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CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction

Lixin Yang	Xinyu Zhan	Kailin Li
Wenqiang Xu	Jiefeng Li	Cewu Lu

Shanghai Jiao Tong University
In ICCV 2021

[arXiv]

[Code]

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Abstract

Modeling the hand-object (HO) interaction not only requires estimation of the HO pose, but also pays attention to the contact due to their interaction. Significant progress has been made in estimating hand and object separately with deep learning methods, simultaneous HO pose estimation and contact modeling has not yet been fully explored. In this paper, we present an explicit contact representation namely Contact Potential Field (CPF), and a learning-fitting hybrid framework namely MIHO for Modeling the Interaction of Hand and Object. In CPF, we treat each contacting HO vertex pair as a spring-mass system. Hence the whole system forms a potential field with minimal elastic energy at the grasp position. Extensive experiments on the two commonly used benchmarks have demonstrated that our method can achieve state-of-the-art in several reconstruction metrics, and allow us to produce more physically plausible HO pose even when the ground-truth exhibits severe interpenetration or disjointedness.

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Code

The architecture of the hybrid model MIHO

 [GitHub]

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Paper and Supplementary Material

Lixin Yang, Xinyu Zhan, Kailin Li, Wenqiang Xu, Jiefeng Li, Cewu Lu CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction In ICCV, 2021.

[Bibtex]

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Acknowledgements This template was originally made by Phillip Isola and Richard Zhang for a colorful ECCV project; the code can be found here.