title: Cycle3D: High-quality and Consistent Image-to-3D Generation via
    Generation-Reconstruction Cycle
summary: Recent 3D large reconstruction models typically employ a two-stage process,
  including first generate multi-view images by a multi-view diffusion model, and
  then utilize a feed-forward model to reconstruct images to 3D content.However,
  multi-view diffusion models often produce low-quality and inconsistent images,
  adversely affecting the quality of the final 3D reconstruction. To address this
  issue, we propose a unified 3D generation framework called Cycle3D, which
  cyclically utilizes a 2D diffusion-based generation module and a feed-forward
  3D reconstruction module during the multi-step diffusion process. Concretely,
  2D diffusion model is applied for generating high-quality texture, and the
  reconstruction model guarantees multi-view consistency.Moreover, 2D diffusion
  model can further control the generated content and inject reference-view
  information for unseen views, thereby enhancing the diversity and texture
  consistency of 3D generation during the denoising process. Extensive
  experiments demonstrate the superior ability of our method to create 3D content
  with high-quality and consistency compared with state-of-the-art baselines.
paper_authors._id: 66a85c530a24e5a2b885f6f3
paper_authors.name: Zhenyu Tang
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paper_authors.fullname: Zhenyu Tang
paper_authors.type: user
paper_authors.user: royjames
paper_authors._id: 66a85c530a24e5a2b885f6f4
paper_authors.name: Junwu Zhang
paper_authors.status: admin_assigned
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paper_authors.fullname: Junwu Zhang
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paper_authors.user: junwuzhang
paper_authors._id: 66a85c530a24e5a2b885f6f5
paper_authors.name: Xinhua Cheng
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paper_authors.fullname: Xinhua Cheng
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paper_authors.user: cxh0519
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paper_authors.name: Wangbo Yu
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paper_authors.fullname: Wangbo Yu
paper_authors.type: user
paper_authors.user: Drexubery
paper_authors._id: 66a85c530a24e5a2b885f6f7
paper_authors.name: Chaoran Feng
paper_authors.status: admin_assigned
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paper_authors.fullname: Feng Chaoran
paper_authors.type: user
paper_authors.user: Falcary
paper_authors._id: 66a85c530a24e5a2b885f6f8
paper_authors.name: Yatian Pang
paper_authors._id: 66a85c530a24e5a2b885f6f9
paper_authors.name: Bin Lin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-03T11:17:04.347Z
paper_authors._id: 6367a8175bb06007ea099b8f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6367a8175bb06007ea099b8f/IjG7HyWyWRlVt_XwRbxRW.jpeg
paper_authors.fullname: linbin
paper_authors.type: user
paper_authors.user: LanguageBind
paper_authors._id: 66a85c530a24e5a2b885f6fa
paper_authors.name: Li Yuan
paper_title: Cycle3D: High-quality and Consistent Image-to-3D Generation via
    Generation-Reconstruction Cycle
paper_summary: Recent 3D large reconstruction models typically employ a two-stage process,
  including first generate multi-view images by a multi-view diffusion model, and
  then utilize a feed-forward model to reconstruct images to 3D content.However,
  multi-view diffusion models often produce low-quality and inconsistent images,
  adversely affecting the quality of the final 3D reconstruction. To address this
  issue, we propose a unified 3D generation framework called Cycle3D, which
  cyclically utilizes a 2D diffusion-based generation module and a feed-forward
  3D reconstruction module during the multi-step diffusion process. Concretely,
  2D diffusion model is applied for generating high-quality texture, and the
  reconstruction model guarantees multi-view consistency.Moreover, 2D diffusion
  model can further control the generated content and inject reference-view
  information for unseen views, thereby enhancing the diversity and texture
  consistency of 3D generation during the denoising process. Extensive
  experiments demonstrate the superior ability of our method to create 3D content
  with high-quality and consistency compared with state-of-the-art baselines.
paper_ai_keywords: 2D diffusion model 3D reconstruction module Cycle3D multi-view diffusion model feed-forward model high-quality texture multi-view consistency denoising process reference-view information texture consistency