YouDream: Generating Anatomically Controllable Consistent Text-to-3D
  Animals
Authors: name: Sandeep Mishra, name: Oindrila Saha, name: Alan C. Bovik

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/