Sciweavers

Share
PAMI
2006

Recovering 3D Human Pose from Monocular Images

8 years 6 months ago
Recovering 3D Human Pose from Monocular Images
We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descriptor vectors extracted automatically from image silhouettes. For robustness against local silhouette segmentation errors, silhouette shape is encoded by histogram-of-shape-contexts descriptors. We evaluate several different regression methods: ridge regression, Relevance Vector Machine (RVM) regression and Support Vector Machine (SVM) regression over both linear and kernel bases. The RVMs provide much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. Loss of depth and limb labelling information often makes the recovery of 3D pose from single silhouettes ambiguous. We propose two solutions to this: the first embeds the ...
Ankur Agarwal, Bill Triggs
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PAMI
Authors Ankur Agarwal, Bill Triggs
Comments (0)
books