Real-Time Deep Hair Matting on Mobile Devices
TL
TL;DR
A modified MobileNet CNN architecture can be used to segment the hair in real-time, and it is shown how this network can produce accurate and fine-detailed hair mattes, while running at over 30 fps on an iPad Pro tablet.
Abstract
Augmented reality is an emerging technology in many application domains. Among them is the beauty industry, where live virtual try-on of beauty products is of great importance. In this paper, we address the problem of live hair color augmentation. To achieve this goal, hair needs to be segmented quickly and accurately. We show how a modified MobileNet CNN architecture can be used to segment the hair in real-time. Instead of training this network using large amounts of accurate segmentation data, which is difficult to obtain, we use crowd sourced hair segmentation data. While such data is much simpler to obtain, the segmentations there are noisy and coarse. Despite this, we show how our system can produce accurate and fine-detailed hair mattes, while running at over 30 fps on an iPad Pro tablet.
References23 items
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Automatic skin and hair masking using fully convolutional networks
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
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Deep Image Matting
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AutoHair
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