Text-to-3D Mesh Generation With Coarse-to-Fine Diffusion
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Solution Overview
Problem
Existing text-to-3D content creation technologies are limited by the lack of high-resolution synthesis capabilities and inefficient Multilayer Perceptron (MLP) architectures for Neural Radiance Field (NeRF) representations, which restrict the generation of high-frequency 3D geometric and texture details.
Innovation Solution
A method involving a two-stage coarse-to-fine diffusion model process is employed, using a neural field representation for coarse optimization and textured meshes for fine optimization, allowing for high-resolution 3D mesh generation from text prompts, with a deformable tetrahedral grid and neural color field for texture representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a pretrained text-to-image diffusion model is used as a prior for text-conditioned 3D content generation, then the generation process can be enabled, but the model cannot synthesize high-frequency 3D geometric and texture details due to low resolution training
Solution Approach 1:
The patent segments the 3D content generation process into two distinct stages: a first diffusion model for generating low-resolution 3D content (coarse structure), and a second diffusion model for generating high-resolution 3D content (fine details). This segmentation allows each model to be optimized for its specific resolution requirement, resolving the contradiction between enabling generation and achieving high-frequency detail synthesis.
Solution Approach 2:
The patent transitions from a single-resolution approach to a multi-resolution approach by introducing a hierarchical generation process. The first model operates at low resolution to establish coarse geometry, while the second model operates at high resolution to add fine geometric and texture details, effectively adding a resolution dimension to the generation process.
2Manufacturing precision
If an MLP architecture for NeRF representation is used, then text-conditioned 3D content generation can be performed, but memory footprint and computation budget grow quickly with resolution
Solution Approach 1:
The patent segments the resolution requirements into two levels, assigning different computational approaches to each. The first diffusion model handles low-resolution generation with simpler computational requirements, while the second diffusion model handles high-resolution generation with optimized memory usage. This segmentation prevents the computational budget from growing excessively with resolution.
Solution Approach 2:
The patent changes the resolution parameter between the two stages of generation. The first model operates at a lower resolution with corresponding computational parameters, while the second model operates at a higher resolution with optimized parameters. This parameter change allows high-resolution synthesis without the computational cost growing linearly with resolution.
3Device complexity
If a single diffusion model is used for both low and high resolution generation, then the process is simplified, but high-frequency 3D geometric and texture details cannot be synthesized
Solution Approach 1:
The patent divides the generation process into two specialized diffusion models: the first for low-resolution coarse structure and the second for high-resolution fine details. This segmentation resolves the contradiction by accepting increased process complexity as the necessary trade-off to achieve high-frequency detail synthesis, which a single model cannot provide.
Solution Approach 2:
The first diffusion model acts as an intermediary that generates low-resolution 3D content which then serves as input conditioning for the second diffusion model. This intermediary process enables the second model to focus specifically on adding high-frequency details without having to learn both coarse and fine structures simultaneously.
Data Source
AI summary
Text-to-image generation generally refers to the process of generating an image from one or more text prompts input by a user. While artificial intelligence has been a valuable tool for text-to-image generation, current artificial intelligence-based solutions are more limited as it relates to text-to-3D content creation. For example, these solutions are oftentimes category-dependent, or synthesize 3D content at a low resolution. The present disclosure provides a process and architecture for high-resolution text-to-3D content creation.