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dc.contributor.authorRaquel Urtasunen_US
dc.contributor.authorPascal Glardonen_US
dc.contributor.authorRonan Boulicen_US
dc.contributor.authorDaniel Thalmannen_US
dc.contributor.authorPascal Fuaen_US
dc.date.accessioned2015-02-19T07:42:25Z
dc.date.available2015-02-19T07:42:25Z
dc.date.issued2004en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttp://dx.doi.org/10.1111/j.1467-8659.2004.00809.xen_US
dc.description.abstractRepresenting motions as linear sums of principal components has become a widely accepted animation technique. While powerful, the simplest version of this approach is not particularly well suited to modeling the specific style of an individual whose motion had not yet been recorded when building the database: it would take an expert to adjust the PCA weights to obtain a motion style that is indistinguishable from his. Consequently, when realism is required, the current practice is to perform a full motion capture session each time a new person must be considered. In this paper, we extend the PCA approach so that this requirement can be drastically reduced: for whole classes of cyclic and noncyclic motions such as walking, running or jumping, it is enough to observe the newcomer moving only once at a particular speed or jumping a particular distance using either an optical motion capture system or a simple pair of synchronized video cameras. This one observation is used to compute a set of principal component weights that best approximates the motion and to extrapolate in real-time realistic animations of the same person walking or running at different speeds, and jumping a different distance.en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleStyle-Based Motion Synthesisen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume23en_US
dc.description.number4en_US
dc.identifier.doi10.1111/j.1467-8659.2004.00809.xen_US
dc.identifier.pages799-812en_US


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