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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTM Press
2022
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| Subjects: | |
| Online Access: | 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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| Summary: | 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. |
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