title: Turbo3D: Ultra-fast Text-to-3D Generation
summary: We present Turbo3D, an ultra-fast text-to-3D system capable of generating
  high-quality Gaussian splatting assets in under one second. Turbo3D employs a
  rapid 4-step, 4-view diffusion generator and an efficient feed-forward Gaussian
  reconstructor, both operating in latent space. The 4-step, 4-view generator is
  a student model distilled through a novel Dual-Teacher approach, which
  encourages the student to learn view consistency from a multi-view teacher and
  photo-realism from a single-view teacher. By shifting the Gaussian
  reconstructor's inputs from pixel space to latent space, we eliminate the extra
  image decoding time and halve the transformer sequence length for maximum
  efficiency. Our method demonstrates superior 3D generation results compared to
  previous baselines, while operating in a fraction of their runtime.
paper_authors._id: 675306120d314916ecfd5e0c
paper_authors.name: Hanzhe Hu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-09T11:06:52.310Z
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paper_authors.fullname: Hanzhe Hu
paper_authors.type: user
paper_authors.user: howard06
paper_authors._id: 675306120d314916ecfd5e0d
paper_authors.name: Tianwei Yin
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/bf18f9f76183f81589a80b3d0215f1a5.svg
paper_authors.fullname: Tianwei Yin
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paper_authors.user: tianweiy
paper_authors._id: 675306120d314916ecfd5e0e
paper_authors.name: Fujun Luan
paper_authors.status: admin_assigned
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paper_authors._id: 662ae0aa4f711ee4e1e17ffc
paper_authors.avatarUrl: /avatars/2372544e8e5cfeb60a052c793f5e2522.svg
paper_authors.fullname: Fujun Luan
paper_authors.type: user
paper_authors.user: luanfujun
paper_authors._id: 675306120d314916ecfd5e0f
paper_authors.name: Yiwei Hu
paper_authors.status: admin_assigned
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paper_authors._id: 6420f17ee40f66bcd1e6ad40
paper_authors.avatarUrl: /avatars/ff1f90ab896a0ebd0f30e9df5fcb84bf.svg
paper_authors.fullname: Yiwei Hu
paper_authors.type: user
paper_authors.user: Crymson-H
paper_authors._id: 675306120d314916ecfd5e10
paper_authors.name: Hao Tan
paper_authors._id: 675306120d314916ecfd5e11
paper_authors.name: Zexiang Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-18T13:44:25.134Z
paper_authors._id: 65565b1b8616d8d740abb976
paper_authors.avatarUrl: /avatars/c245b19ed05292bf1bf4a9d8cf2a409c.svg
paper_authors.fullname: Zexiang Xu
paper_authors.type: user
paper_authors.user: zexiangxu
paper_authors._id: 675306120d314916ecfd5e12
paper_authors.name: Sai Bi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-18T13:44:32.836Z
paper_authors._id: 66303560597dc7c535168f07
paper_authors.avatarUrl: /avatars/a69d52a4a198c6c8451c1d137031bf07.svg
paper_authors.fullname: Sai Bi
paper_authors.type: user
paper_authors.user: saibi
paper_authors._id: 675306120d314916ecfd5e13
paper_authors.name: Shubham Tulsiani
paper_authors._id: 675306120d314916ecfd5e14
paper_authors.name: Kai Zhang
paper_title: Turbo3D: Ultra-fast Text-to-3D Generation
paper_summary: We present Turbo3D, an ultra-fast text-to-3D system capable of generating
  high-quality Gaussian splatting assets in under one second. Turbo3D employs a
  rapid 4-step, 4-view diffusion generator and an efficient feed-forward Gaussian
  reconstructor, both operating in latent space. The 4-step, 4-view generator is
  a student model distilled through a novel Dual-Teacher approach, which
  encourages the student to learn view consistency from a multi-view teacher and
  photo-realism from a single-view teacher. By shifting the Gaussian
  reconstructor's inputs from pixel space to latent space, we eliminate the extra
  image decoding time and halve the transformer sequence length for maximum
  efficiency. Our method demonstrates superior 3D generation results compared to
  previous baselines, while operating in a fraction of their runtime.
paper_ai_keywords: Turbo3D Gaussian splatting diffusion generator feed-forward Gaussian reconstructor latent space Dual-Teacher approach view consistency photo-realism image decoding transformer sequence length