Skeletal Parameter Estimation from Optical Motion Capture Data

Authors:
Adam G. Kirk;James F. O'Brien;David A. Forsyth
Affiliations:
University of California at Berkeley;University of California at Berkeley;University of California at Berkeley
Venue:
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Year:
2005

Citing 0
Cited 27

Computational studies of human motion: part 1, tracking and motion synthesis

Foundations and Trends® in Computer Graphics and Vision
Validating retargeted and interpolated locomotions by dynamics-based analysis

Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia
From motion capture data to character animation

Proceedings of the ACM symposium on Virtual reality software and technology
Video-guided motion synthesis using example motions

ACM Transactions on Graphics (TOG)
A real-time sequential algorithm for human joint localization

SIGGRAPH '05 ACM SIGGRAPH 2005 Posters
Animation collage

SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Practical motion capture in everyday surroundings

ACM SIGGRAPH 2007 papers
Example-based skeleton extraction

SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
Vision-based hand pose estimation: A review

Computer Vision and Image Understanding
Estimating Human Skeleton Parameters and Configuration in Real-Time from Markered Optical Motion Capture

AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Predicting Missing Markers to Drive Real-Time Centre of Rotation Estimation

AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Automatic Estimation of Skeletal Motion from Optical Motion Capture Data

Motion in Games
Discovery of time series in video data through distribution of spatiotemporal gradients

Proceedings of the 2009 ACM symposium on Applied Computing
Learning Articulated Structure and Motion

International Journal of Computer Vision
Towards a low cost multi-camera marker based human motion capture system

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A case for web-based interactive 3D game using motion capture data

Edutainment'10 Proceedings of the Entertainment for education, and 5th international conference on E-learning and games
BoLeRO: a principled technique for including bone length constraints in motion capture occlusion filling

Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Motion capture for a natural tree in the wind

MIG'10 Proceedings of the Third international conference on Motion in games
Marker-based human motion capture in multiview sequences

EURASIP Journal on Advances in Signal Processing
Full body interaction for serious games in motor rehabilitation

Proceedings of the 2nd Augmented Human International Conference
Real-time classification of dance gestures from skeleton animation

SCA '11 Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Automatic learning of articulated skeletons from 3d marker trajectories

ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Unsupervised skeleton extraction and motion capture from 3D deformable matching

Neurocomputing
Content-aware exaggerated editing for life-like captured animations

Proceedings of the 9th European Conference on Visual Media Production
Estimating human motion from multiple Kinect sensors

Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
Special Section on Computer Graphics in Brazil: A selection of papers from SIBGRAPI 2012: Representing mesh-based character animations

Computers and Graphics
Reconstructing 3D tree models using motion capture and particle flow

International Journal of Computer Games Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present an algorithm for automatically estimating a subjectýs skeletal structure from optical motion capture data. Our algorithm consists of a series of steps that cluster markers into segment groups, determine the topological connectivity between these groups, and locate the positions of their connecting joints. Our problem formulation makes use of fundamental distance constraints that must hold for markers attached to an articulated structure, and we solve the resulting systems using a combination of spectral clustering and nonlinear optimization. We have tested our algorithms using data from both passive and active optical motion capture devices. Our results show that the system works reliably even with as few as one or two markers on each segment. For data recorded from human subjects, the system determines the correct topology and qualitatively accurate structure. Tests with a mechanical calibration linkage demonstrate errors for inferred segment lengths on average of only two percent. We discuss applications of our methods for commercial human figure animation, and for identifying human or animal subjects based on their motion independent of marker placement or feature selection.