  In this paper, we address the problem of estimating a 3D human pose from a
single image, which is important but difficult to solve due to many reasons,
such as self-occlusions, wild appearance changes, and inherent ambiguities of
3D estimation from a 2D cue. These difficulties make the problem ill-posed,
which have become requiring increasingly complex estimators to enhance the
performance. On the other hand, most existing methods try to handle this
problem based on a single complex estimator, which might not be good solutions.
In this paper, to resolve this issue, we propose a
multiple-partial-hypothesis-based framework for the problem of estimating 3D
human pose from a single image, which can be fine-tuned in an end-to-end
fashion. We first select several joint groups from a human joint model using
the proposed sampling scheme, and estimate the 3D poses of each joint group
separately based on deep neural networks. After that, they are aggregated to
obtain the final 3D poses using the proposed robust optimization formula. The
overall procedure can be fine-tuned in an end-to-end fashion, resulting in
better performance. In the experiments, the proposed framework shows the
state-of-the-art performances on popular benchmark data sets, namely Human3.6M
and HumanEva, which demonstrate the effectiveness of the proposed framework.
