“Synchronized Hand Difference Visualization for Piano Learning” by Liu, Wu, Liao, Nishioka, Furuya, et al. …

  • ©Ruofan Liu, Erwin Wu, Chen-Chieh Liao, Hayato Nishioka, Shinichi Furuya, and Hideki Koike



Entry Number: 04


    Synchronized Hand Difference Visualization for Piano Learning



    When learning a dexterous skill such as playing the piano, people commonly watch videos of a teacher. However, this conventional way has some downsides such as limited information to be retrieved and less intuitive instructions. We propose a virtual training system by visualizing differences between hands to provide intuitive feedback for skill acquisition. After synchronizing the data, two visual cues are proposed including a hand-overlay manner and a two-keyboards visualization. A pilot study confirm the superiority of the proposed methods over conventional video-viewing.


    Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. 2019. Temporal Cycle-Consistency Learning. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 1801–1810. https://doi.org/10.1109/CVPR.2019.00190Google Scholar
    Werner Goebl and Caroline Palmer. 2013. Temporal Control and Hand Movement Efficiency in Skilled Music Performance. PLOS ONE 8, 1 (01 2013), 1–10. https://doi.org/10.1371/journal.pone.0050901Google Scholar
    Seita Kayukawa, Keita Higuchi, Ryo Yonetani, Masanori Nakamura, Yoichi Sato, and Shigeo Morishima. 2018. Dynamic Object Scanning: Object-Based Elastic Timeline for Quickly Browsing First-Person Videos. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI EA ’18). Association for Computing Machinery, New York, NY, USA, 1–4. https://doi.org/10.1145/3170427.3186501Google ScholarDigital Library
    Takayuki Nozawa, Erwin Wu, Florian Perteneder, and Hideki Koike. 2019. Visualizing Expert Motion for Guidance in a VR Ski Simulator. In ACM SIGGRAPH 2019 Posters (Los Angeles, California) (SIGGRAPH ’19). Association for Computing Machinery, New York, NY, USA, Article 64, 2 pages. https://doi.org/10.1145/3306214.3338561Google ScholarDigital Library

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