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

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.