Deformable Pose Network: A Multi-Stage Deformable Convolutional Network for 2D Hand Pose Estimation

Sartaj Ahmed Salman, Ali Zakir, Hiroki Takahashi

Abstract: Hand pose estimation undergoes a significant advancement with the evolution of Convolutional Neural Networks (CNNs) in the field of computer vision. However, existing CNNs fail in many scenarios in learning the unknown transformations and geometrical constraints along with the other existing challenges for accurate estimation of hand keypoints. To tackle these issues we proposed a multi-stage deformable convolutional network for accurate 2D hand pose estimation from monocular RGB images while considering the computational complexity. We utilized EfficientNet as a backbone due to its powerful feature extraction capability, and deformable convolution to learn about the geometrical constraints. Our proposed model called Deformable Pose Network (DPN) outperforms in predicting the 2D keypoints in complex scenarios. Our analysis on the Panoptic studio hand dataset shows that our proposed model improves the accuracy by 2.36% and 7.29% as compared to existing methods i.e., OCPM and CPM respe