title: MetaDreamer: Efficient Text-to-3D Creation With Disentangling Geometry
    and Texture
summary: Generative models for 3D object synthesis have seen significant advancements
  with the incorporation of prior knowledge distilled from 2D diffusion models.
  Nevertheless, challenges persist in the form of multi-view geometric
  inconsistencies and slow generation speeds within the existing 3D synthesis
  frameworks. This can be attributed to two factors: firstly, the deficiency of
  abundant geometric a priori knowledge in optimization, and secondly, the
  entanglement issue between geometry and texture in conventional 3D generation
  methods.In response, we introduce MetaDreammer, a two-stage optimization
  approach that leverages rich 2D and 3D prior knowledge. In the first stage, our
  emphasis is on optimizing the geometric representation to ensure multi-view
  consistency and accuracy of 3D objects. In the second stage, we concentrate on
  fine-tuning the geometry and optimizing the texture, thereby achieving a more
  refined 3D object. Through leveraging 2D and 3D prior knowledge in two stages,
  respectively, we effectively mitigate the interdependence between geometry and
  texture. MetaDreamer establishes clear optimization objectives for each stage,
  resulting in significant time savings in the 3D generation process. Ultimately,
  MetaDreamer can generate high-quality 3D objects based on textual prompts
  within 20 minutes, and to the best of our knowledge, it is the most efficient
  text-to-3D generation method. Furthermore, we introduce image control into the
  process, enhancing the controllability of 3D generation. Extensive empirical
  evidence confirms that our method is not only highly efficient but also
  achieves a quality level that is at the forefront of current state-of-the-art
  3D generation techniques.
paper_authors._id: 655ac4d04a49906b2c7f5aed
paper_authors.name: Lincong Feng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-20T09:31:54.232Z
paper_authors._id: 64fecad241444912cdd07b5d
paper_authors.avatarUrl: /avatars/a5953a31850e18afd16437c92a86ba67.svg
paper_authors.fullname: Lincong Feng
paper_authors.type: user
paper_authors.user: Lincong
paper_authors._id: 655ac4d04a49906b2c7f5aee
paper_authors.name: Muyu Wang
paper_authors._id: 655ac4d04a49906b2c7f5aef
paper_authors.name: Maoyu Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-20T10:05:48.878Z
paper_authors._id: 64c097f5f613170e7bea2fe4
paper_authors.avatarUrl: /avatars/0b1bfbf3d119791a02208d13d0c9455a.svg
paper_authors.fullname: Wang Maoyu
paper_authors.type: user
paper_authors.user: bstkael
paper_authors._id: 655ac4d04a49906b2c7f5af0
paper_authors.name: Kuo Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-20T10:05:36.965Z
paper_authors._id: 65499cd0a46359771485ed62
paper_authors.avatarUrl: /avatars/cc00354891814e9be61af1fa8f2a0baf.svg
paper_authors.fullname: KuoXu
paper_authors.type: user
paper_authors.user: XKuo
paper_authors._id: 655ac4d04a49906b2c7f5af1
paper_authors.name: Xiaoli Liu
paper_title: MetaDreamer: Efficient Text-to-3D Creation With Disentangling Geometry
    and Texture
paper_summary: Generative models for 3D object synthesis have seen significant advancements
  with the incorporation of prior knowledge distilled from 2D diffusion models.
  Nevertheless, challenges persist in the form of multi-view geometric
  inconsistencies and slow generation speeds within the existing 3D synthesis
  frameworks. This can be attributed to two factors: firstly, the deficiency of
  abundant geometric a priori knowledge in optimization, and secondly, the
  entanglement issue between geometry and texture in conventional 3D generation
  methods.In response, we introduce MetaDreammer, a two-stage optimization
  approach that leverages rich 2D and 3D prior knowledge. In the first stage, our
  emphasis is on optimizing the geometric representation to ensure multi-view
  consistency and accuracy of 3D objects. In the second stage, we concentrate on
  fine-tuning the geometry and optimizing the texture, thereby achieving a more
  refined 3D object. Through leveraging 2D and 3D prior knowledge in two stages,
  respectively, we effectively mitigate the interdependence between geometry and
  texture. MetaDreamer establishes clear optimization objectives for each stage,
  resulting in significant time savings in the 3D generation process. Ultimately,
  MetaDreamer can generate high-quality 3D objects based on textual prompts
  within 20 minutes, and to the best of our knowledge, it is the most efficient
  text-to-3D generation method. Furthermore, we introduce image control into the
  process, enhancing the controllability of 3D generation. Extensive empirical
  evidence confirms that our method is not only highly efficient but also
  achieves a quality level that is at the forefront of current state-of-the-art
  3D generation techniques.
paper_ai_keywords: generative models 3D object synthesis 2D diffusion models multi-view geometric inconsistencies optimization geometric a priori knowledge entanglement issue geometric representation texture optimization rich 2D and 3D prior knowledge two-stage optimization approach MetaDreammer textual prompts text-to-3D generation image control high-quality 3D objects state-of-the-art 3D generation techniques