Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/1180495.1180529acmconferencesArticle/Chapter ViewAbstractPublication PagesvrstConference Proceedingsconference-collections
Article

Learning system for human motion characters of traditional arts

Published: 01 November 2006 Publication History

Abstract

A useful learning system for human motion characters of traditional arts, such as Mai (Japanese classic dance), Kabuki (one of Japan's traditional stage arts), etc., is being developed. In such arts an effective system to pass the tradition down from a top artist to next generations is required. Video contents are generally used to pass the human motions in traditional arts down for non-experts. However, the video contents normally show the human motions as the views from a single direction. If the human motions are presented from orthogonal three-directions at the same time, it comes more useful. So, our learning system produces the three-dimensional human motion by the sequences of views from a single direction in the video contents, then, the motions from any directions can be presented simultaneously with the video contents.In addition, a learner can check the difference in motion between a top artist and him/herself by the producing his/her three-dimensional skeleton motion with our system and the overlapping it with one of the top artist. Here, in the comparison between two different physiques (a top artist and a learner) a simple adjustment method is suggested.In our study standard motion capture processes are employed, but our goal is to develop an original practical training system of performers' motion for beginners in traditional arts. In this paper the developing system is demonstrated for Kyo-mai (Mai originated in Kyoto) as example. The developing system can be useful not only in performing arts but also in industry or in sports.

References

[1]
T. Shiratori, A. Nakazawa and K. Ikeuchi "Detecting Dance Motion Structure through Music Analysis," Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004, pp. 857--862.
[2]
T. Komura, A. Kuroda, and Y. Shinagawa "NiceMeetVR: Facing Professional Baseball Pitchers in the Virtual Batting Cage," ACM Symposium on Applied Computing, 2002, pp. 1060--1065.
[3]
T. Oda, Y. Shinagwa, T. Komura, S. Kobayashi and Y. Maekawa "Virtual Batting Training System Using a Three-Dimensional Haptic Interface," IEEE VR 2004 Workshop, 2004, pp. 97--100.

Index Terms

  1. Learning system for human motion characters of traditional arts

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    VRST '06: Proceedings of the ACM symposium on Virtual reality software and technology
    November 2006
    400 pages
    ISBN:1595933212
    DOI:10.1145/1180495
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 01 November 2006

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. computer graphics
    2. human motion analysis
    3. motion capture
    4. traditional arts
    5. training tools

    Qualifiers

    • Article

    Conference

    VRST06

    Acceptance Rates

    Overall Acceptance Rate 66 of 254 submissions, 26%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 276
      Total Downloads
    • Downloads (Last 12 months)1
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 14 Nov 2024

    Other Metrics

    Citations

    View Options

    Get Access

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media