title: MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh
    Tokenization
summary: We introduce MeshAnything V2, an autoregressive transformer that generates
  Artist-Created Meshes (AM) aligned to given shapes. It can be integrated with
  various 3D asset production pipelines to achieve high-quality, highly
  controllable AM generation. MeshAnything V2 surpasses previous methods in both
  efficiency and performance using models of the same size. These improvements
  are due to our newly proposed mesh tokenization method: Adjacent Mesh
  Tokenization (AMT). Different from previous methods that represent each face
  with three vertices, AMT uses a single vertex whenever possible. Compared to
  previous methods, AMT requires about half the token sequence length to
  represent the same mesh in average. Furthermore, the token sequences from AMT
  are more compact and well-structured, fundamentally benefiting AM generation.
  Our extensive experiments show that AMT significantly improves the efficiency
  and performance of AM generation. Project Page:
  https://buaacyw.github.io/meshanything-v2/
paper_authors._id: 66b19260fb3bf90ddc41883f
paper_authors.name: Yiwen Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: Chen Yiwen
paper_authors.type: user
paper_authors.user: Yiwen-ntu
paper_authors._id: 66b19260fb3bf90ddc418840
paper_authors.name: Yikai Wang
paper_authors.status: claimed_verified
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paper_authors.fullname: Yikai Wang
paper_authors.type: user
paper_authors.user: yikaiw
paper_authors._id: 66b19260fb3bf90ddc418841
paper_authors.name: Yihao Luo
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paper_authors.avatarUrl: /avatars/e0c7a989893d97d3b27bf53c3e9a07ab.svg
paper_authors.fullname: Yihao Luo
paper_authors.type: user
paper_authors.user: Luo-Yihao
paper_authors._id: 66b19260fb3bf90ddc418842
paper_authors.name: Zhengyi Wang
paper_authors.status: admin_assigned
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paper_authors.fullname: Zhengyi Wang
paper_authors.type: user
paper_authors.user: Zhengyi
paper_authors._id: 66b19260fb3bf90ddc418843
paper_authors.name: Zilong Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: Zilong Chen
paper_authors.type: user
paper_authors.user: heheyas
paper_authors._id: 66b19260fb3bf90ddc418844
paper_authors.name: Jun Zhu
paper_authors._id: 66b19260fb3bf90ddc418845
paper_authors.name: Chi Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-08T07:42:05.791Z
paper_authors._id: 64196320ed725fef64419c2a
paper_authors.avatarUrl: /avatars/96feb22fb5e8931d6c9e0ea06148266f.svg
paper_authors.fullname: Chi Zhang
paper_authors.type: user
paper_authors.user: DrChiZhang
paper_authors._id: 66b19260fb3bf90ddc418846
paper_authors.name: Guosheng Lin
paper_title: MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh
    Tokenization
paper_summary: We introduce MeshAnything V2, an autoregressive transformer that generates
  Artist-Created Meshes (AM) aligned to given shapes. It can be integrated with
  various 3D asset production pipelines to achieve high-quality, highly
  controllable AM generation. MeshAnything V2 surpasses previous methods in both
  efficiency and performance using models of the same size. These improvements
  are due to our newly proposed mesh tokenization method: Adjacent Mesh
  Tokenization (AMT). Different from previous methods that represent each face
  with three vertices, AMT uses a single vertex whenever possible. Compared to
  previous methods, AMT requires about half the token sequence length to
  represent the same mesh in average. Furthermore, the token sequences from AMT
  are more compact and well-structured, fundamentally benefiting AM generation.
  Our extensive experiments show that AMT significantly improves the efficiency
  and performance of AM generation. Project Page:
  https://buaacyw.github.io/meshanything-v2/
paper_ai_keywords: autoregressive transformer Artist-Created Meshes (AM) Adjacent Mesh Tokenization (AMT) tokenization method token sequence length