title: ATT3D: Amortized Text-to-3D Object Synthesis
summary: Text-to-3D modelling has seen exciting progress by combining generative
  text-to-image models with image-to-3D methods like Neural Radiance Fields.
  DreamFusion recently achieved high-quality results but requires a lengthy,
  per-prompt optimization to create 3D objects. To address this, we amortize
  optimization over text prompts by training on many prompts simultaneously with
  a unified model, instead of separately. With this, we share computation across
  a prompt set, training in less time than per-prompt optimization. Our framework
  - Amortized text-to-3D (ATT3D) - enables knowledge-sharing between prompts to
  generalize to unseen setups and smooth interpolations between text for novel
  assets and simple animations.
paper_authors._id: 648940b751b69a8c82ec1f15
paper_authors.name: Jonathan Lorraine
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paper_authors.fullname: Jonathan Lorraine
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paper_authors.name: Kevin Xie
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paper_authors.fullname: Kevin Xie
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paper_authors.user: kevincxie
paper_authors._id: 648940b751b69a8c82ec1f17
paper_authors.name: Xiaohui Zeng
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paper_authors.fullname: xiaohui zeng
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paper_authors.user: xiaohui2022
paper_authors._id: 648940b751b69a8c82ec1f18
paper_authors.name: Chen-Hsuan Lin
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paper_authors._id: 6324e100fee2904e370bc940
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paper_authors.fullname: Chen-Hsuan Lin
paper_authors.type: user
paper_authors.user: chenhsuanlin
paper_authors._id: 648940b751b69a8c82ec1f19
paper_authors.name: Towaki Takikawa
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paper_authors.name: Nicholas Sharp
paper_authors._id: 648940b751b69a8c82ec1f1b
paper_authors.name: Tsung-Yi Lin
paper_authors.status: admin_assigned
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paper_authors._id: 63b738acbd2d1535227daa4c
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paper_authors.fullname: Tsung-Yi Lin
paper_authors.type: user
paper_authors.user: tsungyi
paper_authors._id: 648940b751b69a8c82ec1f1c
paper_authors.name: Ming-Yu Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-14T08:44:08.862Z
paper_authors._id: 62f049afdf4b93aad5c7f2d6
paper_authors.avatarUrl: /avatars/e272e58ad996733d7098e50248e5b57e.svg
paper_authors.fullname: Ming-Yu Liu
paper_authors.type: user
paper_authors.user: mingyuliutw
paper_authors._id: 648940b751b69a8c82ec1f1d
paper_authors.name: Sanja Fidler
paper_authors._id: 648940b751b69a8c82ec1f1e
paper_authors.name: James Lucas
paper_title: ATT3D: Amortized Text-to-3D Object Synthesis
paper_summary: Text-to-3D modelling has seen exciting progress by combining generative
  text-to-image models with image-to-3D methods like Neural Radiance Fields.
  DreamFusion recently achieved high-quality results but requires a lengthy,
  per-prompt optimization to create 3D objects. To address this, we amortize
  optimization over text prompts by training on many prompts simultaneously with
  a unified model, instead of separately. With this, we share computation across
  a prompt set, training in less time than per-prompt optimization. Our framework
  - Amortized text-to-3D (ATT3D) - enables knowledge-sharing between prompts to
  generalize to unseen setups and smooth interpolations between text for novel
  assets and simple animations.
paper_ai_keywords: Neural Radiance Fields DreamFusion Amortized text-to-3D (ATT3D)