Integration of bottom-up/top-down approaches for 2D pose estimation using probabilistic Gaussian modelling

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Why is it important?

This work dealt with the challenging problem of 2D pose estimation on single frames, based on the integration of probabilistic bottom-up and top-down processes which iteratively refine each other. The main advantage of the presented framework is its activity-independency since it does not rely on learning any motion model.