SCIENTIA SINICA Informationis, Volume 49 , Issue 2 : 229-244(2019) https://doi.org/10.1360/N112018-00204

Automatic generation of Labanotation for national dynamic art digitalization

More info
  • ReceivedNov 25, 2018
  • AcceptedJan 9, 2019
  • PublishedFeb 18, 2019


Funded by






本文感谢罗秉钰专家在拉班舞谱方面的耐心指导, 感谢李松专家在民间文化与技术结合方面的巨大帮助, 感谢文化部民族民间文艺发展中心在设备和数据上的大力支持.




问题1: 根据你的经验, 系统生成的拉班舞谱的准确率大概是多少?

回答1: 68%$\sim~$93%

问题2: 对于一个拉班舞谱记录任务, 你会选择用本系统生成的拉班舞谱作为参考吗? 如果会, 对你的帮助有哪些?

回答2: 8人选择会. “帮助”归纳如下: 有辅助作用, 可以为记录舞谱提供思路, 缩短记录拉班舞谱的时间; 对于有歧义性的动作可以提供参考.

问题3: (看完一段运动捕捉数据完成舞谱记录后)根据你的经验, 记录的拉班舞谱准确率大概是多少? 在生成系统的辅助下, 记录的准确率大概是多少?

回答3: 70%$\sim~$90% (自己记录); 75%$\sim~$93% (系统辅助).

问题4: 反馈意见.

回答4: (1) 生成的舞谱能够反映整体性的动作特点、节奏, 因此可以为记录任务提供思路、减少工作量, 效率提升约20%$\sim~$50% (2) 系统只能处理一个人的运动数据, 对于具有交互性的双人动作无法处理; 对于简单的节奏分明的动作处理的较好, 对于复杂的旋转动作处理的不好.


[1] Guo H. Research on automatic generation of Labanotation based on human motion capture data. Dissertation for Master Degree. Bejing: Beijing Jiaotong University, 2015. Google Scholar

[2] Hutchinson A. Labanotation. J Am Folklore, 1955, 68: 89. Google Scholar

[3] Guest A H. Labanotation or Kinetography Laban: the System of Analyzing and Recording Movement. 3rd ed. New York: Theatre Arts Books, 1970. Google Scholar

[4] Xiang Z R, Zhi J Y, Xu B C, et al. Motion capture technology and its application research review. Comput Appl Res, 2013, 30: 2241--2245. Google Scholar

[5] Johansson G. Visual perception of biological motion and a model for its analysis. Perception Psychophysics, 1973, 14: 201-211 CrossRef Google Scholar

[6] Villegas R, Yang J M, Ceylan D, et al. Neural kinematic networks for unsupervised motion retargetting. 2018,. arXiv Google Scholar

[7] Meredith M, Maddock S. Motion capture file formats explained. Department of Computer Science, University of Sheffield, 2001. Google Scholar

[8] Liang Q H. Research on key technology of motion capture in digital dynamic art. Dissertation for Ph.D. Degree. Beijing: Beijing Jiaotong University, 2016. Google Scholar

[9] Hachimura K, Nakamura M. Method of generating coded description of human body motion from motion-captured data. In: Proceedings of the 10th IEEE International Workshop on Robot and Human Interactive Communication, 2001. 122--127. Google Scholar

[10] Chen H, Qian G, James J. An autonomous dance scoring system using marker-based motion capture. In: Proceedings of the 7th Workshop on Multimedia Signal Processing, 2005. Google Scholar

[11] Choensawat W, Nakamura M, Hachimura K. GenLaban: A tool for generating Labanotation from motion capture data. Multimed Tools Appl, 2015, 74: 10823-10846 CrossRef Google Scholar

[12] Guo H, Miao Z J, Zhu F Y, et al. Automatic labanotation generation based on human motion capture data. In: Proceedings of Chinese Conference on Pattern Recognition, 2014. 426--435. Google Scholar

[13] Zhou Z M, Miao Z J, Wang J J. A system for automatic generation of labanotation from motion capture data. In: Proceedings of the 13th International Conference on Signal Processing (ICSP), 2016. 1031--1034. Google Scholar

[14] Zhou Z M. Research on automatic generation of labanotation based on dynamic programming. Dissertation for Master Degree. Bejing: Beijing Jiaotong University, 2017. Google Scholar

[15] Yu T, Shen X, Li Q. Motion retrieval based on movement notation language. Comp Anim Virtual Worlds, 2005, 16: 273-282 CrossRef Google Scholar

[16] Shen X J, Li Q L, Yu T, et al. Mocap data editing via movement notations. In: Proceedings of International Conference on Computer Aided Design and Computer Graphics, 2006. 463--470. Google Scholar

[17] Wang J, Miao Z, Guo H. Using automatic generation of Labanotation to protect folk dance. J Electron Imag, 2017, 26: 011028 CrossRef ADS Google Scholar

[18] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70: 489-501 CrossRef Google Scholar

[19] Huang G B, Zhou H M, Ding X J. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B, 2012, 42: 513-529 CrossRef PubMed Google Scholar

[20] Huang Z, Yu Y, Gu J. An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine.. IEEE Trans Cybern, 2017, 47: 920-933 CrossRef PubMed Google Scholar

[21] Li M, Miao Z. Automatic Labanotation generation from motion-captured data based on hidden Markov models. In: Proceedings of the 4th Asia Conference on Pattern Recognition, 2017. Google Scholar

[22] Guest A H. Laban Recording Method: Action Analysis and Recording System. Beijing: China Foreign Translation and Publishing Co., Ltd., 2013. Google Scholar

  • Figure 1

    Exampel of Labanotation with 4 pages

  • Figure 2

    Structure of Labanotation. The “L”, “C” and “R”, representleft, center and right, respectively

  • Figure 3

    27 basic symbols of Labanotation and the corresponding spatialpartition

  • Figure 4

    (Color online) Flow chart of generating Labanotation based onhuman motion capture data

  • Figure 5

    2/4 beat rhythm of Labanotation

  • Figure 6

    (Color online) Body plane and vector that represents the front ofhuman body

  • Figure 7

    (Color online) Generated Labanotation based on the motion capturedata of drum Yangko dance (partially modified)

  • Figure 8

    (Color online) Comparison of original human motion and thecorresponding generated Labanotation

  • Figure 9

    (Color online) Video screenshots of traditional routine clips ofShandong drum Yangko that synthesized with video data (three channels),motion capture data and generated Labanotation. There are nine screenshots,each of which is a live video shot from three different angles on the left,with motion capture data in the middle and corresponding Labanotation on theright

  • Figure 10

    Comparison of expert records and generated Labanotation of sixkinds of basic motion. (a) Go forward;protect łinebreak (b) go right forward; (c) forward low, right low; (d) forward low, origin low; (e) forward, right, backward; (f) backward, left, forward

  • Table 1   Relationships between angle $\alpha~$ and the horizontal direction ofLabanotation
    Value of angle $\alpha~$ Horizontal direction
    $[-22.5^\circ,~22.5^\circ]$ Forward
    $(22.5^\circ,~67.5^\circ]$ Left forward
    $(67.5^\circ,~112.5^\circ]$ Left
    $(112.5^\circ,~157.5^\circ]$ Left back
    $(157.5^\circ,~180^\circ]~\cup~[-180^\circ,~-157.5^\circ)$ Back
    $[-157.5^\circ,~-112.5^\circ)$ Right back
    $[-112.5^\circ,~-67.5^\circ)$ Right
    $[-67.5^\circ,~-22.5^\circ)$ Right forward
  • Table 2   Relationships between angle $\beta~$ and the vertical direction ofLabanotation
    Absolute value of angle $\beta~$ Vertical direction
    $[0^\circ,~30^\circ]$ High
    $(30^\circ,~150^\circ]$ Middle
    $(150^\circ,~180^\circ]$ Low
  • Table 3   Comparison of the approach based on rules , template , HMM and our method
    Accuracy (% Rules [12] Template [13] HMM [21] Ours
    Left arm 80.25 71.03 83.69
    Right arm 82.50 73.42 83.17
    Left leg 64.23 85.83 87.09 88.72
    Right leg 60.71 83.90 86.62 86.24
    Weighted average 68.37 80.90 86.20

Contact and support