Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation (2303.13873v3)

Abstract: Automatic 3D content creation has achieved rapid progress recently due to the availability of pre-trained, LLMs and image diffusion models, forming the emerging topic of text-to-3D content creation. Existing text-to-3D methods commonly use implicit scene representations, which couple the geometry and appearance via volume rendering and are suboptimal in terms of recovering finer geometries and achieving photorealistic rendering; consequently, they are less effective for generating high-quality 3D assets. In this work, we propose a new method of Fantasia3D for high-quality text-to-3D content creation. Key to Fantasia3D is the disentangled modeling and learning of geometry and appearance. For geometry learning, we rely on a hybrid scene representation, and propose to encode surface normal extracted from the representation as the input of the image diffusion model. For appearance modeling, we introduce the spatially varying bidirectional reflectance distribution function (BRDF) into the text-to-3D task, and learn the surface material for photorealistic rendering of the generated surface. Our disentangled framework is more compatible with popular graphics engines, supporting relighting, editing, and physical simulation of the generated 3D assets. We conduct thorough experiments that show the advantages of our method over existing ones under different text-to-3D task settings. Project page and source codes: https://fantasia3d.github.io/.

Disentangling Geometry and Appearance in Text-to-3D Content Creation: Fantasia3D

The paper "Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation" presents an innovative approach to automatic 3D content creation using text prompts. The authors propose a novel method, Fantasia3D, which focuses on disentangling the geometry and appearance modeling processes to improve the quality of generated 3D assets. This method addresses limitations in existing techniques by introducing a hybrid scene representation, providing significant advancements in the photorealistic rendering of 3D objects.

The core contribution of Fantasia3D lies in its distinct approach to modeling geometry and appearance separately, facilitating high-quality surface recovery and material rendering. The authors employ a hybrid scene representation via DMTet, integrating deformable tetrahedral grids and differentiable mesh extraction to control shape generation effectively. This approach contrasts with traditional methods relying on implicit Neural Radiance Fields (NeRF), which often merge geometry and appearance generation, leading to suboptimal results.

For geometry learning, Fantasia3D utilizes a rendered normal map within the input shape encoding of an image diffusion model, leveraging powerful pre-trained resources like stable diffusion. This technique directly addresses the limitations of using color images, as done in prior work, and supports enhanced surface detail and accuracy.

In modeling appearance, the paper introduces the spatially varying Bidirectional Reflectance Distribution Function (BRDF) into the text-to-3D task for the first time, allowing for more sophisticated material definition and support for photorealistic rendering. The material parameters are predicted through simple MLPs, providing a robust framework for generating realistic textures and lighting effects.

The flexibility of Fantasia3D is demonstrated through its ability to accommodate user-guided inputs, allowing for the customization of initial 3D shapes. This feature empowers users to control generated content, contrasting with purely text-driven approaches. The generated 3D assets, characterized by their high-quality geometry and materials, are readily compatible with commonly used graphics engines, facilitating applications in relighting, editing, and physical simulation.

From an experimental standpoint, Fantasia3D shows marked improvements over existing solutions in text-to-3D content creation. The thorough evaluations highlight its superior capability in both zero-shot and user-guided settings, emphasizing the method's adaptability and effectiveness. The authors report that Fantasia3D outperforms state-of-the-art techniques in terms of detail and photorealism in generated 3D assets.

The implications of this research extend to several domains, including virtual reality, gaming, and entertainment, where high-quality 3D asset generation is paramount. The disentangled framework not only enhances the visual fidelity of generated content but also aligns with contemporary graphics architectures, suggesting a promising direction for future developments in AI-driven 3D content creation.

Looking forward, the research could inspire further exploration into integrating 3D diffusion models learned directly from LLMs to enhance the synthesis capabilities of Fantasia3D. Additionally, addressing more complex generation tasks, such as full scene synthesis and intricate loose geometries, represents an exciting avenue for further refinement of the text-to-3D paradigm.