Date of Publication :25th July 2024

Abstract:In the midst of the growth in fashion retail industry, fitting of clothing remotely can be utilized to reduce return rates and unnecessary shipping. Due to recent advancements in Generative AI, models for generating a virtual try-on experience have also been able to develop further. This paper evaluates different models and techniques which are built for the application of virtual try-on. Additionally, this paper discusses about different models - ClothFlow model, Contextual-VTON model or FitGAN along with a deep insight into algorithms and methodologies namely Residual Networks, U-Net and Generative Adversarial Networks. Additionally, the paper compares these state-of-the-art models against the various evaluation metrics such as Frechet Inception Distance (FID), Inception Score (IS), Structural Similarity index (SSIM). It also explains, how the generated image outputs vary based on the changes made in hyper-parameters such as learning rate, batch size etc., and how these changes impact on the generated output.

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