This talk provides a brief overview of how to apply GPU-based deep learning techniques to extract 3D human motion capture from standard 2D RGB video. We describe in detail the stages of our CUDA-based pipeline from training to cloud-based deployment. Our training system is a novel mix of real world data collected with Kinect cameras and synthetic data based on rendering thousands of virtual humans generated in the Unity game engine. Our execution pipeline is a series of connected models including 2D video to 2D pose estimation and 2D pose to 3D pose estimation. We describe how this system can be integrated into a variety of mobile applications ranging from social media to sports training. A live demo using a mobile phone connected into an AWS GPU cluster will be presented.