Text-to-3D Latent Code Generation for High-Resolution Shape Detail
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Solution Overview
Problem
Existing text-to-3D content generation technologies face limitations in generating high-resolution and precise 3D shapes due to reliance on categorical models, lack of data, and inefficient computational methods, leading to difficulties in reproducing complex geometric details and high-frequency textures.
Innovation Solution
A method and system utilizing a first machine learning model to compress high-dimensional 3D shape data into a low-dimensional latent code, combined with a second machine learning model to convert text embeddings into latent codes, enabling efficient generation of high-resolution 3D contents through a compact latent code representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If pre-trained text-image diffusion models are used for 3D generation, then automation of content generation is improved, but resolution of generated 3D shapes deteriorates because the model is trained on low-resolution images
Solution Approach 1:
The patent introduces a latent code representation dimension that bridges text embeddings and high-resolution 3D shapes. Instead of directly mapping text to 3D from low-resolution images, the system uses a latent code space as an intermediate representation that can be trained on low-resolution data but generates high-resolution output, effectively adding a dimensional transformation layer.
Solution Approach 2:
The patent changes the resolution parameter through a two-stage process: training on low-resolution images to establish the latent code space, then generating high-resolution 3D shapes from the latent codes. This parameter transformation allows the model to leverage low-resolution training data while producing high-resolution outputs.
2Adaptability or versatility
If Neural Radiance Field (NeRF) is used for 3D representation, then 3D content generation capability is improved, but memory usage and computational costs increase rapidly at high resolutions
Solution Approach 1:
The patent extracts the essential 3D shape information into a compressed latent code representation, separating the critical geometric data from the computationally intensive NeRF rendering process. This extraction allows the system to maintain 3D generation capability while significantly reducing the computational burden for high-resolution outputs.
Solution Approach 2:
The patent creates a simplified latent code copy of the 3D shape that captures essential geometric information without requiring the full computational complexity of NeRF. This latent code serves as an efficient surrogate that can be processed with minimal computational resources while still enabling high-quality 3D rendering when needed.
3Ease of manufacture
If categorical models of 3D objects are used, then training process is simplified, but range of generatable objects is limited
Solution Approach 1:
The patent creates a universal latent code representation that can encode any 3D object shape, replacing category-specific models with a single multi-functional model. The latent code space serves as a universal language that can represent diverse object categories, enabling the system to generate any type of 3D object through text prompts without requiring separate training for each category.
4Manufacturing precision
If high-resolution 3D content generation is pursued, then quality of generated 3D shapes is improved, but data and computational resources required increase
Solution Approach 1:
The patent extracts high-frequency geometric details into the latent code representation, separating the essential shape information from the redundant data. This extraction allows the system to achieve high-resolution 3D shapes by efficiently representing only the critical geometric features in the latent code, reducing the overall data and computational resources needed.
Data Source
AI summary
A method and a computing system including a memory and a processor learn a content generation model. The method may include preparing a training data set including a plurality of pairs of contents and captions, learning a first machine learning model to restore the contents from a low-dimensional latent code, learning a second machine learning model to output a latent code for a text embedding by learning relationship between text embeddings and latent codes of the pairs of the contents and the captions, and combining the first machine learning model and the second machine learning model. The content is implicit data which is function-based 3D shape data.