title: DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement
    Learning
summary: Triangle meshes play a crucial role in 3D applications for efficient
  manipulation and rendering. While auto-regressive methods generate structured
  meshes by predicting discrete vertex tokens, they are often constrained by
  limited face counts and mesh incompleteness. To address these challenges, we
  propose DeepMesh, a framework that optimizes mesh generation through two key
  innovations: (1) an efficient pre-training strategy incorporating a novel
  tokenization algorithm, along with improvements in data curation and
  processing, and (2) the introduction of Reinforcement Learning (RL) into 3D
  mesh generation to achieve human preference alignment via Direct Preference
  Optimization (DPO). We design a scoring standard that combines human evaluation
  with 3D metrics to collect preference pairs for DPO, ensuring both visual
  appeal and geometric accuracy. Conditioned on point clouds and images, DeepMesh
  generates meshes with intricate details and precise topology, outperforming
  state-of-the-art methods in both precision and quality. Project page:
  https://zhaorw02.github.io/DeepMesh/
paper_authors._id: 67db8c4c9e4f93ee46411c1d
paper_authors.name: Ruowen Zhao
paper_authors.status: claimed_verified
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paper_authors.fullname: Ruowen Zhao
paper_authors.type: user
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paper_authors._id: 67db8c4c9e4f93ee46411c1e
paper_authors.name: Junliang Ye
paper_authors.status: claimed_verified
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paper_authors.fullname: yejunliang
paper_authors.type: user
paper_authors.user: yejunliang23
paper_authors._id: 67db8c4c9e4f93ee46411c1f
paper_authors.name: Zhengyi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-20T19:17:06.252Z
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paper_authors.fullname: Zhengyi Wang
paper_authors.type: user
paper_authors.user: Zhengyi
paper_authors._id: 67db8c4c9e4f93ee46411c20
paper_authors.name: Guangce Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-20T11:06:05.371Z
paper_authors._id: 674fcc541dbfdd4dee12d8e1
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paper_authors.fullname: Guangce Liu
paper_authors.type: user
paper_authors.user: guangce
paper_authors._id: 67db8c4c9e4f93ee46411c21
paper_authors.name: Yiwen Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-20T11:06:35.743Z
paper_authors._id: 6422a9669a00fbe9dfe665a1
paper_authors.avatarUrl: /avatars/288bf5ea18205bcb0bbea29a304ffbbb.svg
paper_authors.fullname: Yiwen Chen
paper_authors.type: user
paper_authors.user: NTU-yiwen
paper_authors._id: 67db8c4c9e4f93ee46411c22
paper_authors.name: Yikai Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-20T11:06:45.552Z
paper_authors._id: 63463bc4547c70e4b7d3009f
paper_authors.avatarUrl: /avatars/6e5350fd998f0a7a4143d7504218164a.svg
paper_authors.fullname: Yikai Wang
paper_authors.type: user
paper_authors.user: yikaiwang
paper_authors._id: 67db8c4c9e4f93ee46411c23
paper_authors.name: Jun Zhu
paper_title: DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement
    Learning
paper_summary: Triangle meshes play a crucial role in 3D applications for efficient
  manipulation and rendering. While auto-regressive methods generate structured
  meshes by predicting discrete vertex tokens, they are often constrained by
  limited face counts and mesh incompleteness. To address these challenges, we
  propose DeepMesh, a framework that optimizes mesh generation through two key
  innovations: (1) an efficient pre-training strategy incorporating a novel
  tokenization algorithm, along with improvements in data curation and
  processing, and (2) the introduction of Reinforcement Learning (RL) into 3D
  mesh generation to achieve human preference alignment via Direct Preference
  Optimization (DPO). We design a scoring standard that combines human evaluation
  with 3D metrics to collect preference pairs for DPO, ensuring both visual
  appeal and geometric accuracy. Conditioned on point clouds and images, DeepMesh
  generates meshes with intricate details and precise topology, outperforming
  state-of-the-art methods in both precision and quality. Project page:
  https://zhaorw02.github.io/DeepMesh/
paper_ai_summary: DeepMesh generates high-quality 3D meshes with intricate details and precise topology using an efficient pre-training strategy and reinforcement learning, outperforming existing methods.
paper_ai_keywords: auto-regressive methods tokenization algorithm data curation Reinforcement Learning Direct Preference Optimization DPO scoring standard visual appeal geometric accuracy point clouds images mesh generation topology