Human pose estimation in 3D from monocular images is a challenging inverse problem due to ambiguity in lifting 2D projection to 3D space. In this article we have made three contributions in order to solve 3D pose estimation. First, a new DNN architecture is proposed to generate multiple feasible 3D pose hypotheses from a given image. Second, we generate weights for the proposed hypotheses using ordinal supervision. These weights are used to predict the final 3D pose from the generated hypotheses. Finally, we report a new regularizer to enforce that the predicted skeleton is consistent with the restriction of anthropomorphic constraints. We compare the results of our algorithm with other state-of-the art approaches on the Human 3. 6m benchmark dataset. Our algorithm reports competitive results.