I am a Senior 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 pose estimation.
New ECCV 2014 paper: Learning 6D Object Pose Estimation using 3D Object Coordinates.
New SIGGRAPH 2014 paper: Learning to be a Depth Camera.
CVPR 2014 Best Demo Honorable Mention Award for Learning to be a Depth Camera.
Three papers at CVPR 2014:
- Our RetroDepth paper wins Best Paper Award at CHI 2014!
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.
Our work on human body part recognition for Kinect.
- Tutorial on Decision Forests and Fields as presented at ICCV 2013.
- 7-Scenes RGB-D camera relocalization dataset now available.
- Decision Forests book including tutorial and software available here.
- For work before I joined MSR, please see my external site.
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 Senior 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.