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

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