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Motion Compression using Principal Geodesics Analysis
Author(s) -
Tournier M.,
Wu X.,
Courty N.,
Arnaud E.,
Revéret L.
Publication year - 2009
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2009.01375.x
Subject(s) - computer science , computer vision , motion capture , geodesic , data compression , algorithm , artificial intelligence , lossy compression , inverse kinematics , computer animation , animation , motion (physics) , mathematics , computer graphics (images) , geometry , robot
Due to the growing need for large quantities of human animation data in the entertainment industry, it has become a necessity to compress motion capture sequences in order to ease their storage and transmission. We present a novel, lossy compression method for human motion data that exploits both temporal and spatial coherence. Given one motion, we first approximate the poses manifold using Principal Geodesics Analysis (PGA) in the configuration space of the skeleton. We then search this approximate manifold for poses matching end‐effectors constraints using an iterative minimization algorithm that allows for real‐time, data‐driven inverse kinematics. The compression is achieved by only storing the approximate manifold parametrization along with the end‐effectors and root joint trajectories, also compressed, in the output data. We recover poses using the IK algorithm given the end‐effectors trajectories. Our experimental results show that considerable compression rates can be obtained using our method, with few reconstruction and perceptual errors.