Generative Garment Imaging With AR Feedback for Fabrication Decisions
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Solution Overview
Problem
Designing apparel items without knowing market demand poses a significant challenge for designers, as it requires expensive and time-consuming processes to create high-quality images for market research, adding complexity and inefficiency to garment fabrication.
Innovation Solution
Utilizing machine learning techniques to automate the process of creating virtual fashion items through generative models, allowing for AR experiences that enable user feedback to determine real-world garment production, thereby optimizing design and fabrication processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual processes are used to create high-quality images for market research, then image quality can be ensured, but time consumption and expense increase significantly
Solution Approach 1:
The patent uses generative machine learning models to create virtual images that copy and represent real-world fashion items. These synthetic images serve as substitutes for expensive, time-consuming professional photography while maintaining sufficient quality for market research purposes. The virtual fashion items are generated through AI models that replicate the visual characteristics of actual garments.
Solution Approach 2:
The patent replaces manual mechanical processes (physical garment photography, manual image editing, and traditional market research methods) with automated machine learning systems. The generative models automatically create high-quality images without human intervention, substituting the mechanical workflow with intelligent automation that reduces both time and cost.
2Manufacturing precision
If traditional manual design processes are used, then design quality can be maintained, but the complexity and time required for garment fabrication increase
Solution Approach 1:
The patent utilizes optimization problems with adjustable parameters to generate fashion item designs. By modifying parameters such as style attributes, color schemes, and design features, the system can rapidly generate multiple design variations. This parameter-based approach allows for precise control over design quality while automating the complex fabrication process, reducing manual intervention requirements.
3Productivity
If virtual fashion items are generated using machine learning, then time and expense are reduced, but the need for accurate market feedback mechanisms becomes more critical
Solution Approach 1:
The patent implements a feedback mechanism where user interactions with augmented reality fashion items are collected and used to refine the generative models. The system tracks user preferences, engagement metrics, and selection patterns to gather market feedback. This feedback loop ensures that the virtual fashion items generated align with actual consumer preferences, preventing information loss despite the automated generation process.
Data Source
AI summary
Methods and systems are disclosed for generating a physical garment using a machine learning model. The methods and systems receive a plurality of parameters of an optimization problem, the plurality of parameters describing a fashion item, and form a prompt based on values of the plurality of parameters. The prompt is processed by a generative machine learning model to output an image comprising an artificial fashion item corresponding to the values of the plurality of parameters. An augmented reality experience is generated in which a real-world object is overlaid with a virtual object that depicts the artificial fashion item. Feedback associated with the augmented reality experience is used to condition fabrication of a real-world fashion item that resembles the artificial fashion item.