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In 3-D articulated human motion tracking, the curse of dimensionality renders commonly-used particle-filter-based approaches inefficient. Also, noisy image measurements and imperfect feature extraction call for strong motion prior. We propose to learn the correlation between the right-side and the left-side human motion using partial least square (PLS) regression. The correlation effectively constrains the sampling of the proposal distribution to portions of the parameter space that correspond to plausible human motions. The learned correlation is then used as motion prior in designing a Rao-Blackwellized particle filter algorithm, RBPF-PLS, which estimates only one group of state variables using the Monte Carlo method, leaving the other group being exactly computed through an analytical filter that utilizes the learned motion correlation. We quantitatively assessed the accuracy of the proposed algorithm with challenging HumanEva-I/II data set. Experiments with comparison with both the annealed particle filter and the standard particle filter show that the proposed method achieves lower estimation error in processing challenging real-world data of 3-D human motion. In particular, the experiments demonstrate that the learned motion correlation model generalizes well to motions outside of the training set and is insensitive to the choice of the training subjects, suggesting the potential wide applicability of the method.