Predicting ready-made garment dressing fit for individuals based on highly reliable examples

Abstract

Predicting the dressing fit of a ready-made garment for different individuals is a challenging problem in online garment sales. At present, the main solution involves physics-based virtual garment simulation, which has limitations in terms of a lack of sufficient reality and high computational costs. In this study, we developed a novel example-based method to guarantee the high reality and efficiency of garment dressing fit prediction using two methods. First, highly reliable examples were captured with the assistance of a robotic mannequin, thereby ensuring the authenticity of the sample data. Second, a mapping was established between the body-garment ease allowance and garment deformations using a convolutional neural network-based network in order to address the problem of dressing fit prediction for ready-made garments on different individuals. This method can also be extended to predicting the dressing fit for ready-made garments with similar styles. Experiments showed our virtual clothes try-on system obtained acceptable precision with a good sense of reality, and it can potentially be used in many applications such as three-dimensional garment design and online clothes retail.