Part-based models with restrictive tree-structured interactions for the Human Pose Estimation problem, leave many part interactions unhandled. Two of the most common and strong manifestations of such unhandled interactions are self-occlusion among the parts and the confusion in the localization of the non-adjacent symmetric parts. By handling the self-occlusion in a data efficient manner, we improve the performance of the basic Mixture of Parts model by a large margin, especially on difficult poses. We address the confusion in the symmetric limb localization using a combination of two complementing trees, showing an improvement in the performance on all the parts with a very small trade-off in the running time. Finally, we show that the combination of the two solutions improves the results. We compare our HOG-based method with other methods using similar features and report results equivalent to the best method on two standard datasets with a large reduction in the running time. © 2015 IEEE.