3D Texture Generation from Text via Neural Network Optimization
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Solution Overview
Problem
Producing 3D textures is challenging due to the complexity of existing 3D modeling software, and there is a demand for generating 3D textures from textual descriptions, which is hindered by the computational requirements of training generative AI systems.
Innovation Solution
A method using a generative neural network trained to generate 2D images from input text, where an initial 3D texture is updated based on the output of the neural network compared with the 3D model rendered with the initial texture, iteratively improving the texture through backpropagation and noise addition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing 3D modeling software is used to produce 3D textures, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical 3D modeling software operations with a neural network-based generative AI system. The neural network automatically generates 3D textures from text descriptions, eliminating the need for users to navigate complex 3D modeling software interfaces and workflows.
Solution Approach 2:
The system enables self-service texture generation where the neural network autonomously creates 3D textures based on text input without requiring user expertise in 3D modeling. The iterative optimization process automatically refines textures by comparing rendered images with target images, making the system self-correcting and user-friendly.
2Ease of operation
If generative AI systems are trained to generate 3D textures from text, then ease of operation is improved, but use of energy increases
Solution Approach 1:
The patent segments the 3D texture generation process into distinct components: text encoding, iterative optimization loops, rendering steps, and loss calculation. This segmentation allows for targeted optimization of energy-consuming operations and enables the system to process textures efficiently in manageable stages.
Solution Approach 2:
The system performs partial optimization by allowing users to control the number of optimization iterations. Users can choose to stop the iterative process early when sufficient quality is achieved, reducing energy consumption for applications that don't require maximum optimization. The system provides diminishing returns analysis to help users decide when to stop iterations.
3Manufacturing precision
If iterative optimization is performed to improve 3D texture quality, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent implements periodic action through iterative optimization loops that systematically refine 3D textures over multiple passes. Each iteration periodically updates the texture based on loss calculations from rendered comparisons, ensuring progressive improvement toward the target appearance while maintaining a structured, time-efficient workflow.
Solution Approach 2:
The system allows partial optimization by enabling users to specify the number of optimization iterations based on their time constraints and quality requirements. The diminishing returns characteristic of iterative optimization means users can achieve satisfactory results with fewer iterations, reducing time loss while maintaining acceptable quality levels for many applications.
Data Source
AI summary
A three-dimensional (3D) texture is generated for an input 3D model based on input text describing the desired texture. Example methods include rendering the 3D model and trainable 3D texture to generate a first two-dimensional (2D) image, adding noise to the first 2D image to generate a first 2D image with added noise, and inputting the first 2D image with added noise and input text into a trained neural network to generate a predicted noise of the first 2D image with added noise. The methods further include determining a loss between the first 2D image with added noise and the predicted noise and updating the trainable 3D texture based on the loss. The method is repeated for a number of time or until a loss between the first 2D image and the predicted noise transgresses a threshold.