A Dual-Source Approach for 3D Pose Estimation From a Single Image

Hashim Yasin, Umar Iqbal, Bjorn Kruger, Andreas Weber, Juergen Gall; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4948-4956

Abstract


One major challenge for 3D pose estimation from a single RGB image is the acquisition of sufficient training data. In particular, collecting large amounts of training data that contain unconstrained images and are annotated with accurate 3D poses is infeasible. We therefore propose to use two independent training sources. The first source consists of images with annotated 2D poses and the second source consists of accurate 3D motion capture data. To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient and robust 3D pose retrieval. In our experiments, we show that our approach achieves state-of-the-art results and is even competitive when the skeleton structure of the two sources differ substantially.

Related Material


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[bibtex]
@InProceedings{Yasin_2016_CVPR,
author = {Yasin, Hashim and Iqbal, Umar and Kruger, Bjorn and Weber, Andreas and Gall, Juergen},
title = {A Dual-Source Approach for 3D Pose Estimation From a Single Image},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}