Mapping optical motion capture data to skeletal motion using a physical model
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Skeleton-Based Motion Capture for Robust Reconstruction of Human Motion
CA '00 Proceedings of the Computer Animation
Example-based control of human motion
SCA '04 Proceedings of the 2004 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
Performance animation from low-dimensional control signals
ACM SIGGRAPH 2005 Papers
Capturing and animating skin deformation in human motion
ACM SIGGRAPH 2006 Papers
Estimation of missing markers in human motion capture
The Visual Computer: International Journal of Computer Graphics
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Bilinear spatiotemporal basis models
ACM Transactions on Graphics (TOG)
Reconstructing motion capture data for human crowd study
MIG'11 Proceedings of the 4th international conference on Motion in Games
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Given a motion capture sequence with occlusions, how can we recover the missing values, respecting bone-length constraints? Recent past work uses Linear Dynamical Systems (LDS), which work well, except for occasionally violating such constraints, and thus lead to unrealistic results. Our main contribution is a principled approach for preserving such distances. Specifically (a) we show how to formulate the problem as a constrained optimization problem, using two variations: hard constraints, and soft constraints; (b) we show how to efficiently solve both variations; (c) we demonstrate the realism of our approaches against competitors, on real motion capture data, illustrating that our 'soft constraints' version eventually produces more realistic results.