A Kinematic Chain Space for Monocular Motion Capture

Bastian Wandt, Hanno Ackermann, Bodo Rosenhahn; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

Abstract


This paper deals with motion capture of kinematic chains (e.g. human skeletons) from monocular image sequences taken by uncalibrated cameras. We present a method based on projecting an observation onto a kinematic chain space (KCS). An optimization of the nuclear norm is proposed that implicitly enforces structural properties of the kinematic chain. Unlike other approaches our method is not relying on training data or previously determined constraints such as particular body lengths. The proposed algorithm is able to reconstruct scenes with little or no camera motion and previously unseen motions. It is not only applicable to human skeletons but also to other kinematic chains for instance animals or industrial robots. We achieve state-of-the-art results on different benchmark databases and real world scenes.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Wandt_2018_ECCV_Workshops,
author = {Wandt, Bastian and Ackermann, Hanno and Rosenhahn, Bodo},
title = {A Kinematic Chain Space for Monocular Motion Capture},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
month = {September},
year = {2018}
}