency.
  arXiv  Detail & Related papers  (2025-09-24T15:37:17Z)
• 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)
• Point2Quad: Generating Quad Meshes from Point Clouds via Face Prediction [8.692983641469612]
  We present Point2Quad, the first learning-based method for quad-only mesh generation from point clouds.<n>The key idea is learning to identify quad mesh with fused pointwise and facewise features.<n>Experiments on both clear and noise data demonstrate the effectiveness and superiority of Point2Quad.
  arXiv  Detail & Related papers  (2025-04-28T07:48:17Z)
• CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe Generation [11.447223770747051]
  CLR ContinuousWire encodes curves as Parametric Curves along with their Parametric Curves into a continuous and fixed latent space.<n>This unified approach generates both geometry and topology.
  arXiv  Detail & Related papers  (2025-04-27T09:32:42Z)
• DTGBrepGen: A Novel B-rep Generative Model through Decoupling Topology and Geometry [3.859930277034918]
  Boundary representation (B-rep) of geometric models is a fundamental format in Computer-Aided Design (CAD)<n>We propose DTGBrepGen, a novel topology-geometry decoupled framework for B-rep generation.
  arXiv  Detail & Related papers  (2025-03-17T12:34:14Z)
• CADDreamer: CAD Object Generation from Single-view Images [43.59340035126575]
  Existing 3D generative models often produce overly dense and unstructured meshes.<n>We introduce CADDreamer, a novel approach for generating boundary representations (B-rep) of CAD objects from a single image.<n>Results demonstrate that our method effectively recovers high-quality CAD objects from single-view images.
  arXiv  Detail & Related papers  (2025-02-28T05:30:29Z)
• NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generation [52.772319840580074]
  3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints. Existing methods often decompose 3D shapes into a sequence of localized components, treating each element in isolation. We introduce a novel spatial-aware 3D shape generation framework that leverages 2D plane representations for enhanced 3D shape modeling.
  arXiv  Detail & Related papers  (2024-03-27T04:09:34Z)
• 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)
• Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshes [52.038346313823524]
  This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology. Our method is able to produce high-quality meshes, particularly with diverse topologies, as compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-10T08:32:57Z)
• Learning Deformable Tetrahedral Meshes for 3D Reconstruction [78.0514377738632]
  3D shape representations that accommodate learning-based 3D reconstruction are an open problem in machine learning and computer graphics. Previous work on neural 3D reconstruction demonstrated benefits, but also limitations, of point cloud, voxel, surface mesh, and implicit function representations. We introduce Deformable Tetrahedral Meshes (DefTet) as a particular parameterization that utilizes volumetric tetrahedral meshes for the reconstruction problem.
  arXiv  Detail & Related papers  (2020-11-03T02:57:01Z)

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.