JointPose: Jointly Optimizing Evolutionary Data Augmentation and Prediction Neural Network for 3D Human Pose Estimation

3D human pose estimation plays important roles in various human-machine interactive applications, but lacking diversity in existing labeled 3D human posture dataset restricts the generalization ability of deep learning based models. Data augmentation is therefore an important method to solve this problem. However, data augmentation and pose estimation network training are usually treated as two isolated processes, limiting the performance of pose estimation network. In this paper, we developed an improved data augmentation method which jointly performs pose network estimation and data augmentation by designing a reward/penalty strategy for effective joint training, making model training and data augmentation improve each other. In particular, an improved evolutionary data augmentation method is proposed to generate the distribution of nodes in crossover and rotation angles in mutation through the process of the evolution. Extensive experiments show that our approach not only significantly improves state-of-the-art models without additional data efforts but also is extremely competitive with other advanced methods.