ABSTRACT
Facial makeup transfer aims to translate the makeup style from a given reference makeup face image to another non-makeup one while preserving face identity. Such an instance-level transfer problem is more challenging than conventional domain-level transfer tasks, especially when paired data is unavailable. Makeup style is also different from global styles (e.g., paintings) in that it consists of several local styles/cosmetics, including eye shadow, lipstick, foundation, and so on. Extracting and transferring such local and delicate makeup information is infeasible for existing style transfer methods. We address the issue by incorporating both global domain-level loss and local instance-level loss in an dual input/output Generative Adversarial Network, called BeautyGAN. Specifically, the domain-level transfer is ensured by discriminators that distinguish generated images from domains' real samples. The instance-level loss is calculated by pixel-level histogram loss on separate local facial regions. We further introduce perceptual loss and cycle consistency loss to generate high quality faces and preserve identity. The overall objective function enables the network to learn translation on instance-level through unsupervised adversarial learning. We also build up a new makeup dataset that consists of 3834 high-resolution face images. Extensive experiments show that BeautyGAN could generate visually pleasant makeup faces and accurate transferring results. Data and code are available at http://liusi-group.com/projects/BeautyGAN.
Supplemental Material
Available for Download
In this supplementary material, we present details about network architectures and runtime performance. We also discuss limitation in some cases and provide some failure cases. More facial makeup transfer results are shown as complements.
- Cunjian Chen, Antitza Dantcheva, and Arun Ross. 2013. Automatic facial makeup detection with application in face recognition. In Biometrics (ICB), 2013 International Conference on. IEEE, 1--8.Google ScholarCross Ref
- Cunjian Chen, Antitza Dantcheva, and Arun Ross. 2016. An ensemble of patchbased subspaces for makeup-robust face recognition. Information fusion 32 (2016), 80--92. Google ScholarDigital Library
- Cunjian Chen, Antitza Dantcheva, Thomas Swearingen, and Arun Ross. 2017. Spoofing faces using makeup: An investigative study. In Identity, Security and Behavior Analysis (ISBA), 2017 IEEE International Conference on. IEEE, 1--8.Google ScholarCross Ref
- Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. 2017. StarGAN: Unified Generative Adversarial Networks for Multi- Domain Image-to-Image Translation. arXiv preprint arXiv:1711.09020 (2017).Google Scholar
- Antitza Dantcheva, Cunjian Chen, and Arun Ross. 2012. Can facial cosmetics affect the matching accuracy of face recognition systems?. In Biometrics: Theory, Applications and Systems (BTAS), 2012 IEEE Fifth International Conference on. IEEE, 391--398.Google Scholar
- Brian Dolhansky and Cristian Canton Ferrer. 2017. Eye In-Painting with Exemplar Generative Adversarial Networks. arXiv preprint arXiv:1712.03999 (2017).Google Scholar
- Hasan Sheikh Faridul, Tania Pouli, Christel Chamaret, Jürgen Stauder, Alain Trémeau, Erik Reinhard, et al. 2014. A Survey of Color Mapping and its Applications. Eurographics (State of the Art Reports) 3 (2014).Google Scholar
- Leon A Gatys, Alexander S Ecker, and Matthias Bethge. 2015. A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 (2015).Google Scholar
- Leon A Gatys, Alexander S Ecker, Matthias Bethge, Aaron Hertzmann, and Eli Shechtman. 2017. Controlling perceptual factors in neural style transfer. In IEEE Conference on Computer Vision and Pattern Recognition.Google ScholarCross Ref
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672--2680. Google ScholarDigital Library
- Dong Guo and Terence Sim. 2009. Digital face makeup by example. In Computer Vision and Pattern Recognition, 2009. IEEE Conference on. IEEE, 73--79.Google Scholar
- Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2016. Imageto- image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004 (2016).Google Scholar
- Justin Johnson, Alexandre Alahi, and Li Fei-Fei. 2016. Perceptual losses for realtime style transfer and super-resolution. In European Conference on Computer Vision. Springer, 694--711.Google ScholarCross Ref
- Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jungkwon Lee, and Jiwon Kim. 2017. Learning to discover cross-domain relations with generative adversarial networks. arXiv preprint arXiv:1703.05192 (2017).Google Scholar
- Taeksoo Kim, Byoungjip Kim, Moonsu Cha, and Jiwon Kim. 2017. Unsupervised visual attribute transfer with reconfigurable generative adversarial networks. arXiv preprint arXiv:1707.09798 (2017).Google Scholar
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
- Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. 2016. Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint arXiv:1609.04802 (2016).Google Scholar
- Chuan Li and MichaelWand. 2016. Precomputed real-time texture synthesis with markovian generative adversarial networks. In European Conference on Computer Vision. Springer, 702--716.Google ScholarCross Ref
- Chen Li, Kun Zhou, and Stephen Lin. 2015. Simulating makeup through physicsbased manipulation of intrinsic image layers. In IEEE Conference on Computer Vision and Pattern Recognition. 4621--4629.Google ScholarCross Ref
- Yi Li, Lingxiao Song, Xiang Wu, Ran He, and Tieniu Tan. 2017. Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification. arXiv preprint arXiv:1709.03654 (2017).Google Scholar
- Jing Liao, Yuan Yao, Lu Yuan, Gang Hua, and Sing Bing Kang. 2017. Visual attribute transfer through deep image analogy. ACM Transactions on Graphics (TOG) 36, 4 (2017), 120. Google ScholarDigital Library
- Ming-Yu Liu and Oncel Tuzel. 2016. Coupled generative adversarial networks. In Advances in neural information processing systems. 469--477. Google ScholarDigital Library
- Si Liu, Xinyu Ou, Ruihe Qian, Wei Wang, and Xiaochun Cao. 2016. Makeup like a superstar: deep localized makeup transfer network. In the Association for the Advance of Artificial Intelligence. AAAI Press, 2568--2575. Google ScholarDigital Library
- Xudong Mao, Qing Li, Haoran Xie, Raymond Y. K. Lau, and Zhen Wang. 2016. Multi-class Generative Adversarial Networks with the L2 Loss Function. CoRR abs/1611.04076 (2016). arXiv:1611.04076 http://arxiv.org/abs/1611.04076Google Scholar
- Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014).Google Scholar
- Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. 2018. Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018).Google Scholar
- Ashish Shrivastava, Tomas P'ster, Oncel Tuzel, Josh Susskind,WendaWang, and Russ Webb. 2016. Learning from simulated and unsupervised images through adversarial training. arXiv preprint arXiv:1612.07828 (2016).Google Scholar
- K. Simonyan and A. Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR abs/1409.1556 (2014).Google Scholar
- Wai-Shun Tong, Chi-Keung Tang, Michael S Brown, and Ying-Qing Xu. 2007. Example-based cosmetic transfer. In Computer Graphics and Applications, 2007. PG'07. 15th Pacific Conference on. IEEE, 211--218. Google ScholarDigital Library
- Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky. 2016. Instance Normalization: The Missing Ingredient for Fast Stylization. CoRR abs/1607.08022 (2016). arXiv:1607.08022 http://arxiv.org/abs/1607.08022Google Scholar
- Shuyang Wang and Yun Fu. 2016. Face Behind Makeup. In the Association for the Advance of Artificial Intelligence. 58--64. Google ScholarDigital Library
- Zhen Wei, Yao Sun, Jinqiao Wang, Hanjiang Lai, and Si Liu. 2017. Learning Adaptive Receptive Fields for Deep Image Parsing Network. In IEEE Conference on Computer Vision and Pattern Recognition. 2434--2442.Google Scholar
- Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. 2017. Pyramid scene parsing network. In IEEE Conference on Computer Vision and Pattern Recognition. 2881--2890.Google ScholarCross Ref
- Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A Efros. 2016. Generative visual manipulation on the natural image manifold. In European Conference on Computer Vision. Springer, 597--613.Google ScholarCross Ref
- Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593 (2017).Google Scholar
Index Terms
- BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network
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