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Transformation of portraits to Picasso’s cubism style

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Abstract

This paper presents an approach to the transformation of portrait photographs to Picasso’s cubism style using deep learning and image processing techniques. We obtain the side-view face by rotating the face model constructed from a frontal portrait image 90\(^\circ \) and then replace the left half of the portrait by the side-view face. Our approach is applicable to online transformation of selfie photographs and potentially extendable to broader categories of images and artistic styles.

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References

  1. Xuemei, Y.: Picasso Art Theory. People Art Publishing House, Beijing (2002)

    Google Scholar 

  2. Osborne, H.: Abstract and Skills in 20th Century’s Art, p. 87. Sichuan Arts Publishing House, Chengdu (1978)

    Google Scholar 

  3. Lian, G., Wang, Y., Zhang, K., Yao, L.: An attempt in modeling Picasso’s cubism style. In: SIGGRAPH Asia 2016 Posters, ACM, Article 53 (2016)

  4. Fishwick, P.: Aesthetic Computing, vol. 38, p. 2. MIT Press, Cambridge (2006)

    Book  Google Scholar 

  5. Curtis, C.J., Anderson, S.E., Seims, J.E., Fleischer, K.W., Salesin, D.H.: Computer-generated watercolor. In: Conference on Computer Graphics and Interactive Techniques, pp. 421–430. ACM Press/Addison-Wesley Publishing Co. (1997)

  6. Maciejewski, R., Isenberg, T., Andrews, W.M., Ebert, D.S., Sousa, M.C.: Measuring stipple aesthetics in hand-drawn and computer-generated images. IEEE Comput. Graph. Appl. 28(2), 62–74 (2008)

    Article  Google Scholar 

  7. Long, X.: Design and Implementation computer-generated brush strokes for painting. J. Comput. Aided Des. Comput. Graph. 17(3), 623–626 (2005)

    Google Scholar 

  8. Tao, W., Liu, Y.X., Zhang, K.: Automatically generating abstract paintings in Malevich style. In: IEEE/ACIS International Conference on Computer and Information Science, pp. 201–215 (2014)

  9. Zhang, K., Harrell, S., Ji, X.: On the complexity of computer-generated paintings. Comput. Aesthet. 45(3), 243–248 (2013)

    Google Scholar 

  10. Zheng, Y., Nie, X., Meng, Z.-P., Feng, W., Zhang, K.: Layered modeling and generation of Pollock’s drip style. Vis. Comput. 31(5), 589–600 (2015)

    Article  Google Scholar 

  11. Seitz, S.M., Kim, J.: Multiperspective imaging. IEEE Comput. Graph. Appl. 23(6), 16–19 (2003)

    Article  Google Scholar 

  12. Arpa, S., Bulbul, A., Capin, T., Ozguc, B.: Perceptual 3D rendering based on principles of analytical cubism. Comput. Graph. 36(8), 991–1000 (2012)

    Article  Google Scholar 

  13. Collomosse, J.P., Hall, P.M.: Cubist style rendering from photographs. IEEE Trans. Vis. Comput. Graph. 9(4), 443–453 (2003)

    Article  Google Scholar 

  14. Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., Song, M.: Neural style transfer: a review. CoRR arXiv:1705.04058 (2017)

  15. Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. Comput. Sci. 35(2), 10005–10014 (2015)

    Google Scholar 

  16. Selim, A., Elgharib, M., Doyle, L.: Painting style transfer for head portraits using convolutional neural networks. ACM Trans. Graph. 35(4), 129 (2016)

    Article  Google Scholar 

  17. Chen, D., Yuan, L., Liao, J., Yu, N., Hua, G.: Stylebank: an explicit representation for neural image style transfer. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1897–1906 (2017)

  18. Zhang, H., Dana, K.: Multi-style generative network for real-time transfer (2017). arXiv:1703.06953

  19. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision. Springer, Cham (2016)

    Chapter  Google Scholar 

  20. Ulyanov, D., Lebedev, V., Vedaldi, A., Lempitsky, V.: Texture networks: feed-forward synthesis of textures and stylized images. In: ICML (2016)

  21. Li, C., Wand, M.: Precomputed real-time texture synthesis with Markovian generative adversarial networks. In: European Conference on Computer Vision. Springer, Cham (2016)

    Chapter  Google Scholar 

  22. Li, Y., Fang, C., Yang, J., Wang, Z., Lu, X., Yang, M.: Universal style transfer via feature transforms. Adv. Neural Inf. Process. Syst. (2017)

  23. Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: ICCV (2017)

  24. Risser, E., Wilmot, P., Barnes, C.: Stable and controllable neural texture synthesis and style transfer using histogram losses (2017). arXiv:1701.08893

  25. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)

  26. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFS. Comput. Sci. 4, 357–361 (2014)

    Google Scholar 

  27. Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision (2015)

  28. Kamnitsas, K., Ledig, C., Newcombe, V.F.J., Simpsonb, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)

    Article  Google Scholar 

  29. Jackson, A.S., Bulat, A., Argyriou, V., Tzimiropoulos, G.: Large pose 3D face reconstruction from a single image via direct volumetric CNN regression. In: ICCV 2017, pp. 1031–1039 (2017)

  30. Cristinacce, D., Cootes, T.F.: Boosted regression active shape models. In: Bmvc (2007)

  31. Cao, X., Wei, Y., Wen, F., Sun, J.: Face alignment by explicit shape regression. Int. J. Comput. Vis. 107(2), 177–190 (2014)

    Article  MathSciNet  Google Scholar 

  32. Qu, Y.D., Cui, C.S., Chen, S.B., Li, J.Q.: A fast subpixel edge detection method using Sobel–Zernike moments. Image Vis. Comput. 23(1), 11–17 (2005)

    Article  Google Scholar 

  33. Sobel, I.: Neighbourhood coding of binary images fast contour following and general array binary processing. Comput. Graph. Image Process. 8(1), 127–135 (1978)

    Article  Google Scholar 

  34. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR arXiv:1409.1556 (2015)

  35. King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10(3), 1755–1758 (2009)

    Google Scholar 

  36. Sadhu, S., Kumar, N., Kumar, B.: Random polygon generation through convex layers. Proc. Technol. 10(10), 356–364 (2013)

    Article  Google Scholar 

  37. Wang, Z., Zhang, D.: Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans. Circuits Syst. II Analog Digit. Signal Process. 46(1), 78–80 (1999)

    Article  Google Scholar 

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Authors and Affiliations

  1. Shanghai, China

    Guanyu Lian

  2. Department of Computer Science, The University of Texas at Dallas, Richardson, TX, USA

    Kang Zhang

  3. Faculty of Information Technology, Macau University of Science and Technology, Macau, China

    Kang Zhang

Authors
  1. Guanyu Lian
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  2. Kang Zhang
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Correspondence to Guanyu Lian.

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Lian, G., Zhang, K. Transformation of portraits to Picasso’s cubism style. Vis Comput 36, 799–807 (2020). https://doi.org/10.1007/s00371-019-01661-2

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