Neural Network-based Human Motion Smoother

Mathias Bastholm; Stella Graßhof; Sami S. Brandt

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

Neural Network-based Human Motion Smoother

Topics: Deep Learning and Neural Networks; Image and Video Analysis and Understanding; Machine Learning Methods; Motion Tracking and Action Recognition; Signal Processing

In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 24-30, 2022

Authors: Mathias Bastholm ; Stella Graßhof and Sami S. Brandt

Affiliation: Computer Science Department, IT University of Copenhagen, Denmark

Keyword(s): Recurrent Neural Networks, Reconstruction, Computer Vision, Animation Denoising.

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.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 3.139.86.56

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

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 - ICPRAM; ISBN 978-989-758-549-4; ISSN 2184-4313, SciTePress, pages 24-30. DOI: 10.5220/0010790500003122

@conference{icpram22,
author={Mathias Bastholm. and Stella Graßhof. and Sami S. Brandt.},
title={Neural Network-based Human Motion Smoother},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2022},
pages={24-30},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010790500003122},
isbn={978-989-758-549-4},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Neural Network-based Human Motion Smoother
SN - 978-989-758-549-4
IS - 2184-4313
AU - Bastholm, M.
AU - Graßhof, S.
AU - Brandt, S.
PY - 2022
SP - 24
EP - 30
DO - 10.5220/0010790500003122
PB - SciTePress