Fast and robust virtual try-on based on parser-free generative adversarial network

Abstract

Image-based virtual try-on models have recently become popular leading to many new developments, especially in the past three years. The problem of virtual try-on requires trying on a cloth image on a target person’s image. Implementing the same turns out to be a complicated task. It involves calculating the position, angle, and texture for the cloth to be placed on the target that could be in varying orientations. Also, texture may change as a result of any change in orientation. Therefore, generating textures for the cloth also poses a major challenge. In this article, we propose a generative adversarial network-based virtual try-on network that is robust, fast, and parser-free. We dive into some of the latest developments in the field of virtual try-on models and discuss their market feasibility as well as techniques. It is observed that the performance of our proposed network is comparable to the state-of-the-art models, and it outperforms the latter in terms of execution speed owing to its low time complexity. Moreover, it uses a parser-free architecture. It does not require any external input or processing while testing or applying a trained model. It uses a “teacher-student” approach to learn from existing models. The loss function is based on final output of the model. Therefore, it can also learn its shortcomings from the output of the model, unlike other architectures where much of the training is done in a self-supervised manner from the real person’s image.