Single Shot Corrective CNN for Anatomically Correct 3D Hand Pose Estimation

Published in "Frontiers in Artificial Intelligence - Machine Learning and Artificial Intelligence"
Joseph H. Isaac , Manivannan Muniyandi , Balaraman Ravindran

Depth-based 3D hand trackers are expected to estimate highly accurate poses of the human hand given the image. One of the critical problems in tracking the hand pose is the generation of realistic predictions. This paper proposes a novel “anatomical filter” that accepts a hand pose from a hand tracker and generates the closest possible pose within the real human hand’s anatomical bounds. The filter works by calculating the 26-DoF vector representing the joint angles and correcting those angles based on the real human hand’s biomechanical limitations. The proposed filter can be plugged into any hand tracker to enhance its performance. The filter has been tested on two state-of-the-art 3D hand trackers. The empirical observations show that our proposed filter improves the hand pose’s anatomical correctness and allows a smooth trade-off with pose error. The filter achieves the lowest prediction error when used with state-of-the-art trackers at 10% correction.