Article

From motion capture data to character animation

Authors:

Dengming ZhuAuthors Info & Claims

VRST '06: Proceedings of the ACM symposium on Virtual reality software and technology

Pages 165 - 168

https://doi.org/10.1145/1180495.1180528

Published: 01 November 2006 Publication History

Abstract

In this paper, we propose a practical and systematical solution to the mapping problem that is from 3D marker position data recorded by optical motion capture systems to joint trajectories together with a matching skeleton based on least-squares fitting techniques. First, we preprocess the raw data and estimate the joint centers based on related efficient techniques. Second, a skeleton of fixed length which precisely matching the joint centers are generated by an articulated skeleton fitting method. Finally, we calculate and rectify joint angles with a minimum angle modification technique. We present the results for our approach as applied to several motion-capture behaviors, which demonstrates the positional accuracy and usefulness of our method.

References

[1]

Autodesk. Character studio. http://www.autodesk.com.]]

[2]

Autodesk. Motionbuilder. http://www.autodesk.com.]]

[3]

J. Cameron and J. Lasenby. A real-time sequential algorithm for human joint localization. In Proc. SIGGRAPH '05, 2005. posters(111).]]

Digital Library

[4]

E. T. Center. Marker placement guide. Carnegie Mellon University, 2005. http://www.etc.cmu.edu/projects/mastermotion/Documents/markerPlacementGuide.doc.]]

[5]

P. Cerveri, A. Pedotti, and G. Ferrigno. Robust recovery of human motion from video using kalman filters and virtual humans. Hun Mov Sci, 22(3):377--404, 2003.]]

[6]

K.-J. Choi, S.-H. Park, and H.-S. Ko. Processing motion capture data to achieve positional accuracy. Graphical Models and Image, 61(5):260--273, September 1999.]]

Digital Library

[7]

J. Davis, M. Agrawala, E. Chuang, Z. Popovic, and D. Salesin. A sketching interface for acticulated figure animation. In Proc. Eurographics/SIGGRAPH Symposium on Computer animation '03, pages 320--328, 2003.]]

Digital Library

[8]

B. K. Horn. Closed-form solution of absolute orientation using unit quaternions. J. Optical Society of America A, 4(4):629--642, 1987.]]

[9]

A. G. Kirk, J. F. O'Brien, and D. A. Forsyth. Skeletal parameter estimation from optical motion capture data. In IEEE Conf. on Computer Vision and Pattern Recognition (2), pages 782--788, 2005.]]

Digital Library

[10]

B. Li, Q. Meng, and H. Holstein. Articulated pose identification with sparse point features. IEEE Trans. Systems, Man and Cybernetics, 34(3):1412--1422, 2004.]]

Digital Library

[11]

J. F. O'Brien, B. E. Bodenheimer, G. J. Brostow, and J. K. Hodgins. Automatic joint parameter estimation from magnetic motion capture data. In Proc. Graphics Interface '00, pages 53--60, 2000.]]

[12]

M. H. Schwartz and A. Rozumalski. A new method for estimating joint parameters from motion data. J. Biomechanics, 38(1):107--116, 2005.]]

[13]

G. K. Seidel, D. M. Marchinda, M. Dijkers, and R. W. S. Little. Hip joint center location from palpable bony landmarks a cadaver study. J. Biomechanics, 28(8):995--998, 1995.]]

[14]

M.-C. Silaghi, R. Plankers, R. Boulic, P. Fua, and D. Thalmann. Local and global skeleton fitting techniques for optical motion capture. Lecture Notes in Computer Science, 1537:26--40, 1998.]]

Digital Library

[15]

S. Umeyama. Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell., 13(4):376--380, 1991.]]

Digital Library

[16]

G. Wen, Z. Wang D. Zhu, and S. Xia. Least-squares fitting of multiple M-dimensional point sets. The Visual Computer, 22(6):387--398, 2006.]]

Digital Library

[17]

V. B. Zordan and N. C. V. D. Horst. Mapping optical motion capture data to skeletal motion using a physical model. In Proc. Eurographics/SIGGRAPH Symposium on Computer animation '03, pages 245--250, 2003.]]

Digital Library

Cited By

Kwon BLee S(2020)Ensemble Learning for Skeleton-Based Body Mass Index ClassificationApplied Sciences10.3390/app1021781210:21(7812)Online publication date: 4-Nov-2020
https://doi.org/10.3390/app10217812
Lv NJiang ZHuang YMeng XMeenakshisundaram GPeng J(2018)Generic Content-Based Retrieval of Marker-Based Motion Capture DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2017.270262024:6(1969-1982)Online publication date: 1-Jun-2018
https://doi.org/10.1109/TVCG.2017.2702620
Fleury ACohen-McFarlane MLing YChau T(2017)Predicting Linear Elongation With Conductive Thread-Based SensorsIEEE Sensors Journal10.1109/JSEN.2017.274345917:20(6537-6548)Online publication date: 15-Oct-2017
https://doi.org/10.1109/JSEN.2017.2743459
Show More Cited By

Index Terms

From motion capture data to character animation

Recommendations

Dynamic response for motion capture animation

Human motion capture embeds rich detail and style which is difficult to generate with competing animation synthesis technologies. However, such recorded data requires principled means for creating responses in unpredicted situations, for example ...
Entropy-based motion extraction for motion capture animation: Motion Capture and Retrieval
CASA 2005

In this paper, we present a new segmentation solution for extracting motion patterns from motion capture data by searching for critical keyposes in the motion sequence. A rank is established for critical keyposes that identifies the significance of the ...
Using motion capture for interactive motion editing
VRCAI '14: Proceedings of the 13th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry

Motion capture technology has been widely used for creating character motions. Motion editing is usually also required to adjust captured motions. Because character poses which include joint rotations, body positions, and orientations are high-...

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

General Chairs:
Mel Slater
ICREA & UCL, UK
,
Yoshifumi Kitamura
Osaka University, Japan
,
Program Chairs:
Ayellet Tal
Technion, Israel
,
Angelos Amditis
ICCS-NTUA, Greece
,
Yiorgos Chrysanthou
University of Cyprus, Cyprus

Copyright © 2006 ACM.

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.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

VRST06

Sponsor:

VRST06: The ACM Symposium on Virtual Reality Software and Technology 2006

November 1 - 3, 2006

Limassol, Cyprus

Acceptance Rates

Overall Acceptance Rate 66 of 254 submissions, 26%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
1,166
Total Downloads

Downloads (Last 12 months)21
Downloads (Last 6 weeks)2

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kwon BLee S(2020)Ensemble Learning for Skeleton-Based Body Mass Index ClassificationApplied Sciences10.3390/app1021781210:21(7812)Online publication date: 4-Nov-2020
https://doi.org/10.3390/app10217812
Lv NJiang ZHuang YMeng XMeenakshisundaram GPeng J(2018)Generic Content-Based Retrieval of Marker-Based Motion Capture DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2017.270262024:6(1969-1982)Online publication date: 1-Jun-2018
https://doi.org/10.1109/TVCG.2017.2702620
Fleury ACohen-McFarlane MLing YChau T(2017)Predicting Linear Elongation With Conductive Thread-Based SensorsIEEE Sensors Journal10.1109/JSEN.2017.274345917:20(6537-6548)Online publication date: 15-Oct-2017
https://doi.org/10.1109/JSEN.2017.2743459
Li MLeung H(2016)Graph-based representation learning for automatic human motion segmentationMultimedia Tools and Applications10.1007/s11042-016-3480-575:15(9205-9224)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s11042-016-3480-5
Long JJones M(2013)Reconstructing 3D tree models using motion capture and particle flowInternational Journal of Computer Games Technology10.1155/2013/3631602013(5-5)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1155/2013/363160
Wang YWu CChung P(2013)Constraint-based Correspondence Matching for Stereo-based Interactive Robotic Massage MachineJournal of Intelligent and Robotic Systems10.1007/s10846-013-9831-972:2(179-196)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1007/s10846-013-9831-9
Long JJones M(2012)3D tree modeling using motion capture2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications10.1109/PMA.2012.6524841(242-249)Online publication date: Oct-2012
https://doi.org/10.1109/PMA.2012.6524841
Xiao JZhuang YWu FGuo TLiang Z(2010)A group of novel approaches and a toolkit for motion capture data reusingMultimedia Tools and Applications10.1007/s11042-009-0329-147:3(379-408)Online publication date: 1-May-2010
https://dl.acm.org/doi/10.1007/s11042-009-0329-1
Xia SWang Z(2009)Recent advances on virtual human synthesisScience in China Series F: Information Sciences10.1007/s11432-009-0088-752:5(741-757)Online publication date: 15-May-2009
https://doi.org/10.1007/s11432-009-0088-7
Zheng YHicks YMarshall DCosker D(2009)Real-Time Generation of Interactive Virtual Human BehavioursComputer Vision and Computer Graphics. Theory and Applications10.1007/978-3-642-10226-4_6(70-82)Online publication date: 2009
https://doi.org/10.1007/978-3-642-10226-4_6

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents