Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with
  Image-Based Surface Representation
Authors: name: Slava Elizarov, name: Ciara Rowles, name: Simon Donné

Generating high-quality 3D objects from textual descriptions remains a
challenging problem due to computational cost, the scarcity of 3D data, and
complex 3D representations. We introduce Geometry Image Diffusion
(GIMDiffusion), a novel Text-to-3D model that utilizes geometry images to
efficiently represent 3D shapes using 2D images, thereby avoiding the need for
complex 3D-aware architectures. By integrating a Collaborative Control
mechanism, we exploit the rich 2D priors of existing Text-to-Image models such
as Stable Diffusion. This enables strong generalization even with limited 3D
training data (allowing us to use only high-quality training data) as well as
retaining compatibility with guidance techniques such as IPAdapter. In short,
GIMDiffusion enables the generation of 3D assets at speeds comparable to
current Text-to-Image models. The generated objects consist of semantically
meaningful, separate parts and include internal structures, enhancing both
usability and versatility.