Advertisement

Swarm-Based Identification of Animation Key Points from 2D-medialness Maps

  • Prashant AparajeyaEmail author
  • Frederic Fol Leymarie
  • Mohammad Majid al-Rifaie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11453)

Abstract

In this article we present the use of dispersive flies optimisation (DFO) for swarms of particles active on a medialness map – a 2D field representation of shape informed by perception studies. Optimising swarms activity permits to efficiently identify shape-based keypoints to automatically annotate movement and is capable of producing meaningful qualitative descriptions for animation applications. When taken together as a set, these keypoints represent the full body pose of a character in each processed frame. In addition, such keypoints can be used to embody the notion of the Line of Action (LoA), a well known classic technique from the Disney studios used to capture the overall pose of a character to be fleshed out. Keypoints along a medialness ridge are local peaks which are efficiently localised using DFO driven swarms. DFO is optimised in a way so that it does not need to scan every image pixel and always tend to converge at these peaks. A series of experimental trials on different animation characters in movement sequences confirms the promising performance of the optimiser over a simpler, currently-in-use brute-force approach.

Keywords

Line of Action Medialness Dispersive flies optimisation Swarm intelligence Dominant points Animation 

References

  1. 1.
    Loomis, A.: Successful Drawing. Viking Books, New York (1951)Google Scholar
  2. 2.
    Leymarie, F.F., Aparajeya, P.: Medialness and the perception of visual art. Art Percept. 5(2), 169–232 (2017)CrossRefGoogle Scholar
  3. 3.
    Bregler, C., Loeb, L., Chuang, E., Deshpande, H.: Turning to the masters: motion capturing cartoons. ACM Trans. Graph. 21(3), 399–407 (2002)CrossRefGoogle Scholar
  4. 4.
    Guay, M., Cani, M.P., Ronfard, R.: The line of action: an intuitive interface for expressive character posing. ACM Trans. Graph. 32(6) (2013). Article no. 205Google Scholar
  5. 5.
    Guay, M., Ronfard, R., Gleicher, M., Cani, M.P.: Adding dynamics to sketch-based character animations. In: Proceedings of the Eurographics/ACM Symposium: Expressive Graphics – Sketch-Based Interfaces and Modeling, Istanbul, Turkey, June 2015Google Scholar
  6. 6.
    Kovács, I., Fehér, Á., Julesz, B.: Medial-point description of shape: a representation for action coding and its psychophysical correlates. Vis. Res. 38(15), 2323–2333 (1998)CrossRefGoogle Scholar
  7. 7.
    Leymarie, F.F., Aparajeya, P., MacGillivray, C.: Point-based medialness for movement computing. In: Workshop on Movement and Computing (MOCO), IRCAM, Paris, pp. 31–36. ACM (2014)Google Scholar
  8. 8.
    Burbeck, C.A., Pizer, S.M.: Object representation by cores: identifying and representing primitive spatial regions. Vis. Res. 35(13), 1917–1930 (1995)CrossRefGoogle Scholar
  9. 9.
    Kovács, I.: “Hot spots” and dynamic coordination in Gestalt perception. In: Von der Malsburg, C.V., Phillips, W.A., Singer, W. (eds.) Dynamic Coordination in the Brain: From Neurons to Mind, Chap. 14, pp. 215–28, Strüngmann Forum Reports. MIT Press (2010)Google Scholar
  10. 10.
    Eberly, D., Gardner, R., Morse, B., Pizer, S., Scharlach, C.: Ridges for image analysis. J. Math. Imaging Vis. 4(4), 353–373 (1994)CrossRefGoogle Scholar
  11. 11.
    Aparajeya, P., Leymarie, F.F.: Point-based medialness for 2D shape description and identification. Multimedia Tools Appl. 75(3), 1667–1699 (2016)CrossRefGoogle Scholar
  12. 12.
    al-Rifaie, M.M.: Dispersive flies optimisation. In: Ganzha, M., Maciaszek, L.M.P. (eds.) Proceedings of the 2014 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, vol. 2, pp. 529–538. IEEE (2014).  https://doi.org/10.15439/2014F142
  13. 13.
    Downes, J.: The swarming and mating flight of diptera. Ann. Rev. Entomol. 14(1), 271–298 (1969)CrossRefGoogle Scholar
  14. 14.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948. IEEE Service Center, Piscataway (1995)Google Scholar
  15. 15.
    Goldberg, D.E.: Genetic Algorithms in Search. Optimization and Machine Learning. Addison-Wesley Longman Publishing Co. Inc., Boston (1989)zbMATHGoogle Scholar
  16. 16.
    Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces (1995). tR-95-012. http://www.icsi.berkeley.edu/~storn/litera.html
  17. 17.
    al-Rifaie, M.M., Aber, A.: Dispersive flies optimisation and medical imaging. In: Fidanova, S. (ed.) Recent Advances in Computational Optimization. SCI, vol. 610, pp. 183–203. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-21133-6_11CrossRefGoogle Scholar
  18. 18.
    Alhakbani, H.: Handling class imbalance using swarm intelligence techniques, hybrid data and algorithmic level solutions. Ph.D. thesis, Goldsmiths, University of London, London, United Kingdom (2018)Google Scholar
  19. 19.
    Alhakbani, H.A., al-Rifaie, M.M.: Optimising SVM to classify imbalanced data using dispersive flies optimisation. In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, Prague, Czech Republic, 3–6 September 2017, pp. 399–402. IEEE (2017)Google Scholar
  20. 20.
    Oroojeni, H., al-Rifaie, M.M., Nicolaou, M.A.: Deep neuroevolution: training deep neural networks for false alarm detection in intensive care units. In: European Association for Signal Processing (EUSIPCO) 2018, pp. 1157–1161. IEEE (2018)Google Scholar
  21. 21.
    al-Rifaie, M.M., Ursyn, A., Zimmer, R., Javid, M.A.J.: On symmetry, aesthetics and quantifying symmetrical complexity. In: Correia, J., Ciesielski, V., Liapis, A. (eds.) EvoMUSART 2017. LNCS, vol. 10198, pp. 17–32. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55750-2_2CrossRefGoogle Scholar
  22. 22.
    King, M., al Rifaie, M.M.: Building simple non-identical organic structures with dispersive flies optimisation and A* path-finding. In: AISB 2017: Games and AI, pp. 336–340 (2017)Google Scholar
  23. 23.
    al Rifaie, M.M., Leymarie, F.F., Latham, W., Bishop, M.: Swarmic autopoiesis and computational creativity. Connection Sci., 1–19 (2017).  https://doi.org/10.1080/09540091.2016.1274960
  24. 24.
    Ceseracciu, E., Sawacha, Z., Cobelli, C.: Comparison of markerless and marker-based motion capture technologies through simultaneous data collection during gait: proof of concept. PloS One 9(3) (2014)Google Scholar
  25. 25.
    Aparajeya, P., Petresin, V., Leymarie, F.F., Rueger, S.: Movement description and gesture recognition for live media arts. In: 12th European Conference on Visual Media Production, pp. 19–20. ACM (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Headers Ltd.LondonUK
  2. 2.Department of ComputingGoldsmiths, University of LondonLondonUK
  3. 3.School of Computing and Mathematical SciencesUniversity of GreenwichLondonUK

Personalised recommendations