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

• URL: http://arxiv.org/abs/2409.03718v1
• Date: Thu, 5 Sep 2024 17:21:54 GMT
• Title: Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with Image-Based Surface Representation
• Authors: Slava Elizarov, Ciara Rowles, Simon Donné,
• Abstract summary: GIMDiffusion is a novel Text-to-3D model that utilizes geometry images to efficiently represent 3D shapes using 2D images. We exploit the rich 2D priors of existing Text-to-Image models such as Stable Diffusion. In short, GIMDiffusion enables the generation of 3D assets at speeds comparable to current Text-to-Image models.
• Score: 2.3213238782019316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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