A survey of deep learning methods and datasets for hand pose estimation from hand-object interaction images

The research topic of estimating hand pose from the images of hand-object interaction has the potential for replicating natural hand behavior in many practical applications of virtual reality and robotics. However, the intricacy of hand-object interaction combined with mutual occlusion, and the need for physical plausibility, brings many challenges to the problem. This paper provides a comprehensive survey of the state-of-the-art deep learning-based approaches for estimating hand pose (joint and shape) in the context of hand-object interaction. We discuss various deep learning-based approaches to image-based hand tracking, including hand joint and shape estimation. In addition, we review the hand-object interaction dataset benchmarks that are well-utilized in hand joint and shape estimation methods. Deep learning has emerged as a powerful technique for solving many problems including hand pose estimation. While we cover extensive research in the field, we discuss the remaining challenges leading to future research directions.(c) 2023 Elsevier Ltd. All rights reserved.

Automated summary

This paper provides a comprehensive survey of state-of-the-art deep learning-based approaches for estimating hand pose in the context of hand-object interaction. It discusses various deep learning-based approaches to image-based hand tracking and reviews hand-object interaction dataset benchmarks. Deep learning has emerged as a powerful technique for solving hand pose estimation problems.