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Watercolour Rendering of Portraits

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Image and Video Technology (PSIVT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10799))

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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.

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Notes

  1. 1.

    For example, wet in wet is a watercolour technique in which paint is specifically applied to wet paper.

  2. 2.

    Computing edges from \(A_H\) after it is resized to the original \(I_O\) size gives similar results, but the edges are still slightly thickened, and the additional more detailed mid-scale edges retained by the preferred method improve the watercolour effect.

References

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  4. Bousseau, A.: Watercolor tutorial. Technical report, Grenoble University (2006). maverick.inria.fr/Membres/Adrien.Bousseau/watercolor_tutorial/tutorial1.pdf

  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. 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. 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. 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. Dong, L., Lu, S., Jin, X.: Real-time image-based Chinese ink painting rendering. Multimedia Tools Appl. 69(3), 605–620 (2014)

    Article  Google Scholar 

  10. Doran, P.J., Hughes, J.: Expressive rendering with watercolor. Master’s thesis, Brown University (2013)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: ACM SIGGRAPH, pp. 327–340 (2001)

    Google Scholar 

  14. Lagae, A., Lefebvre, S., Drettakis, G., Dutré, P.: Procedural noise using sparse Gabor convolution. ACM Trans. Graph. 28(3), 54–64 (2009)

    Article  Google Scholar 

  15. Lai, Y.K., Rosin, P.L.: Efficient circular thresholding. IEEE Trans. Image Process. 23(3), 992–1001 (2014)

    Article  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

  18. Luft, T., Kobs, F., Zinser, W., Deussen, O.: Watercolor illustrations of CAD data. In: Eurographics, pp. 57–63 (2008)

    Google Scholar 

  19. Perlin, K.: An image synthesizer. ACM SIGGRAPH Comput. Graphics 19(3), 287–296 (1985)

    Article  Google Scholar 

  20. Rosin, P.L., Lai, Y.K.: Artistic minimal rendering with lines and blocks. Graph. Models 75(4), 208–229 (2013)

    Article  Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Cardiff University, Cardiff, CF24 3AA, UK

    Paul L. Rosin & Yu-Kun Lai

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  1. Paul L. Rosin
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  2. Yu-Kun Lai
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Correspondence to Paul L. Rosin .

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Editors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Shin'ichi Satoh

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Rosin, P.L., Lai, YK. (2018). Watercolour Rendering of Portraits. In: Satoh, S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science(), vol 10799. Springer, Cham. https://doi.org/10.1007/978-3-319-92753-4_22

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  • DOI: https://doi.org/10.1007/978-3-319-92753-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92752-7

  • Online ISBN: 978-3-319-92753-4

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