Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with Image-Based Surface Representation

Slava Elizarov, Ciara Rowles, Simon Donné

Keywords: Text-to-3D; Geometry Image; Non-watertight mesh; Data efficiency

TL;DR: We propose a fast and data-efficient Text-to-3D model using geometry images as the surface representation.

Abstract: Generating high-quality 3D objects from textual descriptions remains a challenging problem due to high computational costs, the scarcity of 3D data, and the complexity of 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, to achieve strong generalization despite limited 3D training data. This allows us to use only high-quality training data while retaining compatibility with guidance techniques such as IPAdapter. GIMDiffusion enables the generation of 3D assets at speeds comparable to current Text-to-Image models, without being restricted to manifold meshes during either training or inference. We simultaneously generate a UV unwrapping for the objects, consisting of semantically meaningful parts as well as internal structures, enhancing both usability and versatility.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 10401