[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/2393347.2396492acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
abstract

Analysis of dance movements using gaussian processes: extended abstract

Published: 29 October 2012 Publication History

Abstract

This work addresses the Huawei/3DLife Grand Challenge, presenting a novel method for the analysis of dance movements. The approach focuses on the decomposition of the dance movements into elementary motions. Placing this problem into a probabilistic framework, we propose to exploit Gaussian processes to accurately model the different components of the decomposition. The preliminary results, presented in this paper, are very promising. In particular, two applications are considered, illustrating the relevance of the proposed approach, namely the correction of tracking errors and the smoothing of some movements of the teacher to help toward the dance learning.

Supplementary Material

JPG File (d315.jpg)
MP4 File (d315.mp4)

References

[1]
http://3dlife-huawei-gc-submission.blogspot.fr/.
[2]
http://www.openni.org.
[3]
S. Essid, X. Lin, M. Gowing, G. Kordelas, A. Aksay, P. Kelly, T. Fillon, Q. Zhang, A. Dielmann, V. Kitanovski, R. Tournemenne, A. Masurelle, E. Izquierdo, N. E. O'Connor, P. Daras, and G. Richard. A multimodal dance corpus for reseach into interaction between humans in virtual environments. Accepted in Journal on Multimodal User Interfaces, Sp. Issue on Multimodal Corpora, Springer, 2012.
[4]
A. Liutkus, R. Badeau, and G. Richard. Gaussian processes for underdetermined source separation. IEEE Transactions on Signal Processing, 59(7):3155--3167, July 2011.
[5]
C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, 2005.

Cited By

View all
  • (2020)Machine Learning for Intangible Cultural Heritage: A Review of Techniques on Dance AnalysisVisual Computing for Cultural Heritage10.1007/978-3-030-37191-3_6(103-119)Online publication date: 8-Apr-2020

Index Terms

  1. Analysis of dance movements using gaussian processes: extended abstract

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    MM '12: Proceedings of the 20th ACM international conference on Multimedia
    October 2012
    1584 pages
    ISBN:9781450310895
    DOI:10.1145/2393347

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 29 October 2012

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. 3Dlife
    2. dance analysis
    3. gaussian process
    4. grand challenge
    5. interactive environments

    Qualifiers

    • Abstract

    Conference

    MM '12
    Sponsor:
    MM '12: ACM Multimedia Conference
    October 29 - November 2, 2012
    Nara, Japan

    Acceptance Rates

    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 04 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2020)Machine Learning for Intangible Cultural Heritage: A Review of Techniques on Dance AnalysisVisual Computing for Cultural Heritage10.1007/978-3-030-37191-3_6(103-119)Online publication date: 8-Apr-2020

    View Options

    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