title: HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a
    Single Image
summary: 3D content creation from a single image is a long-standing yet highly
  desirable task. Recent advances introduce 2D diffusion priors, yielding
  reasonable results. However, existing methods are not hyper-realistic enough
  for post-generation usage, as users cannot view, render and edit the resulting
  3D content from a full range. To address these challenges, we introduce
  HyperDreamer with several key designs and appealing properties: 1) Viewable:
  360 degree mesh modeling with high-resolution textures enables the creation of
  visually compelling 3D models from a full range of observation points. 2)
  Renderable: Fine-grained semantic segmentation and data-driven priors are
  incorporated as guidance to learn reasonable albedo, roughness, and specular
  properties of the materials, enabling semantic-aware arbitrary material
  estimation. 3) Editable: For a generated model or their own data, users can
  interactively select any region via a few clicks and efficiently edit the
  texture with text-based guidance. Extensive experiments demonstrate the
  effectiveness of HyperDreamer in modeling region-aware materials with
  high-resolution textures and enabling user-friendly editing. We believe that
  HyperDreamer holds promise for advancing 3D content creation and finding
  applications in various domains.
paper_authors._id: 657295b5b268b1c509537ee2
paper_authors.name: Tong Wu
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paper_authors.name: Zhibing Li
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paper_authors.fullname: Zhibing LI
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paper_authors.name: Shuai Yang
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paper_authors.fullname: Shuai Yang
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paper_authors._id: 657295b5b268b1c509537ee5
paper_authors.name: Pan Zhang
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paper_authors.fullname: Pan Zhang
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paper_authors.name: Xinggang Pan
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paper_authors.name: Jiaqi Wang
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paper_authors.fullname: Jiaqi Wang
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paper_authors.user: myownskyW7
paper_authors._id: 657295b5b268b1c509537ee8
paper_authors.name: Dahua Lin
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paper_authors.fullname: Dahua Lin
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paper_authors.user: lindahua
paper_authors._id: 657295b5b268b1c509537ee9
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: Ziwei Liu
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paper_authors.user: liuziwei7
paper_title: HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a
    Single Image
paper_summary: 3D content creation from a single image is a long-standing yet highly
  desirable task. Recent advances introduce 2D diffusion priors, yielding
  reasonable results. However, existing methods are not hyper-realistic enough
  for post-generation usage, as users cannot view, render and edit the resulting
  3D content from a full range. To address these challenges, we introduce
  HyperDreamer with several key designs and appealing properties: 1) Viewable:
  360 degree mesh modeling with high-resolution textures enables the creation of
  visually compelling 3D models from a full range of observation points. 2)
  Renderable: Fine-grained semantic segmentation and data-driven priors are
  incorporated as guidance to learn reasonable albedo, roughness, and specular
  properties of the materials, enabling semantic-aware arbitrary material
  estimation. 3) Editable: For a generated model or their own data, users can
  interactively select any region via a few clicks and efficiently edit the
  texture with text-based guidance. Extensive experiments demonstrate the
  effectiveness of HyperDreamer in modeling region-aware materials with
  high-resolution textures and enabling user-friendly editing. We believe that
  HyperDreamer holds promise for advancing 3D content creation and finding
  applications in various domains.
paper_ai_keywords: 2D diffusion priors 360 degree mesh modeling high-resolution textures semantic segmentation data-driven priors albedo roughness specular properties Con-grained semantic segmentation text-based guidance