Skip to main content

Towards AI Aesthetics: Human-AI Collaboration in Creating Chinese Landscape Painting

  • Conference paper
  • First Online:
Culture and Computing. Interactive Cultural Heritage and Arts (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12794))

Included in the following conference series:

Abstract

Based on the analysis of human-AI (artificial intelligence) collaboration in creating Chinese landscape painting, this article aims to develop the relationship between AI aesthetics and HCI in the context of Chinese aesthetics. We construct a multi-level analysis framework and propose three unique AI Aesthetics models under the framework—the dynamic brushwork (bi-mo) model, the imaged mind (xin-yin) model, and the embodied narrative (shi-jing) model. We believe that the most promising AI aesthetics approach is to promote the collaboration between human and AI. The primary research task at present is to develop interactive creation platforms. In the long run, brain-AI interactive communication will help realize mutual inspiration between human and AI, thereby stimulating more creativity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Galanter, P.: Computational aesthetic evaluation: past and future. In: McCormack, J., Inverno, M. (eds.) Computers and Creativity, pp. 255–293. Springer, Berlin. (2012)

    Chapter  Google Scholar 

  2. Manovich, L.: AI Aesthetics. Strelka Press, Moskva (2018)

    Google Scholar 

  3. Fong, C.W.: Images of the Mind: Selections from the Edward L. Elliott Family and John B. Elliott Collections of Chinese Painting and Calligraphy at the Art Museum, Princeton University. Princeton University Press, New Jersey (1984)

    Google Scholar 

  4. Fong, C.W.: Beyond Representation: Chinese Painting and Calligraphy, 8th–14th Century. Yale University Press, New Haven (1992)

    Google Scholar 

  5. Fan, Z., Li, Y., Yu, J., Zhang, K.: Visual complexity of Chinese ink paintings. In: Stephen, N.S. (Eds) ACM Symposium on Applied Perception 2017, Vol. 9, pp. 1–8. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3119881.3119883

  6. Jia, B., Brandt, J., Mech, R., Kim, B., Manocha, D.: LPaintB: Learning to Paint from Self-supervision (2019). arXiv preprint: arXiv:1906.06841

  7. Mordvintsev, A.: Inceptionism: Going Deeper into Neural Networks (2015). https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html. Accessed 8 Jan 2021.

  8. Gatys, L.A., Ecker, A.S., Bethge, M.: A Neural Algorithm of Artistic Style (2015). arXiv preprint: arXiv:1508.06576

  9. Ha, D., Eck, D.: A Neural Representation of Sketch Drawings (2017). arXiv preprint: arXiv:1704.03477

  10. Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic Image Synthesis with Spatially Adaptive Normalization (2019). arXiv preprint: arXiv:1903.07291

  11. Bidgoli, A., Guevara, M.D.L., Hsiung, C., Oh, J., Kang, E.: Artistic style in robotic painting; a machine learning approach to learning brushstroke from human artists. In: 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 412–418. IEEE Press, New York (2020)

    Google Scholar 

  12. Mousavian, A., Eppner, C., Fox, D.: 6-DOF GraspNet: variational grasp generation for object manipulation. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2901–2910. IEEE Press, New York (2019)

    Google Scholar 

  13. Gordon, D., Kadian, A., Parikh, D., Hoffman, J., Batra, D.: SplitNet: Sim2Sim and Task2Task transfer for embodied visual navigation. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1022–1031. IEEE Press, New York (2019)

    Google Scholar 

  14. Litany, O., Morcos, A., Sridhar, S., Guibas, L., Hoffman, J.: Representation learning through latent canonicalizations. In: 2021 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 645–654. IEEE Press, New York (2021)

    Google Scholar 

  15. Chan, C., Ginosar, S., Zhou, T., Efros, A.A.: Everybody dance now. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5933–5942. IEEE Press, New York (2019).

    Google Scholar 

  16. Guo, C., Hou, Z.X., Shi, Y.Z., Xu, J., Yu, D.D.: A virtual 3D interactive painting method for chinese calligraphy and painting based on real-time force feedback technology. Front. Inf. Technol. Electron. Eng. 18(11), 184–195 (2017)

    Article  Google Scholar 

  17. Zheng, S.Z., Hou, Z.X., Guo, C., Yang, G.Q.: The simulation of the half-dry stroke based on the force feedback technology. J. Comput. Aid. Des. Comput. Graph. 28(6), 1016–1024 (2016)

    Google Scholar 

  18. Zhang, G., Cheng, J.L., Song, J., Guo, J.Q., Zhou, C.R.: Chinese landscape painting automated generation model based on generative adversarial networks. Comput. Telecommun. 280(03), 5–9 (2020)

    Google Scholar 

  19. Sheng, J.C., Li, Y.Z.: Learning artistic objects for improved classification of chinese paintings. J. Image Graph. 23(8), 1193–1206 (2018)

    Google Scholar 

  20. Tong, Y.: Research on the style transfer model of Chinese paintings based on deep network. Chin. Mus. 142(3), 139–145 (2020)

    Google Scholar 

  21. Alice, X.: End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks(2020). arXiv preprint: arXiv:2011.05552

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yiyuan Huang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

Âİ 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chang, R., Huang, Y. (2021). Towards AI Aesthetics: Human-AI Collaboration in Creating Chinese Landscape Painting. In: Rauterberg, M. (eds) Culture and Computing. Interactive Cultural Heritage and Arts. HCII 2021. Lecture Notes in Computer Science(), vol 12794. Springer, Cham. https://doi.org/10.1007/978-3-030-77411-0_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77411-0_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77410-3

  • Online ISBN: 978-3-030-77411-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics