Revolutionizing Fashion Retail: Virtual Try-on Empowered By Deep Learning

Online clothing purchasing has become a widespread practice for millions of individuals worldwide in the last several years. It has advanced significantly and is now a regular activity for many customers. The use of virtual try-on technology, which allows customers to view how particular things look on them prior to making a purchase, is what is facilitating this advancement. Shopping online is gaining more and more popular, especially for clothes. Nevertheless, the inability to physically try on clothing when shopping online limits customers' ability to visualize how a garment might look on them. The technology can produce high-fidelity try-on photos that maintain the features and general look of clothing items. Apart from the target garment region, the method further inserts the face, hair, lower garments, and legs to preserve model's personality characteristics. The two modules that comprise the system are the Try-On Module (TOM) and the Geometric Matching Module (GMM). In the Geometric Matching Module, in-store clothing items are warped to the intended image of a person with great precision using grid interval consistency loss and an occlusion management technique. The transformation is regularized by grid interval consistency loss to prevent distortion of clothing patterns. Additionally, an occlusion handling technique promotes appropriate warping even when target bodies are obscured by arms or hair. Then, the face, hair, lower clothing, and legs are added to the model's representation for the Try on Module in order to preserve personality characteristics other than the target clothing region.