  We propose a method to generate multiple diverse and valid human pose
hypotheses in 3D all consistent with the 2D detection of joints in a monocular
RGB image. We use a novel generative model uniform (unbiased) in the space of
anatomically plausible 3D poses. Our model is compositional (produces a pose by
combining parts) and since it is restricted only by anatomical constraints it
can generalize to every plausible human 3D pose. Removing the model bias
intrinsically helps to generate more diverse 3D pose hypotheses. We argue that
generating multiple pose hypotheses is more reasonable than generating only a
single 3D pose based on the 2D joint detection given the depth ambiguity and
the uncertainty due to occlusion and imperfect 2D joint detection. We hope that
the idea of generating multiple consistent pose hypotheses can give rise to a
new line of future work that has not received much attention in the literature.
We used the Human3.6M dataset for empirical evaluation.
