Problems of learning on manifolds
Problems of learning on manifolds
Monocular Human Motion Capture with a Mixture of Regressors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Pose Tracking in Monocular Sequence Using Multilevel Structured Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Twin Gaussian Processes for Structured Prediction
International Journal of Computer Vision
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In this letter, we introduce a semi-supervised manifold regularization framework for human pose estimation. We utilize the unlabeled data to compensate for the complexities in the input space and model the underlying manifold by a nearest neighbor graph. We argue that the optimal graph is a subgraph of the k nearest neighbors (k-NN) graph. Then, we estimate distances in the output space to approximate this subgraph. In addition, we use the underlying manifold of the points in the output space to introduce a novel regularization term which captures the correlation among the output dimensions. The modified graph and the proposed regularization term are utilized for a smooth regression over both the learned input and output manifolds. Experimental results on various human activities demonstrate the superiority of the proposed algorithm compared to the current state of the art methods.