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
DynaMMo: mining and summarization of coevolving sequences with missing values
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mimesis Model from Partial Observations for a Humanoid Robot
International Journal of Robotics Research
Coping with full occlusion in fronto-normal gait by using missing data theory
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Bilinear spatiotemporal basis models
ACM Transactions on Graphics (TOG)
Real-time classification of dynamic hand gestures from marker-based position data
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Forward non-rigid motion tracking for facial MoCap
The Visual Computer: International Journal of Computer Graphics
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Motion capture is a prevalent technique for capturing and analyzing human articulations. A common problem encountered in motion capture is that some marker positions are often missing due to occlusions or ambiguities. Most methods for completing missing markers may quickly become ineffective and produce unsatisfactory results when a significant portion of the markers are missing for extended periods of time. We propose a data-driven, piecewise linear modeling approach to missing marker estimation that is especially beneficial in this scenario. We model motion sequences of a training set with a hierarchy of low-dimensional local linear models characterized by the principal components. For a new sequence with missing markers, we use a pre-trained classifier to identify the most appropriate local linear model for each frame and then recover the missing markers by finding the least squares solutions based on the available marker positions and the principal components of the associated model. Our experimental results demonstrate that our method is efficient in recovering the full-body motion and is robust to heterogeneous motion data.