Abstract
We propose a novel automatic photographic painting style technique with a single example image by using Convolutional Neural Networks (CNN). The photographic painting style is a challenging problem in the research community. Even though, researchers have been trying to obtain good results on painting style, but not much has been done on photographic stylization. Portrait painting techniques are mainly designed for the graphite style and/or are based on image analogies; an example painting as well as its original unpainted version are required. This preceding issue is a motivation of our proposed methods. As a result, our method extends the limits of their domain of applicability. We present a novel multi-convolutional-learning technique that is developed for both images (NPR/PR) labeling, style transmission and elevating a particular unified CNN model per weight sharing. A new painting technique is generated that follows the example style in the example image and maintains the integrity of facial structures. We believe this novel interpretation connects these two important research fields and could enlighten future researches. Moreover, our proposed technique is not restricted to headshot images or specific styles as our method can also change the photographic painting style in the wild.
Similar content being viewed by others
References
Helen (2016) http://www.ifp.illinois.edu/vuongle2/helen/
Printerest (2017) https://www.pinterest.com/
Ashikhmin M (2001) Synthesizing natural textures. In: Proceedings of the 2001 symposium on interactive 3D graphics. ACM, pp 217–226
Ashikhmin N (2003) Fast texture transfer. IEEE Comput Graph Appl 23(4):38–43
Chen H, Liang L, Xu Y-Q, Shum H-Y, Zheng N-N (2003) Example-based automatic portraiture. Chinese journal of computers-chinese edition- 26 2:147–152
Chen H, Liu Z, Rose C, Xu Y, Shum H-Y, Salesin D (2004) Example-based composite sketching of human portraits. In: Proceedings of the 3rd international symposium on Non-photorealistic animation and rendering. ACM, pp 95–153
Chen H, Xu Y-Q, Shum H-Y, Zhu S-C, Zheng N-N (2001) Example-based facial sketch generation with non-parametric sampling. In: Proceedings 8th IEEE international conference on computer vision, ICCV 2001, vol 2. IEEE, pp 433–438
Chen H, Zheng N-N, Liang L, Li Y, Xu Y-Q, Shum H-Y (2002) Pictoon: a personalized image-based cartoon system. In: Proceedings of the tenth ACM international conference on multimedia. ACM, pp 171–178
Collomosse JP, Hall PM (2005) Genetic paint: A search for salient paintings. In: Workshops on applications of evolutionary computation. Springer, pp 437–447
Efros AA, Freeman WT (2001) Image quilting for texture synthesis and transfer. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques. ACM, pp 341–346
Efros AA, Leung TK (1999) Texture synthesis by non-parametric sampling. In: The proceedings of the 7th IEEE international conference on computer vision, 1999, vol 2. IEEE, pp 1033–1038
Farabet C, Couprie C, Najman L, LeCun Y (2013) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35(8):1915–1929
Gao L, Guo Z, Zhang H, Xu X, Shen HT (2017) Video captioning with attention-based lstm and semantic consistency. IEEE Trans Multimed 19(9):2045–2055
Gatys L, Ecker AS, Bethge M (2015) Texture synthesis using convolutional neural networks. In: Advances in neural information processing systems, pp 262–270
Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2414– 2423
Gooch B, Reinhard E, Gooch A (2004) Human facial illustrations creation and psychophysical evaluation. ACM Trans Graph 23(1):27–44
Gu S, Chen C, Liao J, Yuan L (2018) Arbitrary style transfer with deep feature reshuffle. CoRR: 1805.04103
Hertzmann A (1998) Painterly rendering with curved brush strokes of multiple sizes. In: Proceedings of the 25th annual conference on Computer graphics and interactive techniques. ACM, pp 453– 460
Hertzmann A, Jacobs CE, Oliver N, Curless B, Salesin DH (2001) Image analogies. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques. ACM, pp 327–340
Huang X, Belongie S (2017) Arbitrary style transfer in real-time with adaptive instance normalization. In: IEEE international conference on computer vision (ICCV)
Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision. Springer, pp 694–711
Kim SY, Maciejewski R, Isenberg T, Andrews WM, Chen W, Sousa MC, Ebert DS (2009) Stippling by example. In: Proceedings of the 7th international symposium on non-photorealistic animation and rendering. ACM, pp 41–50
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems 25. Curran Associates, Inc., pp 1097–1105
Kwatra V, Essa I, Bobick A, Kwatra N (2005) Texture optimization for example-based synthesis. ACM Trans Graph 24(3):795–802
Kyprianidis JE, Collomosse J, Wang T, Isenberg T (2013) State of the art: a taxonomy of artistic stylization techniques for images and video. IEEE Trans Vis Comput Graph 19(5):866–885
Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2016) Photo-realistic single image super-resolution using a generative adversarial network. arXiv:1609.04802
Lee H, Seo S, Ryoo S, Yoon K (2010) Directional texture transfer. In: Proceedings of the 8th international symposium on non-photorealistic animation and rendering. ACM, pp 43–48
Li C, Wand M (2016) Combining markov random fields and convolutional neural networks for image synthesis. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 2479– 2486
Li Y, Fang C, Yang J, Wang Z, Lu X, Yang M-H (2017) Universal style transfer via feature transforms. In: Advances in neural information processing systems
Liao J, Yao Y, Yuan L, Hua G, Kang SB (2017) Visual attribute transfer through deep image analogy. ACM Trans Graph 36(4):120:1–120:15
Lu M, Zhao H, Yao A, Xu F, Chen Y, Zhang L (2017) Decoder network over lightweight reconstructed feature for fast semantic style transfer. In: 2017 IEEE international conference on computer vision (ICCV), pp 2488–2496
McKone E, Kanwisher N, Duchaine BC (2007) Can generic expertise explain special processing for faces? Trends Cogn Sci 11(1):8–15
Meng M, Zhao M, Zhu S-C (2010) Artistic paper-cut of human portraits. In: Proceedings of the 18th ACM international conference on multimedia. ACM, pp 931–934
Pinheiro PHO, Collobert R (2013) Recurrent convolutional neural networks for scene parsing. CoRR 1306.2795
Ruder M, Dosovitskiy A, Brox T (2016) Artistic style transfer for videos. In: German conference on pattern recognition. Springer, pp 26–36
Selim A, Elgharib M, Doyle L (2016) Painting style transfer for head portraits using convolutional neural networks. ACM Trans Graph 35(4):129
Shih Y, Paris S, Barnes C, Freeman WT, Durand F (2014) Style transfer for headshot portraits. ACM Trans Graph 33(4):148:1–148:14
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Sinha P, Balas B, Ostrovsky Y, Russell R (2006) Face recognition by humans Nineteen results all computer vision researchers should know about. Proc IEEE 94(11):1948–1962
Song J, Gao L, Nie F, Shen HT, Yan Y, Sebe N (2016) Optimized graph learning using partial tags and multiple features for image and video annotation. IEEE Trans Image Process 25(11):4999– 5011
Song J, Guo Y, Gao L, Li X, Hanjalic A, Shen HT (2018) From deterministic to generative: multi-modal stochastic RNNs for video captioning. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2018.2851077
Song J, Zhang H, Li X, Gao L, Wang M, Hong R (2018) Self-supervised video hashing with hierarchical binary auto-encoder. IEEE Trans Image Process 27 (7):3210–3221
Tompson J, Jain A, LeCun Y, Bregler C (2014) Joint training of a convolutional network and a graphical model for human pose estimation. In: Proceedings of the 27th international conference on neural information processing systems, NIPS’14. MIT Press, Cambridge, pp 1799–1807
Ulyanov D, Lebedev V, Vedaldi A, Lempitsky V (2016) Texture networks Feed-forward synthesis of textures and stylized images. In: Interational conference on machine learning (ICML)
Wang B, Wang W, Yang H, Sun J (2004) Efficient example-based painting and synthesis of 2d directional texture. IEEE Trans Vis Comput Graph 10(3):266–277
Wang T, Collomosse JP, Hunter A, Greig D (2013) Learnable stroke models for example-based portrait painting. In: British machine vision conference
Wang X, Gao L, Song J, Shen H (2017) Beyond frame-level cnn Saliency-aware 3-d cnn with lstm for video action recognition. IEEE Signal Process Lett 24(4):510–514
Wang X, Gao L, Wang P, Sun X, Liu X Two-stream 3-d convnet fusion for action recognition in videos with arbitrary size and length. 1–1
Wang X, Tang X (2009) Face photo-sketch synthesis and recognition. IEEE Trans Pattern Anal Mach Intell 31(11):1955–1967
Zeng K, Zhao M, Xiong C, Zhu S-C (2009) From image parsing to painterly rendering. ACM Trans Graph 29(1):2
Zhao M, Zhu S-C (2011) Portrait painting using active templates. In: Proceedings of the ACM SIGGRAPH/Eurographics symposium on non-photorealistic animation and rendering. ACM, pp 117– 124
Zhu C, Byrd RH, Lu P, Nocedal J (1997) Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans Math Softw 23 (4):550–560
Zhu X, Li X, Zhang S (2016) Block-row sparse multiview multilabel learning for image classification. IEEE Trans Cybern 46:450–461
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 61202296, 61872153) and the National Science Foundation of Guangdong province No. 2018A030313318.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Khan, A., Ahmad, M., Naqvi, N. et al. Photographic painting style transfer using convolutional neural networks. Multimed Tools Appl 78, 19565–19586 (2019). https://doi.org/10.1007/s11042-019-7270-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-7270-8