3D Human Pose from Silhouettes by Relevance Vector Regression

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 histogramof-shape-contexts descriptors. For the main regression, we evaluate both regularized least squares and Relevance Vector Machine (RVM) regressors over both linear and kernel bases. The RVM's provide much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. For realism and good generalization with respect to viewpoints, we train the regressors on images resynthesized from real human motion capture data, and test it both quantitatively on similar independent t...

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Ankur Agarwal, Bill Triggs
• Keywords: Computer Vision, CVPR 2004, Human Body Pose, Monocular Image Sequences, Shape Descriptor Vectors, Silhouette Segmentation Errors, Simpler Upper Body