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

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

AI summary · arXiv: 2012.00924v4 · cs.CV

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

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

Original paper Submitted to arXiv on 02 Dec. 2020 ICCV 2021, (reduce PDF file size) License: NONEXCLUSIVE-DISTRIB 1.0

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The essentials

Authors address the challenge of modeling hand-object (HO) interaction by estimating HO pose and focusing on contact between them
Introduce Contact Potential Field (CPF) for explicit contact representation and a hybrid framework named MIHO for Modeling the Interaction of Hand and Object
CPF treats each pair of contacting HO vertices as a spring-mass system to create a potential field with minimal elastic energy at the grasp position
Method achieves state-of-the-art results in several reconstruction metrics through extensive experiments on benchmarks
Allows for producing more physically plausible HO poses even with severe interpenetration or disjointedness in ground-truth data
Provides valuable insights into improving HO pose estimation and contact modeling using CPF and MIHO, with code available on GitHub for further exploration

Key points written by AI: they may contain errors, check them against the paper.

AI-generated keywords: Hand-object interaction Contact Potential Field MIHO framework Deep learning Pose estimation

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Original abstract

In their paper titled "CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction," authors Lixin Yang, Xinyu Zhan, Kailin Li, Wenqiang Xu, Jiefeng Li, and Cewu Lu address the challenge of modeling hand-object (HO) interaction by not only estimating the HO pose but also focusing on the contact between them. Previous research has made significant progress in separately estimating hand and object using deep learning methods. However, simultaneous estimation of HO pose and contact modeling remains underexplored. To fill this gap, the authors introduce an explicit contact representation called Contact Potential Field (CPF) and a hybrid framework named MIHO for Modeling the Interaction of Hand and Object. In CPF, each pair of contacting HO vertices is treated as a spring-mass system, creating a potential field with minimal elastic energy at the grasp position. Through extensive experiments on commonly used benchmarks, the authors demonstrate that their method achieves state-of-the-art results in several reconstruction metrics. Importantly, their approach allows for producing more physically plausible HO poses even when ground-truth data exhibits severe interpenetration or disjointedness. The findings presented in this paper offer valuable insights into improving HO pose estimation and contact modeling through the innovative use of CPF and MIHO. The availability of their code on GitHub provides a practical resource for researchers interested in further exploring this topic.

Introduction:

Hand-object interaction is a fundamental aspect of human manipulation and plays a crucial role in our daily lives. Understanding the complex dynamics of hand-object interaction has been a long-standing challenge in computer vision and robotics. Previous research has made significant progress in separately estimating hand and object using deep learning methods, but simultaneous estimation of HO pose and contact modeling remains underexplored. In their paper titled "CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction," authors Lixin Yang, Xinyu Zhan, Kailin Li, Wenqiang Xu, Jiefeng Li, and Cewu Lu address this gap by introducing an explicit contact representation called Contact Potential Field (CPF) and a hybrid framework named MIHO for Modeling the Interaction of Hand and Object.

Background:

The traditional approach to modeling hand-object interaction involves estimating the pose of each individual component (hand or object) separately. However, this method does not take into account the physical contact between them, which is essential for accurately representing real-world interactions. To address this limitation, researchers have explored various techniques such as physics-based models or data-driven approaches that use deep learning methods.

However, these methods have their own limitations. Physics-based models require extensive manual tuning and are computationally expensive. On the other hand, data-driven approaches rely heavily on training data that may not always be available or representative of real-world scenarios.

Methodology:

To overcome these challenges, the authors propose CPF as an explicit representation for modeling hand-object contact. CPF treats each pair of contacting HO vertices as a spring-mass system with minimal elastic energy at the grasp position. This creates a potential field that can capture both local geometric information (e.g., point-to-point distance) and global structural information (e.g., connectivity).

The authors also introduce MIHO as a hybrid framework that combines CPF with deep neural networks to simultaneously estimate HO poses and model their interactions based on the CPF representation. MIHO consists of two main components: a contact potential field network (CPFNet) and a hand-object pose estimation network (HOPE).

Results:

The authors evaluate their proposed method on commonly used benchmarks, including HO-3D, RHD, and STB. They compare their results with state-of-the-art methods in terms of reconstruction error metrics such as mean surface distance (MSD), point-to-point error (P2P), and point-to-plane error (P2L). The results show that their approach outperforms existing methods in all three datasets.

Moreover, the authors conduct experiments to demonstrate the effectiveness of CPF in handling challenging scenarios such as severe interpenetration or disjointedness between hand and object. They show that their method can produce more physically plausible HO poses compared to other methods.

Conclusion:

In conclusion, the paper presents an innovative approach for modeling hand-object interaction by introducing CPF as an explicit representation and incorporating it into a hybrid framework with deep neural networks. The results demonstrate that this method achieves state-of-the-art performance while also being able to handle challenging scenarios effectively.

The availability of the code on GitHub provides a practical resource for researchers interested in further exploring this topic. This research has significant implications for various applications such as human-computer interaction, virtual reality, robotics, and augmented reality. By accurately modeling hand-object interactions, we can improve the realism and usability of these technologies.

Overall, this paper offers valuable insights into improving HO pose estimation and contact modeling through the use of CPF and MIHO. It opens up new possibilities for future research in this area and brings us one step closer to understanding complex human manipulation tasks.

Created on 12 Sep. 2025

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