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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

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

Abstract


Funded by

国家自然科学基金(61672089)

国家自然科学基金(61273274)

国家自然科学基金(61572064)

国家重点技术研发计划(2012BAH01F03)


Acknowledgment

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.


Supplement

Appendix

调查问卷中的问题和回答统计

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.


References

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  • 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