We propose a new 2D pose refinement network that learns to predict the human bias in the estimated 2D pose. There are biases in 2D pose estimations that are due to differences between annotations of 2D joint locations based on annotators' perception and those defined by motion capture (MoCap) systems. These biases are crafted into publicly available 2D pose datasets and cannot be removed with existing error reduction approaches. Our proposed pose refinement network allows us to efficiently remove the human bias in the estimated 2D poses and achieve highly accurate multi-view 3D human pose estimation.
翻译：我们建议一个新的 2D 配置改进网络, 学会预测估计 2D 配置中的人的偏向。 2D 中存在偏差,因为基于通知人的看法对2D联合地点的说明与运动捕获系统(Mocap)定义的对2D 配置改进网络之间存在差异。这些偏差被设计成可公开提供的 2D 配置数据集,无法用现有的减少错误的方法去除。我们提议的改进网络使我们能够有效地消除估计 2D 配置中的人的偏差,并实现非常准确的多视角3D 人构成估计。
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