Neural Network Garment Pattern Arrangement on 3D Avatars
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Solution Overview
Problem
The garment industry faces challenges in efficiently and automatically arranging two-dimensional garment patterns on three-dimensional avatars for simulation, requiring significant manual effort and expertise due to the variability of fabric properties and shapes.
Innovation Solution
A neural network model is employed to predict arrangement points for garment patterns on a 3D avatar by processing pattern information, including sizes, shapes, symmetry, and sewing information, and prioritizing patterns based on confidence scores for automated placement and simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual arrangement of garment patterns on 3D avatars is performed, then arrangement precision and simulation accuracy are improved, but time consumption and operational difficulty increase significantly
Solution Approach 1:
The system enables automatic self-arrangement of garment patterns on 3D avatars through neural network models that predict optimal pattern positions and configurations without requiring manual intervention, thereby reducing time consumption while maintaining arrangement precision
Solution Approach 2:
The patent replaces manual mechanical arrangement operations with an automated neural network-based system that processes pattern information and predicts arrangement points, eliminating the need for manual expertise and time-consuming manual placement
2Manufacturing precision
If manual arrangement of garment patterns is performed, then arrangement accuracy is improved, but ease of operation deteriorates due to lack of expertise requirements
Solution Approach 1:
The neural network model performs self-service by automatically analyzing pattern information and predicting optimal arrangement points without requiring user expertise or manual intervention, making the system easy to operate while maintaining high arrangement accuracy
Solution Approach 2:
The neural network model acts as an intermediary between pattern information and arrangement decisions, automatically processing and interpreting pattern configurations to determine optimal positions, thereby eliminating the need for user expertise while maintaining accuracy
3Loss of time
If automated pattern arrangement using neural network is implemented, then time consumption is reduced, but device complexity increases
Solution Approach 1:
The patent replaces complex manual arrangement processes with a neural network-based automated system that uses machine learning models to predict pattern positions, reducing time consumption while managing system complexity through algorithmic automation
Solution Approach 2:
The system creates a virtual digital representation of the garment arrangement process using neural network models, copying and simulating optimal arrangement patterns through computational methods rather than physical manual manipulation
4Manufacturing precision
If manual pattern arrangement is performed, then simulation accuracy is improved, but productivity deteriorates due to high time requirements
Solution Approach 1:
The neural network model performs self-service automation that maintains simulation accuracy by automatically predicting optimal pattern arrangements, thereby increasing productivity by eliminating time-consuming manual operations while preserving the precision needed for accurate simulation
Data Source
AI summary
An automatic arrangement method and device may receive pattern information for each pattern including shapes and sizes of patterns constituting a garment. Arrangement points at which the patterns are to be initially arranged on a three-dimensional (3D) avatar are predicted by applying the pattern information for each pattern to a neural network model trained to classify and arrange the patterns based on confidence scores calculated based on the pattern information. The patterns are arranged on the 3D avatar based on the arrangement points.