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.

通过引入两个学习的损失项，明确捕捉了物理合理性的两个关键方面：抓取可信度和操作可行性，从而训练了一种具有物理感知的去噪网络，实验证明该方法显著改善了细粒度的物理合理性和总体姿势准确性，超越了当前最先进的去噪方法。