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In this report, we present and evaluate a method of reconstructing three-dimensional (3D) periodic human motion from two-dimensional (2D) motion sequences. Based on a Fourier decomposition of a training set of 3D data, we construct a linear, morphable representation. Using this representation a lowdimensional linear model is learned by means of Principle Component Analysis (PCA). Twodimensional test data are now projected onto this model and the resulting 3D reconstructions are evaluated. Wedoi:10.1109/cvpr.2004.276 dblp:conf/cvpr/ZhangT04 fatcat:zb6fs7skrfemjjq7ukmfrstpo4