  The credibility and practicality of a reconstructed hand-object interaction
sequence depend largely on its physical plausibility. However, due to high
occlusions during hand-object interaction, physical plausibility remains a
challenging criterion for purely vision-based tracking methods. To address this
issue and enhance the results of existing hand trackers, this paper proposes a
novel physically-aware hand motion de-noising method. Specifically, we
introduce two learned loss terms that explicitly capture two crucial aspects of
physical plausibility: grasp credibility and manipulation feasibility. These
terms are used to train a physically-aware de-noising network. Qualitative and
quantitative experiments demonstrate that our approach significantly improves
both fine-grained physical plausibility and overall pose accuracy, surpassing
current state-of-the-art de-noising methods.
