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Part-based models with restrictive tree-structured interactions for the HumanPose Estimation problem, leaves many part interactions unhandled. Two of themost common and strong manifestations of such unhandled interactions areself-occlusion among the parts and the confusion in the localization of thenon-adjacent symmetric parts. By handling the self-occlusion in a dataefficient manner, we improve the performance of the basic Mixture of Partsmodel by a large margin, especially on uncommon poses. Through addressing theconfusion in the symmetric limb localization using a combination of twocomplementing trees, we improve the performance on all the parts by atmostdoubling the running time. Finally, we show that the combination of the twosolutions improves the results. We report results that are equivalent to thestate-of-the-art on two standard datasets. Because of maintaining thetree-structured interactions and only part-level modeling of the base Mixtureof Parts model, this is achieved in time that is much less than the bestperforming part-based model.

Mixture of Parts Revisited: Expressive Part Interactions for Pose Estimation