title: Customize-It-3D: High-Quality 3D Creation from A Single Image Using
    Subject-Specific Knowledge Prior
summary: In this paper, we present a novel two-stage approach that fully utilizes the
  information provided by the reference image to establish a customized knowledge
  prior for image-to-3D generation. While previous approaches primarily rely on a
  general diffusion prior, which struggles to yield consistent results with the
  reference image, we propose a subject-specific and multi-modal diffusion model.
  This model not only aids NeRF optimization by considering the shading mode for
  improved geometry but also enhances texture from the coarse results to achieve
  superior refinement. Both aspects contribute to faithfully aligning the 3D
  content with the subject. Extensive experiments showcase the superiority of our
  method, Customize-It-3D, outperforming previous works by a substantial margin.
  It produces faithful 360-degree reconstructions with impressive visual quality,
  making it well-suited for various applications, including text-to-3D creation.
paper_authors._id: 65825ee8afc6b50a2cacfa72
paper_authors.name: Nan Huang
paper_authors._id: 65825ee8afc6b50a2cacfa73
paper_authors.name: Ting Zhang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-12-20T03:26:36.433Z
paper_authors._id: 6350ffa18445bbe32e926360
paper_authors.avatarUrl: /avatars/bc67d114191be7b16b463731a84c7e05.svg
paper_authors.fullname: Ting Zhang
paper_authors.type: user
paper_authors.user: hellozt
paper_authors._id: 65825ee8afc6b50a2cacfa74
paper_authors.name: Yuhui Yuan
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-12-20T03:26:36.433Z
paper_authors._id: 631f108bb45367a05fe74260
paper_authors.avatarUrl: /avatars/c20da6800b643728062712d9a2771648.svg
paper_authors.fullname: Researcher
paper_authors.type: user
paper_authors.user: YuanYuhui
paper_authors._id: 65825ee8afc6b50a2cacfa75
paper_authors.name: Dong Chen
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-12-20T03:26:36.433Z
paper_authors._id: 63801921f6d1901fcab032f7
paper_authors.avatarUrl: /avatars/fdb1cc08c531c13d5b3bae5efd225bb4.svg
paper_authors.fullname: Chen
paper_authors.type: user
paper_authors.user: HelloKitty123
paper_authors._id: 65825ee8afc6b50a2cacfa76
paper_authors.name: Shanghang Zhang
paper_title: Customize-It-3D: High-Quality 3D Creation from A Single Image Using
    Subject-Specific Knowledge Prior
paper_summary: In this paper, we present a novel two-stage approach that fully utilizes the
  information provided by the reference image to establish a customized knowledge
  prior for image-to-3D generation. While previous approaches primarily rely on a
  general diffusion prior, which struggles to yield consistent results with the
  reference image, we propose a subject-specific and multi-modal diffusion model.
  This model not only aids NeRF optimization by considering the shading mode for
  improved geometry but also enhances texture from the coarse results to achieve
  superior refinement. Both aspects contribute to faithfully aligning the 3D
  content with the subject. Extensive experiments showcase the superiority of our
  method, Customize-It-3D, outperforming previous works by a substantial margin.
  It produces faithful 360-degree reconstructions with impressive visual quality,
  making it well-suited for various applications, including text-to-3D creation.
paper_ai_summary: A subject-specific and multi-modal diffusion model enhances NeRF optimization and texture refinement for image-to-3D generation, producing high-quality 360-degree reconstructions.
paper_ai_keywords: diffusion model NeRF shading mode texture refinement faithful reconstruction text-to-3D