The task of multi-person human pose estimation in natural scenes is quite challenging. Existing methods include both top-down and bottom-up approaches. The main advantage of bottom-up methods is its excellent tradeoff between estimation accuracy and computational cost. We follow this path and aim to design smaller, faster, and more accurate neural networks for the regression of keypoints and limb association vectors. These two regression tasks are naturally dependent on each other. In this work, we propose a dual-path network specially designed for multi-person human pose estimation, and compare our performance with the openpose network in aspects of model size, forward speed, and estimation accuracy.

翻译：人类在自然场景中的多人构成估计任务相当艰巨。 现有方法包括自上而下和自下而上的方法。 自下而上方法的主要优势在于其极好地权衡了估算准确性和计算成本。 我们遵循这条道路,并旨在设计更小、更快和更准确的神经网络,用于关键点和肢体关联矢量的回归。 这两个回归任务自然相互依存。 在这项工作中,我们提出了一个专门为多人构成估计设计的双路径网络,并在模型大小、前期速度和估算准确性等方面将我们的业绩与开放位置网络进行比较。