Automated Image Generation System for Manufacturing
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Solution Overview
Problem
The manufacturing industries face inefficiencies in production design, particularly in rapidly changing fashion trends, leading to prolonged cycles from style planning to product launch, which hampers their ability to meet fast-paced updating requirements.
Innovation Solution
An image generation system and method that utilizes a text mining component to generate style description texts based on user behavior data and product category information, followed by a text-to-image neural network to produce initial images, and an image-text matching neural network to select high-quality candidate images meeting specific style requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual design and production methods are used, then design quality and control are maintained, but the design and production cycle becomes excessively long
Solution Approach 1:
The patent uses text-to-image generation technology to create virtual product images that copy and represent physical products. This allows designers to visualize and iterate on design concepts digitally without creating physical prototypes, significantly reducing the time required for design validation and production planning while maintaining design quality through automated image generation and filtering
Solution Approach 2:
The patent replaces manual mechanical design processes with automated neural network-based image generation systems. The system automatically generates product images from text descriptions using trained neural networks, eliminating the need for manual sketching, physical prototyping, and repeated modification cycles, thereby dramatically shortening the design cycle
2Productivity
If automated text-to-image generation is used, then production efficiency is improved, but image quality and style matching may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where generated images are evaluated against the original text description using an image-text matching neural network. The system calculates matching degrees between generated images and target styles, automatically filters images that do not meet quality thresholds, and can iteratively refine generation parameters to improve image quality while maintaining high production efficiency
Solution Approach 2:
The patent performs preliminary training of specialized neural networks on target product images and style data before actual image generation. This preliminary action ensures that the generation system is pre-configured with the correct style understanding and quality standards, enabling it to produce high-quality images consistent with target styles from the outset without requiring extensive post-generation adjustments
Data Source
AI summary
Embodiments of this application provide an image generation system and method. In an exemplary manufacturing industry scenario, a style requirement of a product category in a manufacturing industry is automatically captured according to user behavior data and product description information associated with the product category. Based on these data, a style description text may be generated and converted to product images by using a text prediction-based image generation model. The product images are further screened by using an image-text matching model, to obtain a product image with high quality. This process covers from style description text mining to text-to-image prediction to image quality evaluation. It provides an automation product image generation capability for the manufacturing industry, shorten a cycle of designing and producing the product image in the manufacturing industry, and improve production efficiency of the product image.