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The Use of Deep Learning and VR Technology in Film and Television Production From the Perspective of Audience Psychology

As the development of artificial intelligence (AI) technology, the deep-learning (DL)-based Virtual Reality (VR) technology, and DL technology are applied in human-computer interaction (HCI), and their impacts on modern film and TV works production and audience psychology are analyzed. In film and T...

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Autores principales: Tong, Yangfan, Cao, Weiran, Sun, Qian, Chen, Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8080441/
https://www.ncbi.nlm.nih.gov/pubmed/33935884
http://dx.doi.org/10.3389/fpsyg.2021.634993
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author Tong, Yangfan
Cao, Weiran
Sun, Qian
Chen, Dong
author_facet Tong, Yangfan
Cao, Weiran
Sun, Qian
Chen, Dong
author_sort Tong, Yangfan
collection PubMed
description As the development of artificial intelligence (AI) technology, the deep-learning (DL)-based Virtual Reality (VR) technology, and DL technology are applied in human-computer interaction (HCI), and their impacts on modern film and TV works production and audience psychology are analyzed. In film and TV production, audiences have a higher demand for the verisimilitude and immersion of the works, especially in film production. Based on this, a 2D image recognition system for human body motions and a 3D recognition system for human body motions based on the convolutional neural network (CNN) algorithm of DL are proposed, and an analysis framework is established. The proposed systems are simulated on practical and professional datasets, respectively. The results show that the algorithm's computing performance in 2D image recognition is 7–9 times higher than that of the Open Pose method. It runs at 44.3 ms in 3D motion recognition, significantly lower than the Open Pose method's 794.5 and 138.7 ms. Although the detection accuracy has dropped by 2.4%, it is more efficient and convenient without limitations of scenarios in practical applications. The AI-based VR and DL enriches and expands the role and application of computer graphics in film and TV production using HCI technology theoretically and practically.
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spelling pubmed-80804412021-04-29 The Use of Deep Learning and VR Technology in Film and Television Production From the Perspective of Audience Psychology Tong, Yangfan Cao, Weiran Sun, Qian Chen, Dong Front Psychol Psychology As the development of artificial intelligence (AI) technology, the deep-learning (DL)-based Virtual Reality (VR) technology, and DL technology are applied in human-computer interaction (HCI), and their impacts on modern film and TV works production and audience psychology are analyzed. In film and TV production, audiences have a higher demand for the verisimilitude and immersion of the works, especially in film production. Based on this, a 2D image recognition system for human body motions and a 3D recognition system for human body motions based on the convolutional neural network (CNN) algorithm of DL are proposed, and an analysis framework is established. The proposed systems are simulated on practical and professional datasets, respectively. The results show that the algorithm's computing performance in 2D image recognition is 7–9 times higher than that of the Open Pose method. It runs at 44.3 ms in 3D motion recognition, significantly lower than the Open Pose method's 794.5 and 138.7 ms. Although the detection accuracy has dropped by 2.4%, it is more efficient and convenient without limitations of scenarios in practical applications. The AI-based VR and DL enriches and expands the role and application of computer graphics in film and TV production using HCI technology theoretically and practically. Frontiers Media S.A. 2021-03-18 /pmc/articles/PMC8080441/ /pubmed/33935884 http://dx.doi.org/10.3389/fpsyg.2021.634993 Text en Copyright © 2021 Tong, Cao, Sun and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Tong, Yangfan
Cao, Weiran
Sun, Qian
Chen, Dong
The Use of Deep Learning and VR Technology in Film and Television Production From the Perspective of Audience Psychology
title The Use of Deep Learning and VR Technology in Film and Television Production From the Perspective of Audience Psychology
title_full The Use of Deep Learning and VR Technology in Film and Television Production From the Perspective of Audience Psychology
title_fullStr The Use of Deep Learning and VR Technology in Film and Television Production From the Perspective of Audience Psychology
title_full_unstemmed The Use of Deep Learning and VR Technology in Film and Television Production From the Perspective of Audience Psychology
title_short The Use of Deep Learning and VR Technology in Film and Television Production From the Perspective of Audience Psychology
title_sort use of deep learning and vr technology in film and television production from the perspective of audience psychology
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8080441/
https://www.ncbi.nlm.nih.gov/pubmed/33935884
http://dx.doi.org/10.3389/fpsyg.2021.634993
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