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Watercolour Rendering of Portraits

  • Paul L. RosinEmail author
  • Yu-Kun Lai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10799)

Abstract

Applying non-photorealistic rendering techniques to stylise portraits needs to be done with care, as facial artifacts are particularly disagreeable. This paper describes a technique for watercolour rendering that uses a facial model to preserve distinctive facial characteristics and reduce unpleasing distortions of the face, while maintaining abstraction and stylisation of the overall image, employing stylistic elements of watercolour such as edge darkening, wobbling, glazing and diffusion.

Keywords

Non-photorealistic rendering Watercolour Portraits 

References

  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. PAMI 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Baltru, T., Robinson, P., Morency, L.P.: OpenFace: an open source facial behavior analysis toolkit. In: Winter Conference on Applications of Computer Vision, pp. 1–10 (2016)Google Scholar
  3. 3.
    Berger, I., Shamir, A., Mahler, M., Carter, E., Hodgins, J.: Style and abstraction in portrait sketching. ACM Trans. Graph. 32(4), 55:1–55:12 (2013)CrossRefGoogle Scholar
  4. 4.
    Bousseau, A.: Watercolor tutorial. Technical report, Grenoble University (2006). maverick.inria.fr/Membres/Adrien.Bousseau/watercolor_tutorial/tutorial1.pdf
  5. 5.
    Bousseau, A., Kaplan, M., Thollot, J., Sillion, F.X.: Interactive watercolor rendering with temporal coherence and abstraction. In: Symposium NPAR, pp. 141–149 (2006)Google Scholar
  6. 6.
    Chen, J., Turk, G., MacIntyre, B.: Watercolor inspired non-photorealistic rendering for augmented reality. In: ACM Symposium on Virtual Reality Software and Technology, pp. 231–234 (2008)Google Scholar
  7. 7.
    Curtis, C.J., Anderson, S.E., Seims, J.E., Fleischer, K.W., Salesin, D.H.: Computer-generated watercolor. In: ACM SIGGRAPH, pp. 421–430 (1997)Google Scholar
  8. 8.
    DiVerdi, S., Krishnaswamy, A., Mäch, R., Ito, D.: Painting with polygons: a procedural watercolor engine. IEEE Trans. TVCG 19(5), 723–735 (2013)Google Scholar
  9. 9.
    Dong, L., Lu, S., Jin, X.: Real-time image-based Chinese ink painting rendering. Multimedia Tools Appl. 69(3), 605–620 (2014)CrossRefGoogle Scholar
  10. 10.
    Doran, P.J., Hughes, J.: Expressive rendering with watercolor. Master’s thesis, Brown University (2013)Google Scholar
  11. 11.
    Fišer, J., Jamriška, O., Simons, D., Shechtman, E., Lu, J., Asente, P., Lukáč, M., Sỳkora, D.: Example-based synthesis of stylized facial animations. ACM Trans. Graph. 36(4), 155 (2017)CrossRefGoogle Scholar
  12. 12.
    Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of CVPR, pp. 2414–2423 (2016)Google Scholar
  13. 13.
    Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: ACM SIGGRAPH, pp. 327–340 (2001)Google Scholar
  14. 14.
    Lagae, A., Lefebvre, S., Drettakis, G., Dutré, P.: Procedural noise using sparse Gabor convolution. ACM Trans. Graph. 28(3), 54–64 (2009)CrossRefGoogle Scholar
  15. 15.
    Lai, Y.K., Rosin, P.L.: Efficient circular thresholding. IEEE Trans. Image Process. 23(3), 992–1001 (2014)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Li, C., Wand, M.: Combining Markov random fields and convolutional neural networks for image synthesis. In: Proceedings of CVPR, pp. 2479–2486 (2016)Google Scholar
  17. 17.
    Liao, J., Yao, Y., Yuan, L., Hua, G., Kang, S.B.: Visual attribute transfer through deep image analogy. In: ACM SIGGRAPH, pp. 120:1–120:15 (2017)CrossRefGoogle Scholar
  18. 18.
    Luft, T., Kobs, F., Zinser, W., Deussen, O.: Watercolor illustrations of CAD data. In: Eurographics, pp. 57–63 (2008)Google Scholar
  19. 19.
    Perlin, K.: An image synthesizer. ACM SIGGRAPH Comput. Graphics 19(3), 287–296 (1985)CrossRefGoogle Scholar
  20. 20.
    Rosin, P.L., Lai, Y.K.: Artistic minimal rendering with lines and blocks. Graph. Models 75(4), 208–229 (2013)CrossRefGoogle Scholar
  21. 21.
    Rosin, P.L., Lai, Y.K.: Non-photorealistic rendering of portraits. In: Proceedings of the Workshop on Computational Aesthetics, pp. 159–170 (2015)Google Scholar
  22. 22.
    Rosin, P.L., Mould, D., Berger, I., Collomosse, J., Lai, Y.K., Li, C., Li, H., Shamir, A., Wand, M., Wang, T., Winnemöller, H.: Benchmarking non-photorealistic rendering of portraits. In: Proceedings of Expressive, pp. 11:1–11:12 (2017)Google Scholar
  23. 23.
    Shu, Z., Shechtman, E., Samaras, D., Hadap, S.: EyeOpener: editing eyes in the wild. ACM Trans. Graph. 36(1), 1:11–1:13 (2017)Google Scholar
  24. 24.
    Wang, J., Jiang, H., Yuan, Z., Cheng, M.M., Hu, X., Zheng, N.: Salient object detection: a discriminative regional feature integration approach. Int. J. Comput. Vis. 123(2), 251–268 (2017)CrossRefGoogle Scholar
  25. 25.
    Wang, M., Wang, B., Fei, Y., Qian, K., Wang, W., Chen, J., Yong, J.H.: Towards photo watercolorization with artistic verisimilitude. IEEE Trans. TVCG 20(10), 1451–1460 (2014)Google Scholar
  26. 26.
    Yan, Z., Zhang, H., Wang, B., Paris, S., Yu, Y.: Automatic photo adjustment using deep neural networks. ACM Trans. Graph. 35(2), 11:1–11:15 (2016)CrossRefGoogle Scholar
  27. 27.
    Zhang, Y., Dong, W., Ma, C., Mei, X., Li, K., Huang, F., Hu, B.G., Deussen, O.: Data-driven synthesis of cartoon faces using different styles. IEEE Trans. Image Process. 26(1), 464–478 (2017)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Cardiff UniversityCardiffUK

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