2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)最新文献

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A Perceptual Evaluation of Generative Adversarial Network Real-Time Synthesized Drum Sounds in a Virtual Environment 虚拟环境中生成对抗网络实时合成鼓声的感知评价

2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) Pub Date : 2018-12-01 DOI: 10.1109/AIVR.2018.00030

Minwook Chang, Y. Kim, G. Kim

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引用次数: 4

Machine Learning Architectures to Predict Motion Sickness Using a Virtual Reality Rollercoaster Simulation Tool 使用虚拟现实过山车模拟工具预测晕动病的机器学习架构

2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) Pub Date : 2018-11-02 DOI: 10.1109/AIVR.2018.00032

Stefan Hell, V. Argyriou

{"title":"Machine Learning Architectures to Predict Motion Sickness Using a Virtual Reality Rollercoaster Simulation Tool","authors":"Stefan Hell, V. Argyriou","doi":"10.1109/AIVR.2018.00032","DOIUrl":"https://doi.org/10.1109/AIVR.2018.00032","url":null,"abstract":"Virtual Reality (VR) can cause an unprecedented immersion and feeling of presence yet a lot of users experience motion sickness when moving through a virtual environment. Rollercoaster rides are popular in Virtual Reality but have to be well designed to limit the amount of nausea the user may feel. This paper describes a novel framework to get automated ratings on motion sickness using Neural Networks. An application that lets users create rollercoasters directly in VR, share them with other users and ride and rate them is used to gather real-time data related to the in-game behaviour of the player, the track itself and users' ratings based on a Simulator Sickness Questionnaire (SSQ) integrated into the application. Machine learning architectures based on deep neural networks are trained using this data aiming to predict motion sickness levels. While this paper focuses on rollercoasters this framework could help to rate any VR application on motion sickness and intensity that involves camera movement. A new well defined dataset is provided in this paper and the performance of the proposed architectures are evaluated in a comparative study.","PeriodicalId":371868,"journal":{"name":"2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129699229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

BRIEF: Backward Reduction of CNNs with Information Flow Analysis 摘要:基于信息流分析的cnn逆向约简

2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) Pub Date : 2018-07-16 DOI: 10.1109/AIVR.2018.00014

Yu-Hsun Lin, Chun-Nan Chou, Edward Y. Chang

引用次数: 0

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