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Author, Editor

Author(s):

de Aguiar, Edilson
Theobalt, Christian
Seidel, Hans-Peter

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Editor(s):

Bebis, George
Boyle, Richard
Parvin, Bahram
Koracin, Darko
Remagnino, Paolo
Nefian, Ara
Meenakshisundaram, Gopi
Pascucci, Valerio
Zara, Jiri
Molineros, Jose
Theisel, Holger
Malzbender, Tom

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Not MPII Editor(s):

Bebis, George
Boyle, Richard
Parvin, Bahram
Koracin, Darko
Remagnino, Paolo
Nefian, Ara
Meenakshisundaram, Gopi
Pascucci, Valerio
Zara, Jiri
Molineros, Jose
Malzbender, Tom

BibTeX cite key*:

deAguiar_isvc2006

Title, Booktitle

Title*:

Automatic Learning of Articulated Skeletons from 3D Marker Trajectories

Booktitle*:

Advances in Visual Computing : Second International Symposium, ISVC 2006, Part I

Event, URLs

URL of the conference:


URL for downloading the paper:

http://www.springerlink.com/content/26760v071m0962m5/fulltext.pdf

Event Address*:

Lake Tahoe, NV, USA

Language:

English

Event Date*
(no longer used):


Organization:


Event Start Date:

6 November 2006

Event End Date:

8 November 2006

Publisher

Name*:

Springer

URL:

http://www.springer.com/lncs

Address*:

Berlin, Germany

Type:


Vol, No, Year, pp.

Series:

Lecture Notes in Computer Science

Volume:

4291

Number:


Month:

November

Pages:

485-494

Year*:

2006

VG Wort Pages:

10

ISBN/ISSN:

3-540-48628-3; 978-3-540-48628-2

Sequence Number:


DOI:




Note, Abstract, ©


(LaTeX) Abstract:

We present a novel fully-automatic approach for estimating an articulated skeleton of a moving subject and its motion from body marker trajectories that have been measured with an optical motion capture system. Our method does not require a priori information about the shape and proportions of the tracked subject, can be applied to arbitrary motion sequences, and renders dedicated initialization poses unnecessary. To serve this purpose, our algorithm first identifies individual rigid bodies by means of a variant of spectral clustering. Thereafter, it determines joint positions at each time step of motion through numerical optimization, reconstructs the skeleton topology, and finally enforces fixed bone length constraints. Through experiments, we demonstrate the robustness and effciency of our algorithm and show that it outperforms related methods from the literature in terms of accuracy and speed.

URL for the Abstract:

http://www.springerlink.com/content/26760v071m0962m5/?p=85a3a500b4ec4b33a5fe8ab8085d3c28&pi=48

Keywords:

optical motion capture, skeleton reconstruction



Download
Access Level:

Intranet

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Computer Graphics Group

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{deAguiar_isvc2006,
AUTHOR = {de Aguiar, Edilson and Theobalt, Christian and Seidel, Hans-Peter},
EDITOR = {Bebis, George and Boyle, Richard and Parvin, Bahram and Koracin, Darko and Remagnino, Paolo and Nefian, Ara and Meenakshisundaram, Gopi and Pascucci, Valerio and Zara, Jiri and Molineros, Jose and Theisel, Holger and Malzbender, Tom},
TITLE = {Automatic Learning of Articulated Skeletons from {3D} Marker Trajectories},
BOOKTITLE = {Advances in Visual Computing : Second International Symposium, ISVC 2006, Part I},
PUBLISHER = {Springer},
YEAR = {2006},
VOLUME = {4291},
PAGES = {485--494},
SERIES = {Lecture Notes in Computer Science},
ADDRESS = {Lake Tahoe, NV, USA},
MONTH = {November},
ISBN = {3-540-48628-3},
; ISBN = {978-3-540-48628-2},
ISBN = {0302-9743},
}


Entry last modified by Anja Becker, 05/04/2011
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Editor(s)
Edilson de Aguiar
Created
01/03/2007 04:33:10 PM
Revisions
4.
3.
2.
1.
0.
Editor(s)
Anja Becker
Anja Becker
Christine Kiesel
Edilson de Aguiar
Edilson de Aguiar
Edit Dates
04.05.2011 10:41:18
22.02.2008 13:13:34
25.02.2007 20:06:52
01/04/2007 02:53:40 PM
01/03/2007 04:33:10 PM