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Related Experiment Video

Updated: Sep 5, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

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Published on: May 10, 2024

3D Garment Design Model Based on Convolution Neural Network and Virtual Reality.

Liu Fengyi¹, Siru Liu¹

¹Lu Xun Academy of Fine Arts, Liaoning, Dalian 116021, China.

Computational Intelligence and Neuroscience

|July 7, 2022

Summary

This summary is machine-generated.

This study introduces a novel 3D clothing design model using virtual reality and convolution neural networks. The model demonstrates fast training, high accuracy, and practical wearability for virtual fitting.

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Area of Science:

Fashion Design
Computer Science
Virtual Reality

Background:

Virtual reality (VR) technology is transforming fashion design.
3D clothing design faces challenges in accuracy and fit prediction.

Purpose of the Study:

To develop a 3D clothing design model integrating convolution neural networks (CNNs) and VR.
To address key technologies and research difficulties in 3D fashion design.

Main Methods:

Designed a CNN structure for 3D clothing design.
Constructed a VR-based 3D clothing design model.
Evaluated model performance using error rates, matching rates, prediction accuracy, and pressure values.

Main Results:

The CNN model achieved rapid training with an average error of 0.48% and maximum error of 1.63%.
Increased parameters improved body-garment matching rates, stabilizing around 0.9.
Prediction accuracy reached 92.689% with minimal loss.

Conclusions:

The developed CNN-based VR model offers efficient and accurate 3D clothing design.
Virtual fitting experiments confirmed the wearability and rationality of automatically generated garment fits.