  Existing 2D-to-3D pose lifting networks suffer from poor performance in
cross-dataset benchmarks. Although the use of 2D keypoints joined by
"stick-figure" limbs has shown promise as an intermediate step, stick-figures
do not account for occlusion information that is often inherent in an image. In
this paper, we propose a novel representation using opaque 3D limbs that
preserves occlusion information while implicitly encoding joint locations.
Crucially, when training on data with accurate three-dimensional keypoints and
without part-maps, this representation allows training on abstract synthetic
images, with occlusion, from as many synthetic viewpoints as desired. The
result is a pose defined by limb angles rather than joint positions
$\unicode{x2013}$ because poses are, in the real world, independent of cameras
$\unicode{x2013}$ allowing us to predict poses that are completely independent
of camera viewpoint. The result provides not only an improvement in
same-dataset benchmarks, but a "quantum leap" in cross-dataset benchmarks.
