This paper considers the task of articulated human pose estimation of
multiple people in real world images. We propose an approach that jointly
solves the tasks of detection and pose estimation: it infers the number of
persons in a scene, identifies occluded body parts, and disambiguates body
parts between people in close proximity of each other. This joint formulation
is in contrast to previous strategies, that address the problem by first
detecting people and subsequently estimating their body pose. We propose a
partitioning and labeling formulation of a set of body-part hypotheses
generated with CNN-based part detectors. Our formulation, an instance of an
integer linear program, implicitly performs non-maximum suppression on the set
of part candidates and groups them to form configurations of body parts
respecting geometric and appearance constraints. Experiments on four different
datasets demonstrate state-of-the-art results for both single person and multi
person pose estimation. Models and code available at
this http URL

本研究提出了一种联合解决检测和姿态估计任务的方法，并使用基于 CNN 部件检测器生成的一组身体部位假设的分区和标签化公式，隐含执行非最大化抑制，以形成符合几何和外观约束的身体部件配置，对于单人和多人姿态估计都取得了最新的结果。