Neural Network-based Human Motion Smoother

Mathias Bastholm, Stella Graßhof, Sami Brandt

2022

Abstract

Recording real life human motion as a skinned mesh animation with an acceptable quality is usually difficult. Even though recent advances in pose estimation have enabled motion capture from off-the-shelf webcams, the low quality makes it infeasible for use in production quality animation. This work proposes to use recent advances in the prediction of human motion through neural networks to augment low quality human motion, in an effort to bridge the gap between cheap recording methods and high quality recording. First, a model, competitive with prior work in short-term human motion prediction, is constructed. Then, the model is trained to clean up motion from two low quality input sources, mimicking a real world scenario of recording human motion through two webcams. Experiments on simulated data show that the model is capable of significantly reducing noise, and it opens the way for future work to test the model on annotated data.

Paper Citation

in Harvard Style

Bastholm M., Graßhof S. and Brandt S. (2022). Neural Network-based Human Motion Smoother. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 24-30. DOI: 10.5220/0010790500003122

in Bibtex Style

@conference{icpram22,
author={Mathias Bastholm and Stella Graßhof and Sami Brandt},
title={Neural Network-based Human Motion Smoother},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={24-30},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010790500003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Neural Network-based Human Motion Smoother
SN - 978-989-758-549-4
AU - Bastholm M.
AU - Graßhof S.
AU - Brandt S.
PY - 2022
SP - 24
EP - 30
DO - 10.5220/0010790500003122