Predicting Virtual Garment Fitting Size with Psychographic Characteristics and 3D Body Measurements Using Artificial Neural Network and Visualizing Fitted Bodies Using Generative Adversarial Network

3D virtual garment simulation technology is widely used in apparel industry nowadays with computer-aided manufacturing systems for the earlier stages of apparel design and product development process. The technological advances have brought convenience in garment product fitting procedures with virtual fitting environment, and eventually enhance the supply chain in the aspects of social, economic, and environmental aspects. Many studies have addressed the matters related to non-standardized selection on garment sizing, ease allowance for different selected groups, and use of 3D avatars for virtual fitting in the design and pre-production stages. Nevertheless, the current practice for designers is difficult for them to recognize the customers’ motivation and emotions towards their preferred fit in the virtual environment, leading to a hard time for the designers to determine the appropriate ease allowances for the end users. The present study is to investigate the variations on the ease preferences for the apparel sizes according to the body dimensions and psychological orientation of the subjects by developing a virtual garment fitting prediction model using artificial neural network (ANN). One hundred and twenty adult subjects were recruited to conduct 3D body scans and questionnaire survey for retrieving their body dimensions and psychographic characteristics. Segmentations were performed and each cluster was asked to evaluate the fitting preferences in a co-design interview on virtual garment simulation with a commercial software called Optitex. The results demonstrated that the ANN model is effective in predicting ease preferences from the body measurements and the psychological orientation of the subjects with high correlation coefficients, showing that a non-linear relationship is modelled among pattern parameters, body dimensions and psychographic characteristics. The results were visualized using generative adversarial network (GAN) to generate 3D samples. This new approach is significant to predict the garment sizes and pattern parameters with a highly accurate ANN model. Visualization of the predicted size with the implementation of GAN model is valuable to envision the garment details from 2D to 3D. The project has achieved the conception of mass customization and customer orientation by providing the perfect fit to the end users. Eventually, new size fitting data is generated for improved ease preference charts and augments end-user satisfaction in garment fit.