Title :

Prediction of garment fit level in 3D virtual environment based on artificial neural networks

Author(s) :

Wang, Z. J. [Auteur]
Wang, J. P. [Auteur]
Zeng, Xianyi [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Sharma, Shukla [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Xing, Y. M. [Auteur]
Xu, S. [Auteur]
Li, L. [Auteur]

Journal title :

Textile Research Journal

Abbreviated title :

Text. Res. J.

Volume number :

-

Pages :

-

Publication date :

2021-08-14

ISSN :

0040-5175

English keyword(s) :

Garment computer-aided design (CAD)
garment fit prediction
probabilistic neural networks
ease allowance
digital clothing pressure

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

This paper proposes a probabilistic neural network-based model for predicting and controlling garment fit levels from garment ease allowances, digital pressures, and fabric mechanical properties measured in a three-dimensional ...
Show more >This paper proposes a probabilistic neural network-based model for predicting and controlling garment fit levels from garment ease allowances, digital pressures, and fabric mechanical properties measured in a three-dimensional (3D) virtual environment. The predicted fit levels include both comprehensive and local fit levels. The model was set up by learning from data measured during a series of virtual (input data) and real try-on (output data) experiments and then simulated to predict different garment styles, for example, loose and tight fits. Finally, the performance of the proposed model was compared with the Linear Regression model, the Support Vector Machine model, the Radial Basis Function Artificial Neural Network model, and the Back Propagation Artificial Neural Network model. The results of the comparison revealed that the prediction accuracy of the proposed model was superior to those of the other models. Furthermore, we put forward a new interactive garment design process in a 3D virtual environment based on the proposed model. Based on interactions between real pattern adjustments and virtual garment demonstrations, this new design process will enable designers to rapidly, accurately, and automatically predict relevant garment fit levels without undertaking expensive and time-consuming real try-ons.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

• Génie des matériaux textiles (GEMTEX) - ULR 2641

Submission date :

2023-06-20T11:52:42Z
2024-03-21T07:53:18Z