  Recovering 3D human mesh from monocular images is a popular topic in computer
vision and has a wide range of applications. This paper aims to estimate 3D
mesh of multiple body parts (e.g., body, hands) with large-scale differences
from a single RGB image. Existing methods are mostly based on iterative
optimization, which is very time-consuming. We propose to train a single-shot
model to achieve this goal. The main challenge is lacking training data that
have complete 3D annotations of all body parts in 2D images. To solve this
problem, we design a multi-branch framework to disentangle the regression of
different body properties, enabling us to separate each component's training in
a synthetic training manner using unpaired data available. Besides, to
strengthen the generalization ability, most existing methods have used
in-the-wild 2D pose datasets to supervise the estimated 3D pose via 3D-to-2D
projection. However, we observe that the commonly used weak-perspective model
performs poorly in dealing with the external foreshortening effect of camera
projection. Therefore, we propose a depth-to-scale (D2S) projection to
incorporate the depth difference into the projection function to derive
per-joint scale variants for more proper supervision. The proposed method
outperforms previous methods on the CMU Panoptic Studio dataset according to
the evaluation results and achieves comparable results on the Human3.6M body
and STB hand benchmarks. More impressively, the performance in close shot
images gets significantly improved using the proposed D2S projection for weak
supervision, while maintains obvious superiority in computational efficiency.
