Enhancing Human Pose Estimation Accuracy Using Synthetic Data

In industrial applications, Human Pose Estimation (HPE) is crucial for enhancing both automation and human-computer interaction. This study investigates the impact of synthetic data on HPE model efficacy, particularly examining the performance of the YOLOv8 algorithm. Using Nvidia Omniverse Isaac Sim, we created a synthetic dataset called ISAAC, designed for various complex scenarios. This tool was chosen for its ability to simulate highly realistic and intricate industrial contexts with advanced physics and AI capabilities. The inclusion of this synthetic dataset significantly enhances model accuracy, evidenced by up to a 19% increase in mean Average Precision (mAP) at an Intersection over Union (IoU) of 0.5, and a 12% improvement across the 0.5-0.95 IoU range compared to traditional datasets. These results highlight the substantial advantages of synthetic data in training more accurate and robust HPE models, advocating for the integration of innovative data solutions in the field of computer vision.