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Facelet-Bank for Fast Portrait Manipulation
AbstractDigital face manipulation has become a popular and fascinating way to touch images with the prevalence of smart phones and social networks. With a wide variety of user preferences, facial expressions, and accessories, a general and flexible model is necessary to accommodate different types of facial editing. In this paper, we propose a model to achieve this goal based on an end-to-end convolutional neural network that supports fast inference, edit-effect control, and quick partial-model update. In addition, this model learns from unpaired image sets with different attributes. Experimental results show that our framework can handle a wide range of expressions, accessories, and makeup effects. It produces high-resolution and high-quality results in fast speed.
Related Material
[pdf] [arXiv][bibtex]@InProceedings{Chen_2018_CVPR,
author = {Chen, Ying-Cong and Lin, Huaijia and Shu, Michelle and Li, Ruiyu and Tao, Xin and Shen, Xiaoyong and Ye, Yangang and Jia, Jiaya},
title = {Facelet-Bank for Fast Portrait Manipulation},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}
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