Given an image with multiple people, our goal is to directly regress the pose
and shape of all the people as well as their relative depth. Inferring the
depth of a person in an image, however, is fundamentally ambiguous without
knowing their height. This is particularly problematic when the scene contains
people of very different sizes, e.g. from infants to adults. To solve this, we
need several things. First, we develop a novel method to infer the poses and
depth of multiple people in a single image. While previous work that estimates
multiple people does so by reasoning in the image plane, our method, called
BEV, adds an additional imaginary Bird's-Eye-View representation to explicitly
reason about depth. BEV reasons simultaneously about body centers in the image
and in depth and, by combing these, estimates 3D body position. Unlike prior
work, BEV is a single-shot method that is end-to-end differentiable. Second,
height varies with age, making it impossible to resolve depth without also
estimating the age of people in the image. To do so, we exploit a 3D body model
space that lets BEV infer shapes from infants to adults. Third, to train BEV,
we need a new dataset. Specifically, we create a "Relative Human" (RH) dataset
that includes age labels and relative depth relationships between the people in
the images. Extensive experiments on RH and AGORA demonstrate the effectiveness
of the model and training scheme. BEV outperforms existing methods on depth
reasoning, child shape estimation, and robustness to occlusion. The code and
dataset are released for research purposes.

该论文提出了一种基于 Bird's-Eye-View 方法的姿态回归和深度估计模型，可以同时估计单个图片中多个人的 3D 位置，解决了多尺度和年龄差异等问题，模型拥有单次计算和端到端可微特性，并在实验中展示了优秀的性能。

将人置于其位置：单目深度回归中的三维人体

Putting People in their Place: Monocular Regression of 3D People in  Depth

In this paper we propose an approach for articulated tracking of multiple
people in unconstrained videos. Our starting point is a model that resembles
existing architectures for single-frame pose estimation but is substantially
faster. We achieve this in two ways: (1) by simplifying and sparsifying the
body-part relationship graph and leveraging recent methods for faster
inference, and (2) by offloading a substantial share of computation onto a
feed-forward convolutional architecture that is able to detect and associate
body joints of the same person even in clutter. We use this model to generate
proposals for body joint locations and formulate articulated tracking as
spatio-temporal grouping of such proposals. This allows to jointly solve the
association problem for all people in the scene by propagating evidence from
strong detections through time and enforcing constraints that each proposal can
be assigned to one person only. We report results on a public MPII Human Pose
benchmark and on a new MPII Video Pose dataset of image sequences with multiple
people. We demonstrate that our model achieves state-of-the-art results while
using only a fraction of time and is able to leverage temporal information to
improve state-of-the-art for crowded scenes.

本文提出了一种针对无限约束视频中多人的关节追踪方法，利用简化稀疏的身体关系图和最近的快速推断方法，以及将计算量转移到卷积结构上；并将关节定位方案用于构建关节追踪形式，解决了所有场景中的关联问题，并已在公共 MPII Human Pose 基准测试中取得最新结果。