Portrait of Toby Sharp

Toby Sharp

Principal RSDE

About

Toby Sharp is a Principal Research Software Development Engineer in the Machine Learning and Perception group at Microsoft Research, Cambridge, where he has worked since 2005. He has transferred several research projects into Microsoft technologies and products, and has contributed computer vision components to Microsoft’s Kinect, Office and LifeCam software. His other work includes computer graphics, image editing and GPU programming. He holds a BSc in Mathematics (1st) from the University of York.

Prior to joining Microsoft, he developed consumer photo and video editing software for Serif in the UK. In 2010 he received the Excellence in Design award from RSNA for real-time virtual colonoscopy software. In 2011 he and colleagues received the Best Paper prize at CVPR and the Royal Academy of Engineering MacRobert Award for the machine learning contribution to Kinect’s body tracking.

Toby is a Senior Member of the IEEE.

Projects

Fully Articulated Hand Tracking

Established: October 2, 2014

We present a new real-time articulated hand tracker which can enable new possibilities for human-computer interaction (HCI). Our system accurately reconstructs complex hand poses across a variety of subjects using only a single depth camera. It also allows for a…

Decision Forests

Established: July 25, 2012

Decision Forests for Computer Vision and Medical Image Analysis A. Criminisi and J. Shotton Springer 2013, XIX, 368 p. 143 illus., 136 in color. ISBN 978-1-4471-4929-3  

KinectFusion Project Page

Established: August 9, 2011

This project investigates techniques to track the 6DOF position of handheld depth sensing cameras, such as Kinect, as they move through space and perform high quality 3D surface reconstructions for interaction. Other collaborators (missing from the list below): Richard Newcombe…

Human Pose Estimation for Kinect

Established: January 25, 2011

Kinect for Xbox 360 and Windows makes you the controller by fusing 3D imaging hardware with markerless human-motion capture software. Our group investigates such software. Mixing computer vision, graphics, and machine learning techniques, we look at how to build algorithms…

Publications

2015

2014

2013

2012

2011

2010

2009

2008

Projects