title: LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis
summary: Recent text-to-3D generation approaches produce impressive 3D results but
  require time-consuming optimization that can take up to an hour per prompt.
  Amortized methods like ATT3D optimize multiple prompts simultaneously to
  improve efficiency, enabling fast text-to-3D synthesis. However, they cannot
  capture high-frequency geometry and texture details and struggle to scale to
  large prompt sets, so they generalize poorly. We introduce LATTE3D, addressing
  these limitations to achieve fast, high-quality generation on a significantly
  larger prompt set. Key to our method is 1) building a scalable architecture and
  2) leveraging 3D data during optimization through 3D-aware diffusion priors,
  shape regularization, and model initialization to achieve robustness to diverse
  and complex training prompts. LATTE3D amortizes both neural field and textured
  surface generation to produce highly detailed textured meshes in a single
  forward pass. LATTE3D generates 3D objects in 400ms, and can be further
  enhanced with fast test-time optimization.
paper_authors._id: 6600fc84bcf0790b8d611a6e
paper_authors.name: Kevin Xie
paper_authors.status: claimed_verified
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paper_authors.fullname: Kevin Xie
paper_authors.type: user
paper_authors.user: kevincxie
paper_authors._id: 6600fc84bcf0790b8d611a6f
paper_authors.name: Jonathan Lorraine
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paper_authors.fullname: Jonathan Lorraine
paper_authors.type: user
paper_authors.user: lorraine2
paper_authors._id: 6600fc84bcf0790b8d611a70
paper_authors.name: Tianshi Cao
paper_authors.status: admin_assigned
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paper_authors._id: 63e07dfaf0c75dfb876d9d71
paper_authors.avatarUrl: /avatars/8dab667735111e070cc6d44609dbdaf3.svg
paper_authors.fullname: Tianshi Cao
paper_authors.type: user
paper_authors.user: jeanlancel
paper_authors._id: 6600fc84bcf0790b8d611a71
paper_authors.name: Jun Gao
paper_authors._id: 6600fc84bcf0790b8d611a72
paper_authors.name: James Lucas
paper_authors._id: 6600fc84bcf0790b8d611a73
paper_authors.name: Antonio Torralba
paper_authors._id: 6600fc84bcf0790b8d611a74
paper_authors.name: Sanja Fidler
paper_authors._id: 6600fc84bcf0790b8d611a75
paper_authors.name: Xiaohui Zeng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-16T19:47:27.434Z
paper_authors._id: 63363cb8a8048332fdc268cd
paper_authors.avatarUrl: /avatars/a9bf6ce3394a8b76eade1a7517b37fa1.svg
paper_authors.fullname: xiaohui zeng
paper_authors.type: user
paper_authors.user: xiaohui2022
paper_title: LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis
paper_summary: Recent text-to-3D generation approaches produce impressive 3D results but
  require time-consuming optimization that can take up to an hour per prompt.
  Amortized methods like ATT3D optimize multiple prompts simultaneously to
  improve efficiency, enabling fast text-to-3D synthesis. However, they cannot
  capture high-frequency geometry and texture details and struggle to scale to
  large prompt sets, so they generalize poorly. We introduce LATTE3D, addressing
  these limitations to achieve fast, high-quality generation on a significantly
  larger prompt set. Key to our method is 1) building a scalable architecture and
  2) leveraging 3D data during optimization through 3D-aware diffusion priors,
  shape regularization, and model initialization to achieve robustness to diverse
  and complex training prompts. LATTE3D amortizes both neural field and textured
  surface generation to produce highly detailed textured meshes in a single
  forward pass. LATTE3D generates 3D objects in 400ms, and can be further
  enhanced with fast test-time optimization.
paper_ai_keywords: ATT3D LATTE3D text-to-3D generation amortized methods high-frequency geometry texture details optimization scalable architecture 3D-aware diffusion priors shape regularization model initialization neural field textured surface generation textured meshes forward pass test-time optimization