Improving the Apparel Virtual Size Fitting Prediction under Psychographic Characteristics and 3D Body Measurements Using Artificial Neural Network

Abstract: Background3D virtual simulation prototyping software combined with computer-aided manufacturing systems are widely used and are becoming essential in the fashion industry in the earlier stages of the product development process for apparel design. These technologies streamline the garment product fitting procedures, as well as improve the supply chain environmentally, socially, and economically by eliminating large volumes of redundant samples. Buyers can easily evaluate virtual samples that are showcased with full rotation views and visual draping effects without relying on physical prototypes before confirming orders. The approved designs can be transferred to the production line immediately, which shortens the communication, development, and production lead time between suppliers and buyers. Issues of non-standardized selection on garment sizing, ease allowance, and size of 3D avatar for creating 3D garments have been addressed by many researchers. Understanding the relationship between body dimensions, ease allowance, and apparel sizes before adopting virtual garment simulation is fundamental for satisfying high customer demands in the apparel industry. However, designers find difficulties providing the appropriate garment fit for customers without fully understanding the motivation and emotions of customers’ fitting preferences in a virtual world.A statement of objective The main purpose of this study is to investigate apparel sizes for virtual fitting, particularly looking at garment ease with consideration to body dimensions and the psychographic characteristics of subjects.SignificanceThe quantitative relationship between the pattern measurements, psychological characteristics, and 3D body measurements contributes to improving virtual fit predictions for implementing mass customization in the apparel industry. This new approach and the proposed method of virtual garment fitting model prediction on garment sizes using an Artificial Neural Network (ANN) is significant in prediction accuracy. The results of this project provide sustainable value in providing an ideal communication tool between manufacturers, retailers, and consumers by offering “perfect fit” products to customers. The project will also achieve the concept of mass customization and customer orientation, and generate new size fitting data that could bring a new level of end-user satisfaction.MethodsThe study proposes to develop a virtual garment fitting prediction model using an ANN for improving virtual garment design in terms of its fitting and sizing. The project investigated apparel sizes for virtual fitting with consideration of body dimensions and psychographic characteristics of subjects on garment ease for improving the size prediction of 3D garments. We recruited 50 subjects between the ages 18-35 years old to conduct 3D body scans and a questionnaire survey for physical and psychological segmentation, as well as fitting preferences evaluation through co-design operations on virtual garment simulation using a commercial software called Optitex. Discussion of resultsThe ease preferences from subjects were significantly different from the preset values on the software. The results from the study demonstrate that ANN is effective in modeling the non-linear relationship between pattern measurements, psychological characteristics, and body measurements. The pattern parameters predicted by the ANN model were accurate. The squared correlation coefficient (R2) increased from 0.96 to 0.99 after considering different segmentations of psychographic characteristics. The ANN prediction model is proven to be an effective method for garment pattern drafting, which can achieve an individual fit and is useful for implementing the virtual fitting model.

Keywords: 3D Virtual Garment Fitting, Artificial Neural Network, Artificial Intellignece, Ease Allowances, Psychographic Characteristic, 3D Body Measurements

DOI: 10.54941/ahfe1001543

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