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 benchmarks, while our
proposed losses enable more coherent reconstruction in natural images. The
project website with videos, results, and code can be found at:
this https URL

本研究针对单张图像中的多人 3D 姿态估计问题，采用自顶向下的方法，并利用 SMPL 参数化身体模型，提出了基于距离场碰撞损失和深度排序感知损失的网络训练框架，有效解决了结果不连贯的问题，并且在标准的 3D 姿态基准测试中表现出优异性能。