Neural Network Textured 3D Mesh Generation
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Solution Overview
Problem
Existing content generation systems struggle to generate high-quality 3D objects with both arbitrary textures and shapes simultaneously, often requiring expensive and time-consuming high-quality 3D training data, which limits the quantity and quality of generated content.
Innovation Solution
A neural network system is trained to generate textured 3D meshes using only 2D image data, with a geometry generation branch and a texture generation branch, utilizing techniques like 3D signed distance fields and differentiable rendering to produce meshes with arbitrary topology and high-fidelity textures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing content generation systems are used to generate 3D objects with both arbitrary textures and shapes, then the quality of generated content can be improved, but the cost and time required for generating high-quality 3D training data increases significantly
Solution Approach 1:
The patent uses 2D images as copies or projections of 3D objects to train the neural network. Instead of requiring expensive 3D training data, the system learns to reconstruct 3D meshes from 2D image copies, significantly reducing data preparation time and cost while maintaining generation quality
Solution Approach 2:
The patent transforms the problem from 3D to 2D by training the neural network on 2D images rather than 3D data. The network learns to infer 3D geometric structures and textures from 2D projections, eliminating the need for time-consuming 3D data generation while preserving manufacturing precision of the output
2Manufacturing precision
If high-quality 3D training data is used, then the quality of generated 3D objects can be improved, but the cost of generating training data increases significantly
Solution Approach 1:
The system replaces expensive 3D training data with readily available 2D image copies. Neural networks are trained on 2D images that serve as projections of 3D objects, learning to reconstruct accurate 3D meshes without requiring costly 3D scanned or modeled training data
Solution Approach 2:
The patent uses inexpensive 2D images as training data instead of expensive 3D data. These 2D images can be obtained from standard photograph collections or web sources, dramatically reducing the quantity of substance (cost) required for training while maintaining output quality
3Adaptability or versatility
If existing generators are used to generate 3D content, then arbitrary textures can be generated, but arbitrary shapes and complex geometry cannot be generated simultaneously with high quality
Solution Approach 1:
The patent segments the generation process into distinct neural network components: one branch handles geometry generation (mesh structure, topology, complex shapes) while another branch handles texture generation (surface appearance, colors, patterns). This segmentation allows each component to specialize and achieve high quality in its respective domain simultaneously
Solution Approach 2:
By training on 2D images rather than 3D data, the system gains flexibility in learning complex geometric relationships and texture patterns from multiple viewing angles. The 2D-to-3D transformation enables the network to infer arbitrary shapes and textures that would be difficult to capture with traditional 3D training data
Data Source
AI summary
Apparatuses, systems, and techniques are presented to generate digital content. In at least one embodiment, one or more neural networks are used to generate one or more textured three-dimensional meshes corresponding to one or more objects based, at least in part, one or more two-dimensional images of the one or more objects.