A review: Deep learning for 3D reconstruction of human motion detection
3D reconstruction of human motion is an important research topic in VR/AR content creation, virtual fitting, human-computer interaction and other fields. Deep learning theory has made important achievements in human motion detection, recognition, tracking and other aspects, and human motion detectio...
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my.utm.1088202024-12-09T07:45:38Z http://eprints.utm.my/108820/ A review: Deep learning for 3D reconstruction of human motion detection Yang, Junzi Ismail, Ajune Wanis QA75 Electronic computers. Computer science 3D reconstruction of human motion is an important research topic in VR/AR content creation, virtual fitting, human-computer interaction and other fields. Deep learning theory has made important achievements in human motion detection, recognition, tracking and other aspects, and human motion detection and recognition is an important link in 3D reconstruction. In this paper, the deep learning algorithms in recent years, mainly used for human motion detection and recognition, are reviewed, and the existing methods are divided into three types: CNN-based, RNN-based and GNN-based. At the same time, the main stream data sets and frameworks adopted in the references are summarized. The content of this paper provides some references for the research of 3D reconstruction of human motion. Penerbit UTM Press 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/108820/1/AjuneWanisIsmail2022_AReviewDeepLearningfor3DReconstruction.pdf Yang, Junzi and Ismail, Ajune Wanis (2022) A review: Deep learning for 3D reconstruction of human motion detection. International Journal of Innovative Computing, 12 (1). pp. 65-71. ISSN 2180-4370 http://dx.doi.org/10.11113/ijic.v12n1.353 DOI : 10.11113/ijic.v12n1.353 |
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QA75 Electronic computers. Computer science Yang, Junzi Ismail, Ajune Wanis A review: Deep learning for 3D reconstruction of human motion detection |
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3D reconstruction of human motion is an important research topic in VR/AR content creation, virtual fitting, human-computer interaction and other fields. Deep learning theory has made important achievements in human motion detection, recognition, tracking and other aspects, and human motion detection and recognition is an important link in 3D reconstruction. In this paper, the deep learning algorithms in recent years, mainly used for human motion detection and recognition, are reviewed, and the existing methods are divided into three types: CNN-based, RNN-based and GNN-based. At the same time, the main stream data sets and frameworks adopted in the references are summarized. The content of this paper provides some references for the research of 3D reconstruction of human motion. |
format |
Article |
author |
Yang, Junzi Ismail, Ajune Wanis |
author_facet |
Yang, Junzi Ismail, Ajune Wanis |
author_sort |
Yang, Junzi |
title |
A review: Deep learning for 3D reconstruction of human motion detection |
title_short |
A review: Deep learning for 3D reconstruction of human motion detection |
title_full |
A review: Deep learning for 3D reconstruction of human motion detection |
title_fullStr |
A review: Deep learning for 3D reconstruction of human motion detection |
title_full_unstemmed |
A review: Deep learning for 3D reconstruction of human motion detection |
title_sort |
review: deep learning for 3d reconstruction of human motion detection |
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Penerbit UTM Press |
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2022 |
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http://eprints.utm.my/108820/1/AjuneWanisIsmail2022_AReviewDeepLearningfor3DReconstruction.pdf http://eprints.utm.my/108820/ http://dx.doi.org/10.11113/ijic.v12n1.353 |
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13.222552 |