Learn to Track: From Images to 3D Data

Aimed at a robust object pose estimation from 3D data, we develop a learning-based temporal tracking method. Its remarkable attribute is the speed of less than 2ms per frame using one CPU core. This is then extended to estimate the head pose of an arbitrary user in the scene. The final contribution is an optimization of a personalized hand shape model for a better accuracy in hand tracking.