Analysing the Effectiveness of a Generative Adversarial Network Model for the Creation of New Datasets of 3D Human Body and Garment Sizes in the Clothing Industry

Abstract

Apparel designers and manufacturers are now using virtual garment simulation technology to evaluate 3D prototypes in virtual environments, which reduces the waste of raw materials in the sampling process. With the immediate visualization of the 3D prototypes, designers and manufacturers can communicate with each other to adjust the virtual garments seamlessly, and thereby the production process has become simplified using simulation technologies. Nevertheless, there are several limitations to the current practice. Apparel companies do not have a universal sizing standard, which leads to problems because customers need to identify their sizes in different stores, and a high return rate is expected in this case. Additionally, the psychological preferences of the wearers are not taken into account when evaluating fit. Production teams in apparel companies are only concerned with the physical fit of their targeted customer groups; they neglect the actual will of a specific customer. For example, some may like to wear oversized garments, and a just-fit size is not what they want. It is valuable to find a method to adapt the psychographic orientations of the customers to the design and production process of a garment. Therefore, we had proposed our method for developing a virtual garment fitting prediction model to predict the garment pattern parameters with anthropometric data and psychographic orientations of subjects, and previous work had proven that the prediction model has high accuracy and stability. Nevertheless, a limitation was found in that the subject data was difficult to obtain. It would be advantageous if there were more data to test in the prediction model. Thus, this study proposes the build of generative adversarial network (GAN) models to generate new body dimension data and garment parameter data. The new datasets produced by the GAN models would be favourable for an improvement in the virtual garment fitting prediction model with more training and testing data to be processed. Moreover, the synthetic datasets can be employed by designers to do research in their garment evaluation process since they have more data on similar body dimensions and preferred garment sizes to assess. A more comprehensive appraisal of the garment fit can be attained by this approach, which accelerates the design process in the apparel production stage.

Keywords: Body measurements and psychographic preferences, generative adversarial networks, 3D virtual clothing simulation

DOI: 10.54941/ahfe1004202