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Graph."],"published-print":{"date-parts":[[2013,7,21]]},"abstract":"<jats:p>\n            The central argument against data-driven methods in computer graphics rests on the curse of dimensionality: it is intractable to precompute \"everything\" about a complex space. In this paper, we challenge that assumption by using several thousand CPU-hours to perform a massive exploration of the space of secondary clothing effects on a character animated through a large motion graph. Our system continually explores the phase space of cloth dynamics, incrementally constructing a\n            <jats:italic>secondary cloth motion graph<\/jats:italic>\n            that captures the dynamics of the system. We find that it is possible to sample the dynamical space to a low visual error tolerance and that secondary motion graphs containing tens of gigabytes of raw mesh data can be compressed down to only tens of megabytes. 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