Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Processing motion capture data to achieve positional accuracy
Graphical Models and Image Processing
Mapping optical motion capture data to skeletal motion using a physical model
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
A sketching interface for articulated figure animation
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Skeletal Parameter Estimation from Optical Motion Capture Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Least-squares fitting of multiple M-dimensional point sets
The Visual Computer: International Journal of Computer Graphics
A real-time sequential algorithm for human joint localization
SIGGRAPH '05 ACM SIGGRAPH 2005 Posters
Articulated pose identification with sparse point features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A group of novel approaches and a toolkit for motion capture data reusing
Multimedia Tools and Applications
Constraint-based Correspondence Matching for Stereo-based Interactive Robotic Massage Machine
Journal of Intelligent and Robotic Systems
Reconstructing 3D tree models using motion capture and particle flow
International Journal of Computer Games Technology
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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.