Virtual try-on technology has emerged as a key driver of online clothing sales growth. It addresses the challenge of not being able to try on garments in person, thus improving the online shopping experience. This paper provides a comprehensive review of deep learning-based virtual try-on research. It organizes relevant literature across several areas. These include feature extraction techniques, clothing warping technologies, image synthesis methods, benchmark data sets, and evaluation metrics. This paper also offers a summary and comparative analysis of existing approaches. Special emphasis is given to tasks, such as spatial-semantic alignment between garments and human poses, preservation of clothing texture features, and high-fidelity image generation. Finally, this paper elaborates on current limitations of virtual try-on technology and proposes directions for future research. The goal is to support the broader application of this technology in e-commerce.
Fu et al. (Fri,) studied this question.