ML Anthology

Facelet-Bank for Fast Portrait Manipulation

Ying-Cong Chen, Huaijia Lin, Michelle Shu, Ruiyu Li, Xin Tao, Xiaoyong Shen, Yangang Ye, Jiaya Jia
CVPR 2018
doi:10.1109/CVPR.2018.00373 /cvpr/2018/chen2018cvpr-faceletbank/

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.

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Cite

Text

Chen et al. "Facelet-Bank for Fast Portrait Manipulation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00373

Markdown

[Chen et al. "Facelet-Bank for Fast Portrait Manipulation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/chen2018cvpr-faceletbank/) doi:10.1109/CVPR.2018.00373

BibTeX

@inproceedings{chen2018cvpr-faceletbank,
  title     = {{Facelet-Bank for Fast Portrait Manipulation}},
  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},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00373},
  url       = {https://mlanthology.org/cvpr/2018/chen2018cvpr-faceletbank/}
}