Article

Motion templates for automatic classification and retrieval of motion capture data

Authors:

Meinard Müller,

Tido RöderAuthors Info & Claims

SCA '06: Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation

Pages 137 - 146

Published: 02 September 2006 Publication History

Abstract

This paper presents new methods for automatic classification and retrieval of motion capture data facilitating the identification of logically related motions scattered in some database. As the main ingredient, we introduce the concept of motion templates (MTs), by which the essence of an entire class of logically related motions can be captured in an explicit and semantically interpretable matrix representation. The key property of MTs is that the variable aspects of a motion class can be automatically masked out in the comparison with unknown motion data. This facilitates robust and efficient motion retrieval even in the presence of large spatio-temporal variations. Furthermore, we describe how to learn an MT for a specific motion class from a given set of training motions. In our extensive experiments, which are based on several hours of motion data, MTs proved to be a powerful concept for motion annotation and retrieval, yielding accurate results even for highly variable motion classes such as cartwheels, lying down, or throwing motions.

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Cited By

Lee SKang TPark JLee JWon J(2023)SAME: Skeleton-Agnostic Motion Embedding for Character AnimationSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618206(1-11)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.1145/3610548.3618206
Wallace BHilton CNymoen KTorresen JMartin CFiebrink R(2023)Embodying an Interactive AI for Dance Through Movement IdeationProceedings of the 15th Conference on Creativity and Cognition10.1145/3591196.3593336(454-464)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3591196.3593336
Durupinar F(2021)Perception of Human Motion Similarity Based on Laban Movement AnalysisACM Symposium on Applied Perception 202110.1145/3474451.3476241(1-7)Online publication date: 16-Sep-2021
https://dl.acm.org/doi/10.1145/3474451.3476241
Show More Cited By

Index Terms

Motion templates for automatic classification and retrieval of motion capture data
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        3D imaging
  2. Computer graphics
    1. Animation

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Information & Contributors

Information

Published In

cover image ACM Conferences

SCA '06: Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation

September 2006

370 pages

ISBN:3905673347

General Chairs:
Carol O'Sullivan
Trinity College Dublin, Ireland
,
Fred Pighin
Industrial Light and Magic, USA

Sponsors

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
EUROGRAPHICS: The European Association for Computer Graphics

Publisher

Eurographics Association

Goslar, Germany

Publication History

Published: 02 September 2006

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SCA06

Sponsor:

SIGGRAPH
EUROGRAPHICS

SCA06: The ACM SIGGRAPH / Eurographics Symposium on Computer Animation

September 2 - 4, 2006

Vienna, Austria

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Overall Acceptance Rate 183 of 487 submissions, 38%

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Cited By

Lee SKang TPark JLee JWon J(2023)SAME: Skeleton-Agnostic Motion Embedding for Character AnimationSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618206(1-11)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.1145/3610548.3618206
Wallace BHilton CNymoen KTorresen JMartin CFiebrink R(2023)Embodying an Interactive AI for Dance Through Movement IdeationProceedings of the 15th Conference on Creativity and Cognition10.1145/3591196.3593336(454-464)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3591196.3593336
Durupinar F(2021)Perception of Human Motion Similarity Based on Laban Movement AnalysisACM Symposium on Applied Perception 202110.1145/3474451.3476241(1-7)Online publication date: 16-Sep-2021
https://dl.acm.org/doi/10.1145/3474451.3476241
Wang SXin YKong DYin B(2019)Unsupervised Learning of Human Pose Distance Metric via Sparsity Locality Preserving ProjectionsIEEE Transactions on Multimedia10.1109/TMM.2018.285902921:2(314-327)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1109/TMM.2018.2859029
Switonski AJosinski HWojciechowski K(2019)Dynamic time warping in classification and selection of motion capture dataMultidimensional Systems and Signal Processing10.1007/s11045-018-0611-330:3(1437-1468)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11045-018-0611-3
Choi MKwon T(2019)Motion rankThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-018-1498-635:2(289-300)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s00371-018-1498-6
Aristidou ACohen-Or DHodgins JChrysanthou YShamir A(2018)Deep motifs and motion signaturesACM Transactions on Graphics10.1145/3272127.327503837:6(1-13)Online publication date: 4-Dec-2018
https://dl.acm.org/doi/10.1145/3272127.3275038
Zerrouki NHouacine A(2018)Combined curvelets and hidden Markov models for human fall detectionMultimedia Tools and Applications10.1007/s11042-017-4549-577:5(6405-6424)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s11042-017-4549-5
Lee TKang DKwon T(2018)Motion normalization method based on an inverted pendulum model for clusteringThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-016-1308-y34:1(29-40)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s00371-016-1308-y
(2017)Sequential data feature selection for human motion recognition via Markov blanketPattern Recognition Letters10.5555/3063157.306324486:C(18-25)Online publication date: 15-Jan-2017
https://dl.acm.org/doi/10.5555/3063157.3063244
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