  The treatment of degenerative spinal disorders requires an understanding of
the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e.
standing) under natural weight bearing is crucial for such bio-mechanical
analysis. 3D volumetric imaging modalities (e.g. CT and MRI) are performed in
patients lying down. On the other hand, radiographs are captured in an upright
pose, but result in 2D projections. This work aims to integrate the two realms,
i.e. it combines the upright spinal curvature from radiographs with the 3D
vertebral shape from CT imaging for synthesizing an upright 3D model of spine,
loaded naturally. Specifically, we propose a novel neural network architecture
working vertebra-wise, termed \emph{TransVert}, which takes orthogonal 2D
radiographs and infers the spine's 3D posture. We validate our architecture on
digitally reconstructed radiographs, achieving a 3D reconstruction Dice of
$95.52\%$, indicating an almost perfect 2D-to-3D domain translation. Deploying
our model on clinical radiographs, we successfully synthesise full-3D, upright,
patient-specific spine models for the first time.
