Physics-aware Hand-object Interaction Denoising
<|reference_start|>Physics-aware Hand-object Interaction Denoising: 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.<|reference_end|>
@article{luo2024physics-aware,
    title={Physics-aware Hand-object Interaction Denoising},
    author={Haowen Luo, Yunze Liu, Li Yi},
    journal={arXiv preprint arXiv:2405.11481},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11481},
    primaryClass={cs.CV}
}