This paper presents a realtime performance animation system that reproduces full-body character animation based on sparse 3D motion sensors on the performer. Producing faithful character animation from this setting is a mathematically ill-posed problem because input data from the sensors is not sufficient to determine the full degrees of freedom of a character. Given the input data from 3D motion sensors, we pick similar poses from the motion database and build an online local model that transforms the low-dimensional input signal into a high-dimensional character pose. Kernel CCA (Canonical Correlation Analysis)-based regression is employed as the model, which effectively covers a wide range of motion. Examples show that various human motions are naturally reproduced by our method. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Kim, J., Seol, Y., & Lee, J. (2012). Realtime performance animation using sparse 3D motion sensors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7660 LNCS, pp. 31–42). Springer Verlag. https://doi.org/10.1007/978-3-642-34710-8_4
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