2024/05/19 by Haowen Luo, Luo, Haowen, Yunze Liu +3 · 3 citations
Engineering · Computer Science · #Teleoperation and Haptic Systems #Hand Gesture Recognition Systems #Interactive and Immersive Displays
paper · pdf · doi:10.48550/arxiv.2405.11481
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.