  Egocentric 3D human pose estimation (HPE) from images is challenging due to
severe self-occlusions and strong distortion introduced by the fish-eye view
from the head mounted camera. Although existing works use intermediate
heatmap-based representations to counter distortion with some success,
addressing self-occlusion remains an open problem. In this work, we leverage
information from past frames to guide our self-attention-based 3D HPE
estimation procedure -- Ego-STAN. Specifically, we build a spatio-temporal
Transformer model that attends to semantically rich convolutional neural
network-based feature maps. We also propose feature map tokens: a new set of
learnable parameters to attend to these feature maps. Finally, we demonstrate
Ego-STAN's superior performance on the xR-EgoPose dataset where it achieves a
30.6% improvement on the overall mean per-joint position error, while leading
to a 22% drop in parameters compared to the state-of-the-art.
