title: One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View
    Generation and 3D Diffusion
summary: Recent advancements in open-world 3D object generation have been remarkable,
  with image-to-3D methods offering superior fine-grained control over their
  text-to-3D counterparts. However, most existing models fall short in
  simultaneously providing rapid generation speeds and high fidelity to input
  images - two features essential for practical applications. In this paper, we
  present One-2-3-45++, an innovative method that transforms a single image into
  a detailed 3D textured mesh in approximately one minute. Our approach aims to
  fully harness the extensive knowledge embedded in 2D diffusion models and
  priors from valuable yet limited 3D data. This is achieved by initially
  finetuning a 2D diffusion model for consistent multi-view image generation,
  followed by elevating these images to 3D with the aid of multi-view conditioned
  3D native diffusion models. Extensive experimental evaluations demonstrate that
  our method can produce high-quality, diverse 3D assets that closely mirror the
  original input image. Our project webpage:
  https://sudo-ai-3d.github.io/One2345plus_page.
paper_authors._id: 65544370f7f0fcad58b387e9
paper_authors.name: Minghua Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-15T09:34:52.896Z
paper_authors._id: 63b70a696fca9d2a1904f606
paper_authors.avatarUrl: /avatars/a11741787b884cc865c1b7e9e6a7ec24.svg
paper_authors.fullname: Minghua Liu
paper_authors.type: user
paper_authors.user: minghua
paper_authors._id: 65544370f7f0fcad58b387ea
paper_authors.name: Ruoxi Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-15T09:39:30.020Z
paper_authors._id: 640820798dca6cec91cb2f0a
paper_authors.avatarUrl: /avatars/e011bc04facd251570247d1ae214dbde.svg
paper_authors.fullname: Ruoxi Shi
paper_authors.type: user
paper_authors.user: eliphatfs
paper_authors._id: 65544370f7f0fcad58b387eb
paper_authors.name: Linghao Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-15T09:39:37.904Z
paper_authors._id: 648396701d2f54a4882a36f1
paper_authors.avatarUrl: /avatars/b00c9d7df0113bd797894b5fa09f55fb.svg
paper_authors.fullname: Linghao Chen
paper_authors.type: user
paper_authors.user: fsky097
paper_authors._id: 65544370f7f0fcad58b387ec
paper_authors.name: Zhuoyang Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-15T09:39:44.847Z
paper_authors._id: 650e6ab08f3228d807707735
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/650e6ab08f3228d807707735/yFo6aLuyGH06t9yG8AOp7.png
paper_authors.fullname: Zhuoyang Zhang
paper_authors.type: user
paper_authors.user: zhuoyang20
paper_authors._id: 65544370f7f0fcad58b387ed
paper_authors.name: Chao Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-15T08:18:59.481Z
paper_authors._id: 6468a48cff18750165a5aad2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vmXEWka_q2a4mBDNjbMuY.jpeg
paper_authors.fullname: Chao Xu
paper_authors.type: user
paper_authors.user: chaoxu
paper_authors._id: 65544370f7f0fcad58b387ee
paper_authors.name: Xinyue Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-15T09:39:51.658Z
paper_authors._id: 646d6615da8e99940b7143cf
paper_authors.avatarUrl: /avatars/e6e24e993fc4f3e9c7026b4c34bd1a25.svg
paper_authors.fullname: Xinyue Wei
paper_authors.type: user
paper_authors.user: sarahwei0210
paper_authors._id: 65544370f7f0fcad58b387ef
paper_authors.name: Hansheng Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-15T09:40:10.762Z
paper_authors._id: 638067fcb334960c987fbeda
paper_authors.avatarUrl: /avatars/87f1eaaf6b3a9c0d47d6f406261ccc18.svg
paper_authors.fullname: Hansheng Chen
paper_authors.type: user
paper_authors.user: Lakonik
paper_authors._id: 65544370f7f0fcad58b387f0
paper_authors.name: Chong Zeng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-15T09:40:18.182Z
paper_authors._id: 60747cbf3ea03830676542b5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60747cbf3ea03830676542b5/wGr1Jzz520JM9nZ-UcLyb.png
paper_authors.fullname: Chong Zeng
paper_authors.type: user
paper_authors.user: NCJ
paper_authors._id: 65544370f7f0fcad58b387f1
paper_authors.name: Jiayuan Gu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-15T09:40:26.405Z
paper_authors._id: 638aa283cebef0d13aa2ec2e
paper_authors.avatarUrl: /avatars/3b67e6a6073033864a817230e97c27ca.svg
paper_authors.fullname: Jiayuan Gu
paper_authors.type: user
paper_authors.user: jigu
paper_authors._id: 65544370f7f0fcad58b387f2
paper_authors.name: Hao Su
paper_title: One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View
    Generation and 3D Diffusion
paper_summary: Recent advancements in open-world 3D object generation have been remarkable,
  with image-to-3D methods offering superior fine-grained control over their
  text-to-3D counterparts. However, most existing models fall short in
  simultaneously providing rapid generation speeds and high fidelity to input
  images - two features essential for practical applications. In this paper, we
  present One-2-3-45++, an innovative method that transforms a single image into
  a detailed 3D textured mesh in approximately one minute. Our approach aims to
  fully harness the extensive knowledge embedded in 2D diffusion models and
  priors from valuable yet limited 3D data. This is achieved by initially
  finetuning a 2D diffusion model for consistent multi-view image generation,
  followed by elevating these images to 3D with the aid of multi-view conditioned
  3D native diffusion models. Extensive experimental evaluations demonstrate that
  our method can produce high-quality, diverse 3D assets that closely mirror the
  original input image. Our project webpage:
  https://sudo-ai-3d.github.io/One2345plus_page.
paper_ai_keywords: diffusion models multi-view image generation 3D native diffusion models masked modeling fine-tuning continual pretraining multi-view conditioned 3D