title: You See it, You Got it: Learning 3D Creation on Pose-Free Videos at
    Scale
summary: Recent 3D generation models typically rely on limited-scale 3D `gold-labels'
  or 2D diffusion priors for 3D content creation. However, their performance is
  upper-bounded by constrained 3D priors due to the lack of scalable learning
  paradigms. In this work, we present See3D, a visual-conditional multi-view
  diffusion model trained on large-scale Internet videos for open-world 3D
  creation. The model aims to Get 3D knowledge by solely Seeing the visual
  contents from the vast and rapidly growing video data -- You See it, You Got
  it. To achieve this, we first scale up the training data using a proposed data
  curation pipeline that automatically filters out multi-view inconsistencies and
  insufficient observations from source videos. This results in a high-quality,
  richly diverse, large-scale dataset of multi-view images, termed WebVi3D,
  containing 320M frames from 16M video clips. Nevertheless, learning generic 3D
  priors from videos without explicit 3D geometry or camera pose annotations is
  nontrivial, and annotating poses for web-scale videos is prohibitively
  expensive. To eliminate the need for pose conditions, we introduce an
  innovative visual-condition - a purely 2D-inductive visual signal generated by
  adding time-dependent noise to the masked video data. Finally, we introduce a
  novel visual-conditional 3D generation framework by integrating See3D into a
  warping-based pipeline for high-fidelity 3D generation. Our numerical and
  visual comparisons on single and sparse reconstruction benchmarks show that
  See3D, trained on cost-effective and scalable video data, achieves notable
  zero-shot and open-world generation capabilities, markedly outperforming models
  trained on costly and constrained 3D datasets. Please refer to our project page
  at: https://vision.baai.ac.cn/see3d
paper_authors._id: 6757e5546adb3cd340be4820
paper_authors.name: Baorui Ma
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paper_authors._id: 6757e5546adb3cd340be4821
paper_authors.name: Huachen Gao
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paper_authors.fullname: Huachen Gao
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paper_authors.user: Infinite888
paper_authors._id: 6757e5546adb3cd340be4822
paper_authors.name: Haoge Deng
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paper_authors.fullname: Haoge Deng
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paper_authors.user: Bitterdhg
paper_authors._id: 6757e5546adb3cd340be4823
paper_authors.name: Zhengxiong Luo
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paper_authors.fullname: zhengxiong
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paper_authors.user: lzxlog
paper_authors._id: 6757e5546adb3cd340be4824
paper_authors.name: Tiejun Huang
paper_authors._id: 6757e5546adb3cd340be4825
paper_authors.name: Lulu Tang
paper_authors._id: 6757e5546adb3cd340be4826
paper_authors.name: Xinlong Wang
paper_title: You See it, You Got it: Learning 3D Creation on Pose-Free Videos at
    Scale
paper_summary: Recent 3D generation models typically rely on limited-scale 3D `gold-labels'
  or 2D diffusion priors for 3D content creation. However, their performance is
  upper-bounded by constrained 3D priors due to the lack of scalable learning
  paradigms. In this work, we present See3D, a visual-conditional multi-view
  diffusion model trained on large-scale Internet videos for open-world 3D
  creation. The model aims to Get 3D knowledge by solely Seeing the visual
  contents from the vast and rapidly growing video data -- You See it, You Got
  it. To achieve this, we first scale up the training data using a proposed data
  curation pipeline that automatically filters out multi-view inconsistencies and
  insufficient observations from source videos. This results in a high-quality,
  richly diverse, large-scale dataset of multi-view images, termed WebVi3D,
  containing 320M frames from 16M video clips. Nevertheless, learning generic 3D
  priors from videos without explicit 3D geometry or camera pose annotations is
  nontrivial, and annotating poses for web-scale videos is prohibitively
  expensive. To eliminate the need for pose conditions, we introduce an
  innovative visual-condition - a purely 2D-inductive visual signal generated by
  adding time-dependent noise to the masked video data. Finally, we introduce a
  novel visual-conditional 3D generation framework by integrating See3D into a
  warping-based pipeline for high-fidelity 3D generation. Our numerical and
  visual comparisons on single and sparse reconstruction benchmarks show that
  See3D, trained on cost-effective and scalable video data, achieves notable
  zero-shot and open-world generation capabilities, markedly outperforming models
  trained on costly and constrained 3D datasets. Please refer to our project page
  at: https://vision.baai.ac.cn/see3d
paper_ai_keywords: See3D multi-view diffusion model video data WebVi3D data curation pipeline multi-view inconsistencies visual signal time-dependent noise visual-condition warping-based pipeline zero-shot generation open-world generation