  Recovering accurate 3D human pose and shape from in-the-wild crowd scenes is
highly challenging and barely studied, despite their common presence. In this
regard, we present 3DCrowdNet, a 2D human pose-guided 3D crowd pose and shape
estimation system for in-the-wild scenes. 2D human pose estimation methods
provide relatively robust outputs on crowd scenes than 3D human pose estimation
methods, as they can exploit in-the-wild multi-person 2D datasets that include
crowd scenes. On the other hand, the 3D methods leverage 3D datasets, of which
images mostly contain a single actor without a crowd. The train data difference
impedes the 3D methods' ability to focus on a target person in in-the-wild
crowd scenes. Thus, we design our system to leverage the robust 2D pose outputs
from off-the-shelf 2D pose estimators, which guide a network to focus on a
target person and provide essential human articulation information. We show
that our 3DCrowdNet outperforms previous methods on in-the-wild crowd scenes.
We will release the codes.
