Estimating hand-object (HO) pose during interaction has been brought remarkable growth in virtue of deep learning methods. Modeling the contact between the hand and object properly is the key to construct a plausible grasp. Yet, previous works usually focus on jointly estimating HO pose but not fully explore the physical contact preserved in grasping. In this paper, we present an explicit contact representation, Contact Potential Field (CPF) that models each hand-object contact as a spring-mass system. Then we can refine a natural grasp by minimizing the elastic energy w.r.t those systems. To recover CPF, we also propose a learning-fitting hybrid framework named MIHO. Extensive experiments on two public benchmarks have shown 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. Our code is available at https://github.com/lixiny/CPF .

本文提出了一种显式的接触表示——接触势场（CPF）和一种混合框架——MIHO，用于模拟手和物体的相互作用。通过在CPF中将每个接触的HO顶点对视为弹簧质心系统，形成在抓握位置具有最小弹性能的潜能场。实验表明，我们的方法在多个重建指标上可以实现最先进的效果，并可以在地面真实性呈现严重穿透或分离时产生更加真实的HO姿势。

CPF: 学习接触势场以建模手-物体交互