Real-Time Pose Regression with Fast Volume Descriptor Computation

We present a real-time method for estimating the pose of a human body using its 3D volume obtained from synchronized videos. The method achieves pose estimation by pose regression from its 3D volume. While the 3D volume allows us to estimate the pose robustly against self occlusions, 3D volume analysis requires a large amount of computational cost. We propose fast and stable volume tracking with efficient volume representation in a low dimensional dynamical model. Experimental results demonstrated that pose estimation of a body with a significantly deformable clothing could run at around 60 fps.