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Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with Image-Based Surface Representation (2409.03718v1)

Published 5 Sep 2024 in cs.CV and cs.GR

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.

Summary

  • The paper introduces Geometry Image Diffusion (GIMDiffusion), a novel approach that generates 3D objects from text by encoding shapes as 2D images.
  • It achieves rapid generation (under 10 seconds per object) and high fidelity using minimal 3D training data and robust 2D priors.
  • The method streamlines 3D workflows by outputting separate texture and geometry components, eliminating complex post-processing steps.

Geometry Image Diffusion: Text-to-3D with Image-Based Surface Representation

The paper "Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with Image-Based Surface Representation" introduces Geometry Image Diffusion (GIMDiffusion), a novel approach to generating 3D objects from textual descriptions. This method addresses some longstanding challenges in the field of 3D object generation, including computational expense, data scarcity, and complications inherent in traditional 3D representations.

GIMDiffusion suggests using a geometry image representation, which encodes 3D shapes as 2D images. This choice allows the leveraging of existing robust 2D architectures, circumventing the need to develop intricate 3D-aware models. Significant emphasis is placed on utilizing the rich 2D priors of Text-to-Image models, such as Stable Diffusion. The integration of a Collaborative Control mechanism facilitates this process, enabling the reuse of pre-trained priors to achieve strong generalization capabilities even with a limited 3D training dataset. This ultimately results in the ability to generate complex and semantically rich 3D assets rapidly.

One of the core strengths of GIMDiffusion is its compatibility and operational speed. The authors report generation times of under 10 seconds per object, which is on par with current Text-to-Image models. This efficiency does not sacrifice quality; the generated meshes are detailed and contain separate, semantically meaningful parts. They include internal structures and are artifact-free when moving through conventional graphic pipelines.

Another significant advantage of GIMDiffusion lies in the practical aspect of its output. The method produces 3D objects with separate texture and geometry components, enabling more straightforward post-processing. Unlike many existing methods, GIMDiffusion does not require iso-surface extraction algorithms or UV unwrapping, simplifying the workflow considerably.

From a theoretical perspective, the choice of using geometry images, particularly multi-chart geometry images, provides flexibility and high fidelity. These representations allow for an almost uniform triangulation over the surface, preserving the details and topology necessary for high-quality rendering. As a result, the generated objects are highly editable, aligning with practical needs in areas such as video game production and animation.

The paper also positions GIMDiffusion within the broader landscape of recent methodologies. It differentiates itself from optimization-based and feed-forward approaches by not requiring pre-trained models to be fine-tuned for 3D but instead repurposing powerful existing 2D diffusion models.

Moreover, the practical potential of the system is enhanced by its compatibility with guidance techniques such as IPAdapter, allowing modifications to the style of the generated 3D objects using image-based conditioning. This capability is crucial for applications that demand stylistic consistency with existing assets.

While the paper highlights several key advantages, it does acknowledge limitations, particularly in seam artifact issues that arise at the boundaries of charts within geometry images. These issues can, however, be manually corrected due to the separable nature of the generated parts.

In summary, GIMDiffusion opens new pathways for research and application in the field of Text-to-3D synthesis by proposing an efficient, data-effective method that leverages existing 2D architectures. The implications are significant for future developments in AI and 3D content generation, providing a framework that balances complexity, quality, and speed. This research has the potential to catalyze further innovations in generating visually and semantically rich 3D content at scale, fostering advancements across various industries, from animation to architecture.

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