  Lifting the 2D human pose to the 3D pose is an important yet challenging
task. Existing 3D pose estimation suffers from 1) the inherent ambiguity
between the 2D and 3D data, and 2) the lack of well labeled 2D-3D pose pairs in
the wild. Human beings are able to imagine the human 3D pose from a 2D image or
a set of 2D body key-points with the least ambiguity, which should be
attributed to the prior knowledge of the human body that we have acquired in
our mind. Inspired by this, we propose a new framework that leverages the
labeled 3D human poses to learn a 3D concept of the human body to reduce the
ambiguity. To have consensus on the body concept from 2D pose, our key insight
is to treat the 2D human pose and the 3D human pose as two different domains.
By adapting the two domains, the body knowledge learned from 3D poses is
applied to 2D poses and guides the 2D pose encoder to generate informative 3D
"imagination" as embedding in pose lifting. Benefiting from the domain
adaptation perspective, the proposed framework unifies the supervised and
semi-supervised 3D pose estimation in a principled framework. Extensive
experiments demonstrate that the proposed approach can achieve state-of-the-art
performance on standard benchmarks. More importantly, it is validated that the
explicitly learned 3D body concept effectively alleviates the 2D-3D ambiguity
in 2D pose lifting, improves the generalization, and enables the network to
exploit the abundant unlabeled 2D data.
