Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes

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.