“Imaginary Stroke Movement Measurement and Visualization” by Lyu, Zhang and Yuan

  • ©Ruimin Lyu, Tianqin Zhang, and Zhaolin Yuan




    Imaginary Stroke Movement Measurement and Visualization



    When viewing visual artworks, one can feel the suggestive movement from the brushstrokes. This phenomenon has been recorded widely in literature on art theory, and its physiological basis has been found in neuroaesthetic studies, but there is no method to measure its details at present. In this paper, two experiments are designed to measure the velocity sense and the trace sense, which are the instantaneous and cumulative representations of the same content—the kinetic feeling of strokes, respectively. Furthermore, various visualizations are designed for the two kinds of experimental data as artistic recreation of traditional artworks. In addition, the quantitative analysis is performed on the imaginary stroke movement, showing that imaginary stroke movement can be studied by mathematics.


    1. Daniel Berio, Memo Akten, Frederic Fol Leymarie, Mick Grierson, and Réjean Plamondon. 2017a. Calligraphic stylisation learning with a physiologically plausible model of movement and recurrent neural networks. In Proceedings of the 4th International Conference on Movement Computing (MOCO ’17), 1–8. DOI: https://doi.org/10.1145/3077981.3078049

    2. Daniel Berio, Sylvain Calinon, and Frederic Fol Leymarie. 2017b. Generating calligraphic trajectories with model predictive control. In Proceedings of Graphics Interface 2017, May 16–19, 2017, Edmonton, Alberta, 132–139. DOI: https://doi.org/10.20380/GI2017.17

    3. Yihang Bo, Jinhui Yu, and Kang Zhang. 2018. Computational aesthetics and applications. Vis. Comput. Ind. Biomed. Art 1, 6. DOI: https://doi.org/10.1186/s42492-018-0006-1

    4. Rebecca Chamberlain, Caitlin Mullin, Daniel Berio, Frederic Fol Leymarie, and Johan Wagemans. 2020. Aesthetics of graffiti: Comparison to text-based and pictorial artforms. Empirical Studies of the Arts. DOI: https://doi.org/10.1177/0276237420951415

    5. Anjan Chatterjee and Oshin Vartanian. 2016. Neuroscience of aesthetics. Ann. N.Y. Acad. Sci. 1369, 1 (April 2016), 172–194. DOI: https://doi.org/10.1111/nyas.13035

    6. ZhenBao Fan, Kang Zhang, and XianJun Sam Zheng. 2019. Evaluation and analysis of white space in Wu Guanzhong’s Chinese paintings. Leonardo 52, 2 (April 1, 2019), 111–116. DOI: https://doi.org/10.1162/leon_a_01409

    7. David Freedberg and Vittorio Gallese. 2007. Motion, emotion and empathy in esthetic experience. Trends in Cognitive Sciences 11, 5 (May 1, 2007), 197–203. DOI: https://doi.org/10.1016/j.tics.2007.02.003

    8. Vittorio Gallese. 2019. Embodied simulation: Its bearing on aesthetic experience and the dialogue between neuroscience and the humanities. Gestalt Theory 41, 2 (July 2019), 113–127. DOI: https://doi.org/10.2478/gth-2019-0013

    9. Günther Knoblich, Eva Seigerschmidt, Rüdiger Flach, and Wolfgang Prinz. 2002. Authorship effects in the prediction of handwriting strokes: Evidence for action simulation during action perception. Q. J. Exp. Psychol. Sect. A Hum. Exp. Psychol. 55, 1027–1046. DOI: https://doi.org/10.1080/02724980143000631

    10. Lothar Ledderose. 1980. Mi Fu and the Classical Tradition of Chinese Calligraphy, [xiii], 131 pp., front., 50 pis. Princeton, NJ: Princeton University Press, 1979. Bull. Sch. Orient. African Stud. DOI: https://doi.org/10.1017/s0041977x00137826

    11. Stephen E. Palmer, Karen B. Schloss, and Jonathan Sammartino. 2012. Visual aesthetics and human preference. Annual Review of Psychology 64. DOI: https://doi.org/10.1146/annurev-psych-120710-100504

    12. Jaume Rigau, Miquel Feixas, and Mateu Sbert. 2008. Informational aesthetics measures. IEEE Computer Graphics and Applications 28, 2 (March 2008), 24–34. DOI: https://doi.org/10.1109/MCG.2008.34

    13. Beatrice Sbriscia-Fioretti, Cristina Berchio, David Freedberg, Vittorio Gallese, and Maria Alessandra Umiltà. 2013. ERP modulation during observation of abstract paintings by Franz Kline. PLOS ONE 8, 10, e75241. DOI: https://doi.org/10.1371/journal.pone.0075241

    14. Ines Schindler, Georg Hosoya, Winfried Menninghaus, Ursula Beermann, Valentin Wagner, Michael Eid, and Klaus R. Scherer. 2017. Measuring aesthetic emotions: A review of the literature and a new assessment tool. PLOS ONE 12. DOI: https://doi.org/10.1371/journal.pone.0178899

    15. Luca F. Ticini, Laura Rachman, Jerome Pelletier, and Stephanie Dubal. 2014. Enhancing aesthetic appreciation by priming canvases with actions that match the artist’s painting style. Front. Hum. Neurosci. 8, 391. DOI: https://doi.org/10.3389/fnhum.2014.00391

    16. Maria Alessandra Umiltà, Cristina Berchio, Mariateresa Sestito, David Freedberg, and Vittorio Gallese. 2012. Abstract art and cortical motor activation: an EEG study. Frontiers in Human Neuroscience 6.

    17. Li-Jie Yang, Tian-Chen Xu, Xiao-Shan Li, and En-Hua Wu. 2014. Feature-oriented writing process reproduction of Chinese calligraphic artwork. In SIGGRAPH Asia 2014 Technical Briefs. Article 5, 1–4. DOI: https://doi.org/10.1145/2669024.2669032

ACM Digital Library Publication:

Overview Page:

Art Paper/Presentation Type: