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Human motion reconstruction from sparse 3D motion sensors using kernel CCA‐based regression
Author(s) -
Kim Jongmin,
Seol Yeongho,
Lee Jehee
Publication year - 2013
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.1557
Subject(s) - computer science , animation , canonical correlation , character animation , motion capture , kernel (algebra) , character (mathematics) , artificial intelligence , motion (physics) , computer vision , computer animation , signal (programming language) , pattern recognition (psychology) , computer graphics (images) , mathematics , geometry , combinatorics , programming language
This paper presents a real‐time performance animation system that reproduces full‐body character animation based on sparse three‐dimensional (3D) motion sensors on a performer. Producing faithful character animation from this setting is a mathematically ill‐posed problem, because input data from the sensors are not sufficient to determine the full degrees of freedom of a character. Given the input data from 3D motion sensors, we select similar poses from a motion database and build an online local model that transforms the low‐dimensional input signal into a high‐dimensional character pose. A regression method based on kernel canonical correlation analysis (CCA) is employed, because it effectively handles a wide variety of motions. Examples show that various human motions are naturally reproduced by the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.