  Pursuing natural and marker-less human-robot interaction (HRI) has been a
long-standing robotics research focus, driven by the vision of seamless
collaboration without physical markers. Marker-less approaches promise an
improved user experience, but state-of-the-art struggles with the challenges
posed by intrinsic errors in human pose estimation (HPE) and depth cameras.
These errors can lead to issues such as robot jittering, which can
significantly impact the trust users have in collaborative systems. We propose
a filtering pipeline that refines incomplete 3D human poses from an HPE
backbone and a single RGB-D camera to address these challenges, solving for
occlusions that can degrade the interaction. Experimental results show that
using the proposed filter leads to more consistent and noise-free motion
representation, reducing unexpected robot movements and enabling smoother
interaction.
