FashionFlow: A Lightweight Pix2Pix-Based Approach to Virtual Try-On

The proposed virtual try-on model works on synthesizing naturalistic images by superimposition of target clothing item on a person. The proposed model offers a significant advancement in virtual try-on technology, by introducing a lightweight architecture that reduces the system requirements required to run the application. Many different preprocessing methods have been utilized to account for different poses and body shapes. Great efficiency has been achieved by employing the Pix2Pix generative adversarial networks (GAN) architecture with a minimal number of training epochs. The proposed model’s performance shows great results without a discriminator, a change from traditional GAN setups, thus emphasizing the need for simple and effective virtual try-on solutions. While the performance is characterized as good, this novel approach encourages further exploration in lightweight model design for practical and efficient try-on applications. Additionally, this paper showcases results from other GAN architectures providing a comprehensive overview of our research contributions in virtual try-on technology. Received: 10 January 2024 | Revised: 31 July 2024 | Accepted: 22 October 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https://github.com/shadow2496/VITON-HD.git; in Kaggle at https://www.kaggle.com/datasets/marquis03/high-resolution-viton-zalando-dataset. Author Contribution Statement Samar Pratap: Conceptualization, Formal analysis, Data curation. Alston Richard Aranha: Methodology, Validation, Visualization. Divyanshu Kumar: Software, Investigation, Writing – original draft. P. Preethi: Writing - review & editing, Supervision, Project administration.