ClothFit: Cloth-Human-Attribute Guided Virtual Try-On Network Using 3D
  Simulated Dataset
Yunmin Cho Lala Shakti Swarup Ray Kundan Sai Prabhu Thota Sungho Suh Paul Lukowicz
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.