6. The method of claim 1 wherein the discriminative model is received from a learning stage and wherein the learning stage is prior to said receiving steps, the learning stage comprising steps of: receiving motion capture data from a motion capture source, the motion capture data comprising a first motion from a first motion class and a second motion from a second motion class that is different from the first motion class; processing the motion capture data to extract a first sequence of pose states representing the first motion and a second sequence of pose states representing the second motion; learning the discriminative model configured to project the first and second sequence of pose states to a low dimensional space and enforce discriminance between the first and second motion classes in the low dimensional space; applying a clustering algorithm to cluster the temporally contiguous state vectors into the learned motion segments in the low dimensional space; and learning a dynamic model for each motion segment to generate motion predictions in the low dimensional space.
6. The method of claim 1 wherein the discriminative model is received from a learning stage and wherein the learning stage is prior to said receiving steps, the learning stage comprising steps of: receiving motion capture data from a motion capture source, the motion capture data comprising a first motion from a first motion class and a second motion from a second motion class that is different from the first motion class; processing the motion capture data to extract a first sequence of pose states representing the first motion and a second sequence of pose states representing the second motion; learning the discriminative model configured to project the first and second sequence of pose states to a low dimensional space and enforce discriminance between the first and second motion classes in the low dimensional space; applying a clustering algorithm to cluster the temporally contiguous state vectors into the learned motion segments in the low dimensional space; and learning a dynamic model for each motion segment to generate motion predictions in the low dimensional space. 9. The method of claim 6 wherein learning a discriminative model comprises steps of: computing an intra-class variety representing the sum of the distances between data points that are in the same motion class; computing the inter-class separability representing the sum of the distances between data points that are in different motion classes; obtaining a projection matrix configured to reduce the intra-class variety and increase the inter-class separability; and projecting the motion capture data from the high dimensional space to the low dimensional space based on the projection matrix.