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Recovering 3D Human Pose from Monocular Images
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@MISC{Agarwal_recovering3d,
author = {Ankur Agarwal and Bill Triggs},
title = { Recovering 3D Human Pose from Monocular Images},
year = {}
}
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Abstract
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 method in a tracking framework, using dynamics from the previous state estimate to disambiguate the pose; the second uses a mixture of regressors framework to return multiple solutions for each silhouette. We show that the resulting system tracks long sequences stably, and is also capable of accurately reconstructing 3D human pose from single images, giving multiple possible solutions in ambiguous cases. For realism and good generalization over a wide range of viewpoints, we train the regressors on images resynthesized from real human motion capture data. The method is demonstrated on a 54-parameter full body pose model, both quantitatively on independent but similar test data, and qualitatively on real image sequences. Mean angular errors of 4–5 degrees are obtained — a factor of 3 better than the current state of the art for the much simpler upper body problem.
Keyphrases
human pose monocular image single image kernel base local silhouette segmentation error silhouette shape explicit body model worthwhile improvement ambiguous case single silhouette wide range previous state estimate histogram-of-shape-contexts descriptor multiple solution shape descriptor vector body problem 54-parameter full body pose model human body real human motion capture data real image sequence similar test data prior labelling mean angular error multiple possible solution several different regression method direct nonlinear regression relevance vector machine support vector machine monocular image sequence tracking framework image silhouette much simpler current state body part system track long sequence ridge regression good generalization
