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.
"Accurate, Robust, Flexible" hand tracking paper, supplementary material, and video now available.
Papers on exploiting uncertainty in scene coordinate regression and on learning a parametric shape basis for the human hand accepted to CVPR 2015. Coming soon.
Hand tracking paper awarded Best of CHI 2015 Honorable Mention!
Real-time, accurate, robust, and flexible articulated tracking of the human hand.
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.
- Jan Stühmer
- Sameh Khamis
- Danhang Tang
- Sean Fanello
- Varun Ramakrishna
- Abner Guzman-Rivera
- Julien Valentin
- Richard Stebbing
- Nima Razavi
- Jonathan Taylor
- Gerard Pons-Moll
- Stefan Holzer
- Ross Girshick
- Albert Montillo
- Min Sun
- Richard Newcombe
- Nicolas Heess
- Inmar Givoni
- Brian Amberg
- Zhao Yi
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.