Corpus ID: 174803447

Lightweight Real-time Makeup Try-on in Mobile Browsers with Tiny CNN Models for Facial Tracking

@article{Li2019LightweightRM,
  title={Lightweight Real-time Makeup Try-on in Mobile Browsers with Tiny CNN Models for Facial Tracking},
  author={Tianxing Li and Zhi Yu and Edmund Phung and Brendan Duke and I. Kezele and P. Aarabi},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.02260}
}
Recent works on convolutional neural networks (CNNs) for facial alignment have demonstrated unprecedented accuracy on a variety of large, publicly available datasets. However, the developed models are often both cumbersome and computationally expensive, and are not adapted to applications on resource restricted devices. In this work, we look into developing and training compact facial alignment models that feature fast inference speed and small deployment size, making them suitable for… Expand
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This paper introduces an inverse computer graphics method for automatic makeup synthesis from a reference image, by learning a model that maps an example portrait image with makeup to the space of rendering parameters. Expand

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