VMHPE: Human Pose Estimation for Virtual Maintenance Tasks

Virtual maintenance, as an important means of industrial training and education, places strict requirements on the accuracy of participant pose perception and assessment of motion standardization. However, existing research mainly focuses on human pose estimation in general scenarios, lacking specialized solutions for maintenance scenarios. This paper proposes a virtual maintenance human pose estimation method based on multi-scale feature enhancement (VMHPE), which integrates adaptive input feature enhancement, multi-scale feature correction for improved expression of fine movements and complex poses, and multi-scale feature fusion to enhance keypoint localization accuracy. Meanwhile, this study constructs the first virtual maintenance-specific human keypoint dataset (VMHKP), which records standard action sequences of professional maintenance personnel in five typical maintenance tasks and provides a reliable benchmark for evaluating operator motion standardization. The dataset is publicly available at https://doi.org/10.5281/zenodo.15525037. Using high-precision keypoint prediction results, an action assessment system utilizing topological structure similarity was established. Experiments show that our method achieves significant performance improvements: average precision (AP) reaches 94.4%, an increase of 2.3 percentage points over baseline methods; average recall (AR) reaches 95.6%, an increase of 1.3 percentage points. This research establishes a scientific four-level evaluation standard based on comparative motion analysis and provides a reliable solution for standardizing industrial maintenance training.