mlsanityData to 7 Oct 2026

MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers

arXiv 2406.10163 ↗First posted 14 Jun 2024cs.CV cs.AIcited by 140

Not on the map. Why?

Cited by 27 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement. However, this potential is largely unrealized because these assets always need to be converted to meshes for 3D industry applications, and the meshes produced by current mesh extraction methods are significantly inferior to Artist-Created Meshes (AMs), i.e., meshes created by human artists. Specifically, current mesh extraction methods rely on dense faces and ignore geometric features, leading to inefficiencies, complicated post-processing, and lower representation quality. To address these issues, we introduce MeshAnything, a model that treats mesh extraction as a generation problem, producing AMs aligned with specified shapes. By converting 3D assets in any 3D representation into AMs, MeshAnything can be integrated with various 3D asset production methods, thereby enhancing their application across the 3D industry. The architecture of MeshAnything comprises a VQ-VAE and a shape-conditioned decoder-only transformer. We first learn a mesh vocabulary using the VQ-VAE, then train the shape-conditioned decoder-only transformer on this vocabulary for shape-conditioned autoregressive mesh generation. Our extensive experiments show that our method generates AMs with hundreds of times fewer faces, significantly improving storage, rendering, and simulation efficiencies, while achieving precision comparable to previous methods.

Key facts. Cited by 140 papers in the map (data through 7 Oct 2026). Builds on 59 of its 75 references that are in the map. First posted on arXiv 14 Jun 2024.

Builds on

59 of 75 references.

References · 59

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38 more
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  15. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 555
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  18. 2017Neural 3D Mesh Renderercited by 437
  19. 2024InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Modelscited by 421
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50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 20; 2025: 71; 2026: 49.

140 papers.

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  1. 2024Structured 3D Latents for Scalable and Versatile 3D Generationcited by 742
  2. 2025Native and Compact Structured Latents for 3D Generationcited by 157
  3. 2024MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenizationcited by 102
  4. 2024LLaMA-Mesh: Unifying 3D Mesh Generation with Language Modelscited by 87
  5. 2024Meshtron: High-Fidelity, Artist-Like 3D Mesh Generation at Scalecited by 87
  6. 2025Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assetscited by 85
  7. 2024EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generationcited by 84
  8. 2025Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modelingcited by 84
  9. 2025PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformerscited by 78
  10. 2024Scaling Mesh Generation via Compressive Tokenizationcited by 71
  11. 2025DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learningcited by 62
  12. 2025Amodal3R: Amodal 3D Reconstruction from Occluded 2D Imagescited by 52
38 more
  1. 2025Efficient Part-level 3D Object Generation via Dual Volume Packingcited by 47
  2. 2025MagicArticulate: Make Your 3D Models Articulation-Readycited by 43
  3. 2025Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attentioncited by 43
  4. 2025Puppeteer: Rig and Animate Your 3D Modelscited by 41
  5. 2024DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusioncited by 38
  6. 2024SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAEcited by 38
  7. 2025TreeMeshGPT: Artistic Mesh Generation with Autoregressive Tree Sequencingcited by 38
  8. 2024An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusioncited by 34
  9. 2024MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3Dcited by 34
  10. 2025ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understandingcited by 34
  11. 2025PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Imagecited by 32
  12. 2025OctGPT: Octree-based Multiscale Autoregressive Models for 3D Shape Generationcited by 27
  13. 2025Generating Physically Stable and Buildable Brick Structures from Textcited by 23
  14. 2025Advances in Feed-Forward 3D Reconstruction and View Synthesis: A Surveycited by 23
  15. 2025CADDreamer: CAD Object Generation from Single-view Imagescited by 22
  16. 2025Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuningcited by 21
  17. 2024SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshescited by 20
  18. 2025Nautilus: Locality-aware Autoencoder for Scalable Mesh Generationcited by 18
  19. 2025GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photographycited by 18
  20. 2025MeshLLM: Empowering Large Language Models to Progressively Understand and Generate 3D Meshcited by 18
  21. 2025LL3M: Large Language 3D Modelerscited by 18
  22. 2025FastMesh: Efficient Artistic Mesh Generation via Component Decouplingcited by 17
  23. 2024LaGeM: A Large Geometry Model for 3D Representation Learning and Diffusioncited by 16
  24. 2024TAR3D: Creating High-Quality 3D Assets via Next-Part Predictioncited by 16
  25. 2025Sat2City: 3D City Generation from A Single Satellite Image with Cascaded Latent Diffusioncited by 15
  26. 2025LLM/Agent-as-Data-Analyst: A Surveycited by 15
  27. 2025AR-1-to-3: Single Image to Consistent 3D Object Generation via Next-View Predictioncited by 14
  28. 2025MeshCraft: Exploring Efficient and Controllable Mesh Generation with Flow-based DiTscited by 14
  29. 2025TSGS: Improving Gaussian Splatting for Transparent Surface Reconstruction via Normal and De-lighting Priorscited by 14
  30. 2025VertexRegen: Mesh Generation with Continuous Level of Detailcited by 14
  31. 2025iFlame: Interleaving Full and Linear Attention for Efficient Mesh Generationcited by 13
  32. 2025HumanDreamer-X: Photorealistic Single-image Human Avatars Reconstruction via Gaussian Restorationcited by 13
  33. 2025Efficient Autoregressive Shape Generation via Octree-Based Adaptive Tokenizationcited by 12
  34. 2025PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformercited by 12
  35. 2025BrepGPT: Autoregressive B-rep Generation with Voronoi Half-Patchcited by 12
  36. 2024VideoGameBunny: Towards vision assistants for video gamescited by 11
  37. 2024Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2cited by 11
  38. 2024DMesh++: An Efficient Differentiable Mesh for Complex Shapescited by 11

50 most cited of 140 All 140 (JSON)