  The 3D pose estimation from a single image is a challenging problem due to
depth ambiguity. One type of the previous methods lifts 2D joints, obtained by
resorting to external 2D pose detectors, to the 3D space. However, this type of
approaches discards the contextual information of images which are strong cues
for 3D pose estimation. Meanwhile, some other methods predict the joints
directly from monocular images but adopt a 2.5D output representation $P^{2.5D}
= (u,v,z^{r}) $ where both $u$ and $v$ are in the image space but $z^{r}$ in
root-relative 3D space. Thus, the ground-truth information (e.g., the depth of
root joint from the camera) is normally utilized to transform the 2.5D output
to the 3D space, which limits the applicability in practice. In this work, we
propose a novel end-to-end framework that not only exploits the contextual
information but also produces the output directly in the 3D space via cascaded
dimension-lifting. Specifically, we decompose the task of lifting pose from 2D
image space to 3D spatial space into several sequential sub-tasks, 1) kinematic
skeletons \& individual joints estimation in 2D space, 2) root-relative depth
estimation, and 3) lifting to the 3D space, each of which employs direct
supervisions and contextual image features to guide the learning process.
Extensive experiments show that the proposed framework achieves
state-of-the-art performance on two widely used 3D human pose datasets
(Human3.6M, MuPoTS-3D).
