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Coherent Reconstruction of Multiple Humans from a Single Image by Jiang, Wen; Kolotouros, Nikos; Pavlakos, Georgios; Zhou, Xiaowei; Daniilidis, Kostas is a scholarly article available to read on EtoBox.

What is Coherent Reconstruction of Multiple Humans from a Single Image about?

In this work, we address the problem of multi-person 3D pose estimation from a single image. A typical regression approach in the top-down setting of this problem would first detect all humans and then reconstruct each one of them independently. However, this type of prediction suffers from incoherent results, e.g., interpenetration and inconsistent depth ordering between the people in the scene. Our goal is to train a single network that learns to avoid these problems and generate a coherent 3D reconstruction of all the humans in the scene. To this end, a key design choice is the incorporation of the SMPL parametric body model in our top-down framework, which enables the use of two novel losses. First, a distance field-based collision loss penalizes interpenetration among the reconstructed people. Second, a depth ordering-aware loss reasons about occlusions and promotes a depth ordering of people that leads to a rendering which is consistent with the annotated instance segmentation. This provides depth supervision signals to the network, even if the image has no explicit 3D annotations. The experiments show that our approach outperforms previous methods on standard 3D pose benchma

Author

Jiang, Wen; Kolotouros, Nikos; Pavlakos, Georgios; Zhou, Xiaowei; Daniilidis, Kostas

Published

2020

Language

EN