title: YouDream: Generating Anatomically Controllable Consistent Text-to-3D
    Animals
summary: 3D generation guided by text-to-image diffusion models enables the creation
  of visually compelling assets. However previous methods explore generation
  based on image or text. The boundaries of creativity are limited by what can be
  expressed through words or the images that can be sourced. We present YouDream,
  a method to generate high-quality anatomically controllable animals. YouDream
  is guided using a text-to-image diffusion model controlled by 2D views of a 3D
  pose prior. Our method generates 3D animals that are not possible to create
  using previous text-to-3D generative methods. Additionally, our method is
  capable of preserving anatomic consistency in the generated animals, an area
  where prior text-to-3D approaches often struggle. Moreover, we design a fully
  automated pipeline for generating commonly found animals. To circumvent the
  need for human intervention to create a 3D pose, we propose a multi-agent LLM
  that adapts poses from a limited library of animal 3D poses to represent the
  desired animal. A user study conducted on the outcomes of YouDream demonstrates
  the preference of the animal models generated by our method over others.
  Turntable results and code are released at https://youdream3d.github.io/
paper_authors._id: 667b8d11bfe251ebedad28aa
paper_authors.name: Sandeep Mishra
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-06-26T04:05:41.987Z
paper_authors._id: 6635048a7523759ca1355b22
paper_authors.avatarUrl: /avatars/644dfa9eb489229c5e809d574099d759.svg
paper_authors.fullname: Sandeep Mishra
paper_authors.type: user
paper_authors.user: battleMaster
paper_authors._id: 667b8d11bfe251ebedad28ab
paper_authors.name: Oindrila Saha
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-06-26T03:37:58.089Z
paper_authors._id: 6635368639a7401f16fdec55
paper_authors.avatarUrl: /avatars/5a1790b5e1e54b18363f14ef699a8b21.svg
paper_authors.fullname: Oindrila Saha
paper_authors.type: user
paper_authors.user: oindrila13saha
paper_authors._id: 667b8d11bfe251ebedad28ac
paper_authors.name: Alan C. Bovik
paper_title: YouDream: Generating Anatomically Controllable Consistent Text-to-3D
    Animals
paper_summary: 3D generation guided by text-to-image diffusion models enables the creation
  of visually compelling assets. However previous methods explore generation
  based on image or text. The boundaries of creativity are limited by what can be
  expressed through words or the images that can be sourced. We present YouDream,
  a method to generate high-quality anatomically controllable animals. YouDream
  is guided using a text-to-image diffusion model controlled by 2D views of a 3D
  pose prior. Our method generates 3D animals that are not possible to create
  using previous text-to-3D generative methods. Additionally, our method is
  capable of preserving anatomic consistency in the generated animals, an area
  where prior text-to-3D approaches often struggle. Moreover, we design a fully
  automated pipeline for generating commonly found animals. To circumvent the
  need for human intervention to create a 3D pose, we propose a multi-agent LLM
  that adapts poses from a limited library of animal 3D poses to represent the
  desired animal. A user study conducted on the outcomes of YouDream demonstrates
  the preference of the animal models generated by our method over others.
  Turntable results and code are released at https://youdream3d.github.io/
paper_ai_keywords: text-to-image diffusion models 3D generation anatomically controllable animals 2D views 3D pose prior multi-agent LLM turntable results