Skeletal tracking first became viable as a product around 2011, since then, many different additional approaches to the problem have been explored, but almost all of them suffer from high compute usage – which absorbs a lot of GPU or CPU cycles, as well as being designed for general pose estimation. By focusing on VR/AR, it allows us to use the position of the HMD to train with a much smaller machine learning model.

Philip will share some of his work on this convolutional neural network with integrated head and controller information approach, as well as talking about some of his prior work on hand tracking in real time using only a CPU