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Jamie Shotton

Jamie Shotton

I am a Principal Researcher in the Computer Vision and Machine Learning and Perception groups at Microsoft Research in Cambridge, UK.  My research is focused at the intersection of machine learning, computer vision, and graphics, with particular emphasis on human body and hand pose estimation.

Follow me on Twitter: @JamieDJS.


Research Highlights

Hand Pose Estimation

Real-time, accurate, robust, and flexible articulated tracking of the human hand.


Decision Jungles

Memory-efficient generalization of decision trees and forests with improved generalization.


Scene Coordinate Regression Forests

A new approach to 6D camera pose estimation by regression 3D scene coordinates.

Human pose estimation for Kinect

Our work on human body part recognition for Kinect.



Former Interns

(In rough reverse chronological order)

Short Biography

Jamie Shotton studied Computer Science at the University of Cambridge, where he remained for his PhD in computer vision and machine learning for visual object recognition. He joined Microsoft Research in 2008 where he is now a Principal Researcher in the Machine Learning & Perception group. His research focuses at the intersection of vision, graphics, and machine learning, with particular interests including human pose and shape estimation, object recognition, gesture and action recognition, and medical imaging. He has received multiple Best Paper and Best Demo awards at top academic conferences. His work on machine learning for body part recognition for Kinect was awarded the Royal Academy of Engineering's gold medal MacRobert Award 2011, and he shares Microsoft's Outstanding Technical Achievement Award for 2012 with the Kinect engineering team. In 2014 he received the PAMI Young Researcher Award.