  Occlusion is an omnipresent challenge in 3D human pose estimation (HPE). In
spite of the large amount of research dedicated to 3D HPE, only a limited
number of studies address the problem of occlusion explicitly. To fill this
gap, we propose to combine exploitation of spatio-temporal features with
synthetic occlusion augmentation during training to deal with occlusion. To
this end, we build a spatio-temporal 3D HPE model, StridedPoseGraphFormer based
on graph convolution and transformers, and train it using occlusion
augmentation. Unlike the existing occlusion-aware methods, that are only tested
for limited occlusion, we extensively evaluate our method for varying degrees
of occlusion. We show that our proposed method compares favorably with the
state-of-the-art (SoA). Our experimental results also reveal that in the
absence of any occlusion handling mechanism, the performance of SoA 3D HPE
methods degrades significantly when they encounter occlusion.
