To meet the women appearance needs, we present a novel virtual experience approach of facial makeup transfer, developed into windows platform application software. The makeup effects could present on the user's input image in real time, with an only single reference image. The input image and reference image are divided into three layers by facial feature points landmarked: facial structure layer, facial color layer, and facial detail layer. Except for the above layers are processed by different algorithms to generate output image, we also add illumination transfer, so that the illumination effect of the reference image is automatically transferred to the input image. Our approach has the following three advantages: (1) Black or dark and white facial makeup could be effectively transferred by introducing illumination transfer; (2) Efficiently transfer facial makeup within seconds compared to those methods based on deep learning frameworks; (3) Reference images with the air-bangs could transfer makeup perfectly.
In 2016, @cite_28 of NVIDIA Research designed a new deep convolutional neural network for makeup transfer, which not only could transfer makeup, eye shadow, lip makeup, but also recommend the most suitable input image’s makeup. The network consists of two consecutive steps. The first step is to use the FCN network to parse the facial and resolve different parts, which are distinguished by different colors. The input image and the facial decomposition image of the input image, and the reference image and the facial decomposition image of the reference image are used as input of the makeup transfer network. According to the characteristics of the facial makeup, eye shadow and lip makeup are processed by different loss functions, and the three are integrated. And adding a part of the retained facial image of the input image to get the final result image.
@cite_28
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In this paper, we propose a novel Deep Localized Makeup Transfer Network to automatically recommend the most suitable makeup for a female and synthesis the makeup on her face. Given a before-makeup face, her most suitable makeup is determined automatically. Then, both the before-makeup and the reference faces are fed into the proposed Deep Transfer Network to generate the after-makeup face. Our end-to-end makeup transfer network have several nice properties including: (1) with complete functions: including foundation, lip gloss, and eye shadow transfer; (2) cosmetic specific: different cosmetics are transferred in different manners; (3) localized: different cosmetics are applied on different facial regions; (4) producing naturally looking results without obvious artifacts; (5) controllable makeup lightness: various results from light makeup to heavy makeup can be generated. Qualitative and quantitative experiments show that our network performs much better than the methods of [Guo and Sim, 2009] and two variants of NerualStyle [, 2015a].