Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Efficient Regression of General-Activity Human Poses from Depth Images

Ross Girshick, Jamie Shotton, Pushmeet Kohli, Antonio Criminisi, and Andrew Fitzgibbon


We present a new approach to general-activity human pose estimation from depth images, building on Hough forests. We extend existing techniques in several ways: real time prediction of multiple 3D joints, explicit learning of voting weights, vote compression to allow larger training sets, and a comparison of several decision-tree training objectives. Key aspects of our work include: regression directly from the raw depth image, without the use of an arbitrary intermediate representation; applicability to general motions (not constrained to particular activities) and the ability to localize occluded as well as visible body joints. Experimental results demonstrate that our method produces state of the art results on several data sets including the challenging MSRC-5000 pose estimation test set, at a speed of about 200 frames per second. Results on silhouettes suggest broader applicability to other imaging modalities.


Publication typeInproceedings
Published inICCV

Newer versions

Jamie Shotton, Ross Girshick, Andrew Fitzgibbon, Toby Sharp, Mat Cook, Mark Finocchio, Richard Moore, Pushmeet Kohli, Antonio Criminisi, Alex Kipman, and Andrew Blake. Efficient Human Pose Estimation from Single Depth Images, Trans. PAMI, IEEE, 2012.

> Publications > Efficient Regression of General-Activity Human Poses from Depth Images