title: Structured 3D Latents for Scalable and Versatile 3D Generation
summary: We introduce a novel 3D generation method for versatile and high-quality 3D
  asset creation. The cornerstone is a unified Structured LATent (SLAT)
  representation which allows decoding to different output formats, such as
  Radiance Fields, 3D Gaussians, and meshes. This is achieved by integrating a
  sparsely-populated 3D grid with dense multiview visual features extracted from
  a powerful vision foundation model, comprehensively capturing both structural
  (geometry) and textural (appearance) information while maintaining flexibility
  during decoding. We employ rectified flow transformers tailored for SLAT as our
  3D generation models and train models with up to 2 billion parameters on a
  large 3D asset dataset of 500K diverse objects. Our model generates
  high-quality results with text or image conditions, significantly surpassing
  existing methods, including recent ones at similar scales. We showcase flexible
  output format selection and local 3D editing capabilities which were not
  offered by previous models. Code, model, and data will be released.
paper_authors._id: 674f2822ecb7d1fab211ac7b
paper_authors.name: Jianfeng Xiang
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paper_authors.fullname: Jianfeng Xiang
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paper_authors.user: JeffreyXiang
paper_authors._id: 674f2822ecb7d1fab211ac7c
paper_authors.name: Zelong Lv
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paper_authors.fullname: Zelong Lv
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paper_authors.user: Maxtir
paper_authors._id: 674f2822ecb7d1fab211ac7d
paper_authors.name: Sicheng Xu
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paper_authors.fullname: Sicheng Xu
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paper_authors.user: sicxu
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paper_authors.name: Yu Deng
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paper_authors.fullname: Yu Deng
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paper_authors.user: bEijuuu
paper_authors._id: 674f2822ecb7d1fab211ac7f
paper_authors.name: Ruicheng Wang
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paper_authors.fullname: Ruicheng Wang
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paper_authors.user: Ruicheng
paper_authors._id: 674f2822ecb7d1fab211ac80
paper_authors.name: Bowen Zhang
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paper_authors.statusLastChangedAt: 2024-12-06T10:42:58.378Z
paper_authors._id: 64560bb04cfac492278df590
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paper_authors.fullname: Bowen Zhang
paper_authors.type: user
paper_authors.user: bowenzhang
paper_authors._id: 674f2822ecb7d1fab211ac81
paper_authors.name: Dong Chen
paper_authors._id: 674f2822ecb7d1fab211ac82
paper_authors.name: Xin Tong
paper_authors._id: 674f2822ecb7d1fab211ac83
paper_authors.name: Jiaolong Yang
paper_authors.status: admin_assigned
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paper_authors._id: 66a59f6b94d2b190a22b7c80
paper_authors.avatarUrl: /avatars/9b59c0bb3e171d5246e3cb3aa8377a77.svg
paper_authors.fullname: jiaolong yang
paper_authors.type: user
paper_authors.user: godjiaolongge
paper_title: Structured 3D Latents for Scalable and Versatile 3D Generation
paper_summary: We introduce a novel 3D generation method for versatile and high-quality 3D
  asset creation. The cornerstone is a unified Structured LATent (SLAT)
  representation which allows decoding to different output formats, such as
  Radiance Fields, 3D Gaussians, and meshes. This is achieved by integrating a
  sparsely-populated 3D grid with dense multiview visual features extracted from
  a powerful vision foundation model, comprehensively capturing both structural
  (geometry) and textural (appearance) information while maintaining flexibility
  during decoding. We employ rectified flow transformers tailored for SLAT as our
  3D generation models and train models with up to 2 billion parameters on a
  large 3D asset dataset of 500K diverse objects. Our model generates
  high-quality results with text or image conditions, significantly surpassing
  existing methods, including recent ones at similar scales. We showcase flexible
  output format selection and local 3D editing capabilities which were not
  offered by previous models. Code, model, and data will be released.
paper_ai_keywords: Structured LATent (SLAT) Radiance Fields 3D Gaussians meshes sparsely-populated 3D grid dense multiview visual features vision foundation model rectified flow transformers 3D generation models parameter-efficient fine-tuning