Facelet-Bank for Fast Portrait Manipulation
Ying-Cong Chen, Huaijia Lin, Michelle Shu, Ruiyu Li, Xin Tao, Xiaoyong Shen, Yangang Ye, Jiaya Jia
Abstract
Digital 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.
BibTeX
@inproceedings{cvpr2018_faceletbankforfa,
title = {Facelet-Bank for Fast Portrait Manipulation},
author = {Ying-Cong Chen and Huaijia Lin and Michelle Shu and Ruiyu Li and Xin Tao and Xiaoyong Shen and Yangang Ye and Jiaya Jia},
booktitle = {CVPR 2018},
year = {2018}
}