Real-Time Patch-Based Stylization of Portraits Using Generative Adversarial Network
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Date
2019
Authors
Futschik, David
Chai, Menglei
Cao, Chen
Ma, Chongyang
Stoliar, Aleksei
Korolev, Sergey
Tulyakov, Sergey
Kučera, Michal
Sýkora, Daniel
Chai, Menglei
Cao, Chen
Ma, Chongyang
Stoliar, Aleksei
Korolev, Sergey
Tulyakov, Sergey
Kučera, Michal
Sýkora, Daniel
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We present a learning-based style transfer algorithm for human portraits which significantly outperforms current state-of-the-art in computational overhead while still maintaining comparable visual quality. We show how to design a conditional generative adversarial network capable to reproduce the output of Fišer et al.'s patch-based method [FJS*17] that is slow to compute but can deliver state-of-the-art visual quality. Since the resulting end-to-end network can be evaluated quickly on current consumer GPUs, our solution enables first real-time high-quality style transfer to facial videos that runs at interactive frame rates. Moreover, in cases when the original algorithmic approach of Fišer et al. fails our network can provide a more visually pleasing result thanks to generalization. We demonstrate the practical utility of our approach on a variety of different styles and target subjects.
Description
@inproceedings{10.2312:exp.20191074,
booktitle = {ACM/EG Expressive Symposium},
editor = {Kaplan, Craig S. and Forbes, Angus and DiVerdi, Stephen},
title = {{Real-Time Patch-Based Stylization of Portraits Using Generative Adversarial Network}},
author = {Futschik, David and Chai, Menglei and Cao, Chen and Ma, Chongyang and Stoliar, Aleksei and Korolev, Sergey and Tulyakov, Sergey and Kučera, Michal and Sýkora, Daniel},
year = {2019},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-078-9},
DOI = {10.2312/exp.20191074}
}