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With the increasing popularity of augmented and virtual reality, retailersare now focusing more towards customer satisfaction to increase the amount ofsales. Although augmented reality is not a new concept but it has gained muchneeded attention over the past few years. Our present work is targeted towardsthis direction which may be used to enhance user experience in various virtualand augmented reality based applications. We propose a model to change skintone of a person. Given any input image of a person or a group of persons withsome value indicating the desired change of skin color towards fairness ordarkness, this method can change the skin tone of the persons in the image.This is an unsupervised method and also unconstrained in terms of pose,illumination, number of persons in the image etc. The goal of this work is toreduce the time and effort which is generally required for changing the skintone using existing applications (e.g., Photoshop) by professionals or novice.To establish the efficacy of this method we have compared our result with thatof some popular photo editor and also with the result of some existingbenchmark method related to human attribute manipulation. Rigorous experimentson different datasets show the effectiveness of this method in terms ofsynthesizing perceptually convincing outputs.

An Unsupervised Approach towards Varying Human Skin Tone Using Generative Adversarial Networks