Abstract
Achieving Clothing Try-On with 2D images is always complicated because retrieving all the characteristics of a person and the clothing can be difficult. The absence of an ideal dataset and the presence of considerable variance in the problem poses challenges. Despite this, there has been a growing interest in this task, thanks to the advancements in generative modelling. Various approaches have been developed and implemented, but due to the data and computation expensive nature of the task, they may not be widely applicable. To overcome this limitation, we present two different models: an Encoder-Decoder-based model and a Generative Adversarial Network-based model, both of which can effectively achieve virtual try-on with minimal architectural complexity through style transfer. Without excessive data, we aim to put the desired garment onto an intended target image. The network has been trained to learn the clothing and posture details of the image and perform the desired style transfer. The proposed method gives competitive results despite a considerable difference in model complexity.
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Karn, R.R.P., Sanodiya, R.K., Chandaluri, E.S., Suryavardan, S., Reddy, L.R., Yao, L. (2022). Virtual Try-On Using Style Transfer. In: Mathew, J., Santhosh Kumar, G., P., D., Jose, J.M. (eds) Responsible Data Science. Lecture Notes in Electrical Engineering, vol 940. Springer, Singapore. https://doi.org/10.1007/978-981-19-4453-6_9
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DOI: https://doi.org/10.1007/978-981-19-4453-6_9
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