An evaluation of garment fit to improve customer body fit of fashion design clothing

Currently, garment fit evaluation is one of the biggest bottlenecks for fashion design and manufacturing. In this paper, we proposed a garment fit prediction model using data learning technology based on Artificial Neural Networks. The inputs of the proposed model are digital clothing pressures measured by virtual try-on, while the output of the model is one of the three fit conditions-tight, fit, or loose. To acquire reliable learning data, virtual and real try-on experiments were carried out to collect input and output learning data, respectively. We collected 72 samples, each sample contains 20 clothing virtual pressure values and the corresponding fit values of the garment. After learning from the collected input and output experimental data, the proposed model can predict garment fit rapidly and automatically by inputting digital clothing pressures measured by virtual try-on. Test results showed that the prediction accuracy of fit evaluation model based on Back Propagation Artificial Neural Networks (BP-ANNs) is 93%. Compared with the 50% prediction accuracy of the traditional method, our proposed method has obvious advantages. This technology can be applied to the process of garment design and manufacturing to improve work efficiency.