An Unsupervised Approach towards Varying Human Skin Tone Using Generative Adversarial Networks
This work addresses a domain-specific problem for retailers and users in AR/VR to enhance customer experience, but it is incremental as it builds on existing generative adversarial networks for human attribute manipulation.
The paper tackles the problem of changing human skin tone in images for augmented and virtual reality applications, proposing an unsupervised method that reduces time and effort compared to existing tools like Photoshop, with experiments showing perceptually convincing outputs.
With the increasing popularity of augmented and virtual reality, retailers are now focusing more towards customer satisfaction to increase the amount of sales. Although augmented reality is not a new concept but it has gained much needed attention over the past few years. Our present work is targeted towards this direction which may be used to enhance user experience in various virtual and augmented reality based applications. We propose a model to change skin tone of a person. Given any input image of a person or a group of persons with some value indicating the desired change of skin color towards fairness or darkness, 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 to reduce the time and effort which is generally required for changing the skin tone using existing applications (e.g., Photoshop) by professionals or novice. To establish the efficacy of this method we have compared our result with that of some popular photo editor and also with the result of some existing benchmark method related to human attribute manipulation. Rigorous experiments on different datasets show the effectiveness of this method in terms of synthesizing perceptually convincing outputs.