ACRNet: Attention Cube Regression Network for Multi-view Real-time 3D Human Pose Estimation in Telemedicine

TL;DR

ACRNet is a real-time multi-view 3D human pose estimation network using attention cubes, demonstrating high accuracy and speed suitable for telemedicine and rehabilitation scenarios.

Contribution

This paper introduces ACRNet, a novel attention cube regression network that improves real-time 3D human pose estimation accuracy and efficiency in telemedicine applications.

Findings

Outperforms state-of-the-art methods on ITOP dataset

Validates effectiveness on a new rehabilitation dataset

Achieves high accuracy and real-time performance

Abstract

Human pose estimation (HPE) for 3D skeleton reconstruction in telemedicine has long received attention. Although the development of deep learning has made HPE methods in telemedicine simpler and easier to use, addressing low accuracy and high latency remains a big challenge. In this paper, we propose a novel multi-view Attention Cube Regression Network (ACRNet), which regresses the 3D position of joints in real time by aggregating informative attention points on each cube surface. More specially, a cube whose each surface contains uniformly distributed attention points with specific coordinate values is first created to wrap the target from the main view. Then, our network regresses the 3D position of each joint by summing and averaging the coordinates of attention points on each surface after being weighted. To verify our method, we first tested ACRNet on the open-source ITOP dataset; meanwhile, we collected a new multi-view upper body movement dataset (UBM) on the trunk support trainer (TruST) to validate the capability of our model in real rehabilitation scenarios. Experimental results demonstrate the superiority of ACRNet compared with other state-of-the-art methods. We also validate the efficacy of each module in ACRNet. Furthermore, Our work analyzes the performance of ACRNet under the medical monitoring indicator. Because of the high accuracy and running speed, our model is suitable for real-time telemedicine settings. The source code is available at https://github.com/BoceHu/ACRNet