Physics-aware Hand-object Interaction Denoising

Haowen Luo; Yunze Liu; Li Yi

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arXiv · 2405.11481

Physics-aware Hand-object Interaction Denoising

Haowen Luo·Yunze Liu·Li Yi

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Abstract

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.

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Haowen Luo, Yunze Liu, Li Yi. 2024-05-19. Physics-aware Hand-object Interaction Denoising. https://arxiv.org/abs/2405.11481

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