Abstract
Portrait style transfer is a hot and practical direction for in-depth learning. As a deep learning model, Generative Adversarial Networks (GANs) have been widely used in image style conversion. We study Generative Adversarial Networks as a solution to the portrait style transfer problem. Here, we use GANs to recognize facial features. With large training in the conversion from plain to cosmetic drawings, this algorithm can make up the plain faces better intelligently. The experimental results provide the representation of facial image features by GANs and show the ability of character transformation and operation of portrait style.
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References
Xie, S., Huang, X., Tu, Z.: Top-down learning for structured labeling with convolutional pseudoprior. In: European Conference on Computer Vision, pp. 302–317 (2015)
Zhao, H.H., Liu, H.: Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition. Granul. Comput. 1–8 (2019)
Zhao, H.H., Rosin, P., Lai, Y.K., Zheng, J.H., Wang, Y.N.: Adaptive gradient-based block compressive sensing with sparsity for noisy images. Multimed. Tools Appl. 1–23 (2019)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: CVPR, vol. 2, pp. 60–65 (2005)
Denton, E.L., Chintala, S., Fergus, R., et al.: Deep generative image models using a laplacian pyramid of adversarial networks. In: NIPS, pp. 1486–1494 (2015)
Tyleček, R.: Spatial pattern templates for recognition of objects with regular structure. In: GCPR, Saarbrucken, Germany (2013)
Zhao, H.H., Rosin, P., Lai, Y.K.: Image neural network style transfer with global and local optimization fusion. IEEE Access (2019)
Zhao, H., Rosin, P.L., Lai, Y.K.: Automatic semantic style transfer using deep convolutional neural networks and soft masks. Vis. Comput. (2019)
Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: SIGGRAPH, pp. 341–346 (2001)
Efros, A.A., Leung, T.K.: Texture synthesis by nonparametric sampling. In: ICCV, vol. 2, pp. 1033–1038 (1999)
Gatys, L.A., Ecker, A.S., Bethge, M.: Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks. Preprint at arXiv:1505.07376 (2015)
Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelet coefficients. Int. J. Comput. Vis. 40(1), 49–70 (2000)
Efros, A.A., Leung, T.K.: Texture synthesis by nonparametric sampling. In: Proceedings of International Conference Computer Vision, Washington, DC, USA (1999)
Isola, P., Zhu, J.Y., Zhou, T.H., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)
Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P.: Colortransfer between images. IEEE Comput. Graph. Appl. 21, 34–41 (2001)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision, pp. 818–833 (2014)
Zhao, J., Mathieu, M., LeCun, Y.: Energy-based generative adversarial network. Preprint at arXiv:1609.03126 (2016)
Zhu, J.Y., Krähenbühl, P., Shechtman, E., Efros, A.A.: Generative visual manipulation on the natural image manifold. In: ECCV (2016)
Theis, L., Bethge, M.: Generative image modeling using spatial lstms. Adv. Neural Inf. Process. Syst. 28 (2015)
Laffont, P.Y., Ren, Z., Tao, X., Qian, C., Hays, J.: Transient attributes for high-level understanding and editing of outdoor scenes. ACM Trans. Graph. (TOG) 33(4), 149 (2014)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. Preprint at arXiv:1502.03167 (2015)
Yoo, D., Kim, N., Park, S., Paek, A.S., Kweon, I.S.: Pixel level domain transfer. In: ECCV (2016)
Gatys, L.A., Ecker, A.S.: Image style transfer using convolutional neural networks. In: CVPR (2016)
Li, C., Wand, M.: Combining markov random fields and convolutional neural networks for image synthesis. Preprint at arXiv:1601.04589 (2016)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. Preprint at arXiv:1511.06434 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: MICCAI, pp. 234–241 (2015)
Wang, X., Gupta, A.: Generative image modeling using style and structure adversarial networks. In: ECCV (2016)
Li, C., Wand, M.: Precomputed real-time texture synthesis with markovian generative adversarial networks. In: ECCV (2016)
Acknowledgements
This work is supported by the Hunan Provincial Natural Science Foundation of China (No. 2019JJ40005), the Science and Technology Plan Project of Hunan Province (No. 2016TP1020), the General Scientific Research Fund of Hunan Provincial Education Department (No. 17C0223), the Double First-Class University Project of Hunan Province (No. Xiangjiaotong [2018]469), and Postgraduate Research and Innovation Projects of Hunan Province (No. Xiangjiaotong [2019]248–998). Hengyang guided science and technology projects and application-oriented special disciplines (No. Hengkefa [2018]60–31).
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Liu, Q., Zhang, F., Lin, M., Wang, Y. (2021). Portrait Style Transfer with Generative Adversarial Networks. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, HH. (eds) Proceedings of the 9th International Conference on Computer Engineering and Networks . Advances in Intelligent Systems and Computing, vol 1143. Springer, Singapore. https://doi.org/10.1007/978-981-15-3753-0_36
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DOI: https://doi.org/10.1007/978-981-15-3753-0_36
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