title: MeshAnything: Artist-Created Mesh Generation with Autoregressive
    Transformers
summary: 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.
paper_authors._id: 666f92d1963bc5d5884bb55c
paper_authors.name: Yiwen Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-17T16:28:36.099Z
paper_authors._id: 64e45b1c2fbf570ac70dc6e8
paper_authors.avatarUrl: /avatars/8dd65f75f2c1b92abf2f189f6d8466a2.svg
paper_authors.fullname: Chen Yiwen
paper_authors.type: user
paper_authors.user: Yiwen-ntu
paper_authors._id: 666f92d1963bc5d5884bb55d
paper_authors.name: Tong He
paper_authors._id: 666f92d1963bc5d5884bb55e
paper_authors.name: Di Huang
paper_authors._id: 666f92d1963bc5d5884bb55f
paper_authors.name: Weicai Ye
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-18T09:11:15.633Z
paper_authors._id: 6360d9f0472131c3bc4f61df
paper_authors.avatarUrl: /avatars/c5d884e5ef19b781e3405aba6dd68ca8.svg
paper_authors.fullname: WeicaiYe
paper_authors.type: user
paper_authors.user: WeicaiYe
paper_authors._id: 666f92d1963bc5d5884bb560
paper_authors.name: Sijin Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-18T09:11:28.999Z
paper_authors._id: 64d6ee19c3b41747a271da59
paper_authors.avatarUrl: /avatars/c67da5d25fdf1dcb552772500955edb2.svg
paper_authors.fullname: Sijin Chen
paper_authors.type: user
paper_authors.user: ch3cook-fdu
paper_authors._id: 666f92d1963bc5d5884bb561
paper_authors.name: Jiaxiang Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-18T09:11:34.939Z
paper_authors._id: 6664dc00da1e6adefea0e177
paper_authors.avatarUrl: /avatars/17facee3d6feb800eee233ae81816cd5.svg
paper_authors.fullname: jiaxiang tang
paper_authors.type: user
paper_authors.user: jiaxiang-tang
paper_authors._id: 666f92d1963bc5d5884bb562
paper_authors.name: Xin Chen
paper_authors._id: 666f92d1963bc5d5884bb563
paper_authors.name: Zhongang Cai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-18T09:11:50.702Z
paper_authors._id: 652d06833b5997ed71ce5c46
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/xZTXEcnEogEmBm_ledJQr.jpeg
paper_authors.fullname: Zhongang Cai
paper_authors.type: user
paper_authors.user: caizhongang
paper_authors._id: 666f92d1963bc5d5884bb564
paper_authors.name: Lei Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-10T08:10:23.855Z
paper_authors._id: 6626a471430a124253f197c8
paper_authors.avatarUrl: /avatars/f5747fdbe495d1296fed9d16d8c95857.svg
paper_authors.fullname: yl-1993
paper_authors.type: user
paper_authors.user: yl-1993
paper_authors._id: 666f92d1963bc5d5884bb565
paper_authors.name: Gang Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-15T09:46:47.907Z
paper_authors._id: 63417332c5565a4b8d43a0d8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63417332c5565a4b8d43a0d8/MmnYG7Wu2Z_lqCBvTfJmy.png
paper_authors.fullname: Gang Yu
paper_authors.type: user
paper_authors.user: skicy
paper_authors._id: 666f92d1963bc5d5884bb566
paper_authors.name: Guosheng Lin
paper_authors._id: 666f92d1963bc5d5884bb567
paper_authors.name: Chi Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-08T07:42:38.386Z
paper_authors._id: 64196320ed725fef64419c2a
paper_authors.avatarUrl: /avatars/96feb22fb5e8931d6c9e0ea06148266f.svg
paper_authors.fullname: Chi Zhang
paper_authors.type: user
paper_authors.user: DrChiZhang
paper_title: MeshAnything: Artist-Created Mesh Generation with Autoregressive
    Transformers
paper_summary: 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.
paper_ai_keywords: VQ-VAE shape-conditioned decoder-only transformer shape-conditioned autoregressive mesh generation mesh vocabulary 3D representation 3D asset production methods 3D industry mesh extraction Artist-Created Measures (AMs) shape-conditioned autoregressive mesh generation