Online clothing shopping has become increasingly popular, but the high rate
of returns due to size and fit issues has remained a major challenge. To
address this problem, virtual try-on systems have been developed to provide
customers with a more realistic and personalized way to try on clothing. In
this paper, we propose a novel virtual try-on method called ClothFit, which can
predict the draping shape of a garment on a target body based on the actual
size of the garment and human attributes. Unlike existing try-on models,
ClothFit considers the actual body proportions of the person and available
cloth sizes for clothing virtualization, making it more appropriate for current
online apparel outlets. The proposed method utilizes a U-Net-based network
architecture that incorporates cloth and human attributes to guide the
realistic virtual try-on synthesis. Specifically, we extract features from a
cloth image using an auto-encoder and combine them with features from the
user's height, weight, and cloth size. The features are concatenated with the
features from the U-Net encoder, and the U-Net decoder synthesizes the final
virtual try-on image. Our experimental results demonstrate that ClothFit can
significantly improve the existing state-of-the-art methods in terms of
photo-realistic virtual try-on results.

本研究提出了一种名为 ClothFit 的虚拟试衣方法，通过考虑衣服的实际尺寸和人体属性，能够在目标身体上预测衣服的覆盖形状，该方法结合了 U-Net 网络结构和 Auto-encoder 技术，相比现有技术实现更加真实、高效的虚拟试衣效果。