title: TreeMeshGPT: Artistic Mesh Generation with Autoregressive Tree
    Sequencing
summary: We introduce TreeMeshGPT, an autoregressive Transformer designed to generate
  high-quality artistic meshes aligned with input point clouds. Instead of the
  conventional next-token prediction in autoregressive Transformer, we propose a
  novel Autoregressive Tree Sequencing where the next input token is retrieved
  from a dynamically growing tree structure that is built upon the triangle
  adjacency of faces within the mesh. Our sequencing enables the mesh to extend
  locally from the last generated triangular face at each step, and therefore
  reduces training difficulty and improves mesh quality. Our approach represents
  each triangular face with two tokens, achieving a compression rate of
  approximately 22% compared to the naive face tokenization. This efficient
  tokenization enables our model to generate highly detailed artistic meshes with
  strong point cloud conditioning, surpassing previous methods in both capacity
  and fidelity. Furthermore, our method generates mesh with strong normal
  orientation constraints, minimizing flipped normals commonly encountered in
  previous methods. Our experiments show that TreeMeshGPT enhances the mesh
  generation quality with refined details and normal orientation consistency.
paper_authors._id: 67d816adc51e889cd00ad162
paper_authors.name: Stefan Lionar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T14:42:03.888Z
paper_authors._id: 63bfb9a54ed1b8fd6883524c
paper_authors.avatarUrl: /avatars/5462264a8cf58cb15fcc5194336d45ac.svg
paper_authors.fullname: Stefan Lionar
paper_authors.type: user
paper_authors.user: slionar
paper_authors._id: 67d816adc51e889cd00ad163
paper_authors.name: Jiabin Liang
paper_authors._id: 67d816adc51e889cd00ad164
paper_authors.name: Gim Hee Lee
paper_title: TreeMeshGPT: Artistic Mesh Generation with Autoregressive Tree
    Sequencing
paper_summary: We introduce TreeMeshGPT, an autoregressive Transformer designed to generate
  high-quality artistic meshes aligned with input point clouds. Instead of the
  conventional next-token prediction in autoregressive Transformer, we propose a
  novel Autoregressive Tree Sequencing where the next input token is retrieved
  from a dynamically growing tree structure that is built upon the triangle
  adjacency of faces within the mesh. Our sequencing enables the mesh to extend
  locally from the last generated triangular face at each step, and therefore
  reduces training difficulty and improves mesh quality. Our approach represents
  each triangular face with two tokens, achieving a compression rate of
  approximately 22% compared to the naive face tokenization. This efficient
  tokenization enables our model to generate highly detailed artistic meshes with
  strong point cloud conditioning, surpassing previous methods in both capacity
  and fidelity. Furthermore, our method generates mesh with strong normal
  orientation constraints, minimizing flipped normals commonly encountered in
  previous methods. Our experiments show that TreeMeshGPT enhances the mesh
  generation quality with refined details and normal orientation consistency.
paper_ai_summary: TreeMeshGPT uses a novel Autoregressive Tree Sequencing approach to generate high-quality artistic meshes from point clouds, reducing training difficulty and improving mesh quality and normal orientation consistency.
paper_ai_keywords: autoregressive Transformer Autoregressive Tree Sequencing triangle adjacency mesh generation face tokenization compression rate point cloud conditioning mesh quality normal orientation constraints