|
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.
|
