We present an approach to efficiently detect the 2D pose of multiple people
in an image. The approach uses a nonparametric representation, which we refer
to as Part Affinity Fields (PAFs), to learn to associate body parts with
individuals in the image. The architecture encodes global context, allowing a
greedy bottom-up parsing step that maintains high accuracy while achieving
realtime performance, irrespective of the number of people in the image. The
architecture is designed to jointly learn part locations and their association
via two branches of the same sequential prediction process. Our method placed
first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds
the previous state-of-the-art result on the MPII Multi-Person benchmark, both
in performance and efficiency.

本研究提出了一种有效快速的图像中多人二维姿态检测方法，使用无参数表示方法命名为 Part Affinity Fields (PAFs)，可以学习将身体部位与图像中的个人关联，并设计了一个适用于任意人数图像的贪婪自下而上的解析步骤，该算法通过同一顺序预测过程的两个分支来联合学习部位位置及其关联，显著高于 MPII Multi-Person 基准的现有最新技术结果。

使用部分关联场的多人实时二维姿势估计

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

Human pose estimation is a key step to action recognition. We propose a
method of estimating 3D human poses from a single image, which works in
conjunction with an existing 2D pose/joint detector. 3D pose estimation is
challenging because multiple 3D poses may correspond to the same 2D pose after
projection due to the lack of depth information. Moreover, current 2D pose
estimators are usually inaccurate which may cause errors in the 3D estimation.
We address the challenges in three ways: (i) We represent a 3D pose as a linear
combination of a sparse set of bases learned from 3D human skeletons. (ii) We
enforce limb length constraints to eliminate anthropomorphically implausible
skeletons. (iii) We estimate a 3D pose by minimizing the $L_1$-norm error
between the projection of the 3D pose and the corresponding 2D detection. The
$L_1$-norm loss term is robust to inaccurate 2D joint estimations. We use the
alternating direction method (ADM) to solve the optimization problem
efficiently. Our approach outperforms the state-of-the-arts on three benchmark
datasets.

提出了一种在现有 2D 姿态检测器的基础上，从单张图像中估计 3D 人体姿态的方法，通过线性组合稀疏基，实施限制并优化，得出该方法在三个基准数据集上的表现优于现有技术。