3D human pose estimation using monocular images is an important yet
challenging task. Existing 3D pose detection methods exhibit excellent
performance under normal conditions however their performance may degrade due
to occlusion. Recently some occlusion aware methods have also been proposed,
however, the occlusion handling capability of these networks has not yet been
thoroughly investigated. In the current work, we propose an occlusion-guided 3D
human pose estimation framework and quantify its occlusion handling capability
by using different protocols. The proposed method estimates more accurate 3D
human poses using 2D skeletons with missing joints as input. Missing joints are
handled by introducing occlusion guidance that provides extra information about
the absence or presence of a joint. Temporal information has also been
exploited to better estimate the missing joints. A large number of experiments
are performed for the quantification of occlusion handling capability of the
proposed method on three publicly available datasets in various settings
including random missing joints, fixed body parts missing, and complete frames
missing, using mean per joint position error criterion. In addition to that,
the quality of the predicted 3D poses is also evaluated using action
classification performance as a criterion. 3D poses estimated by the proposed
method achieved significantly improved action recognition performance in the
presence of missing joints. Our experiments demonstrate the effectiveness of
the proposed framework for handling the missing joints as well as
quantification of the occlusion handling capability of the deep neural
networks.

本文提出了一种基于遮挡指导的 3D 人体姿态估计框架，利用不同协议对其遮挡处理能力进行了量化，并利用动作分类表现作为评估标准，证明了该方法能更准确地预测缺失节点的 3D 姿态，并在存在缺失节点时显著提高了动作识别性能。