Neural Network Garment Pattern Placement Automation
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Solution Overview
Problem
Existing clothing simulation technologies require manual placement of garment patterns on 3D avatars, which is time-consuming and challenging for users without expertise in clothing design or simulation.
Innovation Solution
A neural network model is used to automatically arrange garment patterns on a 3D avatar by predicting arrangement points based on extracted features from pattern information, and then assembling the patterns into a simulated garment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual arrangement of clothing patterns is used, then users can control pattern placement, but the process is time-consuming and requires expertise
Solution Approach 1:
The system performs automatic pattern arrangement without requiring user intervention. The neural network model independently analyzes pattern information, extracts features, predicts arrangement points, and assembles patterns on the 3D avatar, enabling the system to serve itself rather than requiring manual user operation.
Solution Approach 2:
The patent replaces the manual mechanical process of pattern arrangement with an automated neural network-based system. The neural network model substitutes human expertise and manual manipulation, using machine learning to perform feature extraction, prediction, and assembly tasks that previously required skilled operators.
2Reliability
If manual arrangement of clothing patterns is used, then users can ensure proper fit, but it requires expertise in clothing design or simulation
Solution Approach 1:
The system autonomously performs pattern arrangement tasks without requiring user expertise. The neural network model self-manages the entire process from feature extraction to final assembly, eliminating the need for users to possess specialized knowledge in clothing design or simulation techniques.
Solution Approach 2:
The neural network model acts as an intermediary between the raw pattern information and the final garment assembly. It processes pattern data, extracts relevant features, predicts optimal arrangement points, and generates assembly instructions, serving as an intelligent mediator that translates complex pattern information into actionable placement decisions.
3Productivity
If automatic pattern arrangement is implemented, then time consumption is reduced, but accuracy of pattern placement may decrease
Solution Approach 1:
The patent replaces manual pattern arrangement with an automated neural network system that maintains high precision. The neural network model processes pattern information through multiple layers, extracting hierarchical features and making intelligent predictions about arrangement points, thereby achieving both automation and accuracy simultaneously.
Solution Approach 2:
The system incorporates feedback mechanisms where the neural network model learns from training data consisting of correct pattern arrangements. The model receives feedback during training through loss calculations comparing predicted arrangements with ground truth data, continuously improving its accuracy in predicting arrangement points while maintaining automated operation.
Data Source
AI summary
A clothing simulation method and apparatus are provided. The clothing simulation method includes obtaining pattern information for each of patterns of a garment, the pattern information including information about sample points extracted from each of the patterns, based on an embedding vector for each of the patterns obtained by applying the pattern information to a pattern embedding model trained to estimate a correlation between input pattern information, predicting sewing information about the patterns, and based on the sewing information, generating a simulation result of the garment.