Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation

Catalin Ionescu, Joao Carreira, Cristian Sminchisescu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1661-1668

Abstract


Recently, the emergence of Kinect systems has demonstrated the benefits of predicting an intermediate body part labeling for 3D human pose estimation, in conjunction with RGB-D imagery. The availability of depth information plays a critical role, so an important question is whether a similar representation can be developed with sufficient robustness in order to estimate 3D pose from RGB images. This paper provides evidence for a positive answer, by leveraging (a) 2D human body part labeling in images, (b) second-order label-sensitive pooling over dynamically computed regions resulting from a hierarchical decomposition of the body, and (c) iterative structured-output modeling to contextualize the process based on 3D pose estimates. For robustness and generalization, we take advantage of a recent large-scale 3D human motion capture dataset, Human3.6M [18] that also has human body part labeling annotations available with images. We provide extensive experimental studies where alternative intermediate representations are compared and report a substantial 33% error reduction over competitive discriminative baselines that regress 3D human pose against global HOG features.

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[bibtex]
@InProceedings{Ionescu_2014_CVPR,
author = {Ionescu, Catalin and Carreira, Joao and Sminchisescu, Cristian},
title = {Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}