Pub Date : 2019-10-22DOI: 10.1109/AIVR46125.2019.00032
Menghe Zhang, Junsong Zhang, Dong Zhang
We present an attention training system based on the principles of multitasking training scenario and neurofeedback, which can be targeted on PCs and VR platforms. Our training system is a video game following the principle of multitasking training, which is designed for all ages. It adopts a non-invasive Electroencephalography (EEG) device Emotiv EPOC+ to collect EEG. Then wavelet package transformation(WPT) is applied to extract specific components of EEG signals. We then build a multi-class supporting vector machine(SVM) to classify different attention levels. The training system is built with the Unity game engine, which can be targeted on both desktops and Oculus VR headsets. We also launched an experiment by applying the system to preliminarily evaluate the effectiveness of our system. The results show that our system can generally improve users' abilities of multitasking and attention level.
{"title":"ATVR: An Attention Training System using Multitasking and Neurofeedback on Virtual Reality Platform","authors":"Menghe Zhang, Junsong Zhang, Dong Zhang","doi":"10.1109/AIVR46125.2019.00032","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00032","url":null,"abstract":"We present an attention training system based on the principles of multitasking training scenario and neurofeedback, which can be targeted on PCs and VR platforms. Our training system is a video game following the principle of multitasking training, which is designed for all ages. It adopts a non-invasive Electroencephalography (EEG) device Emotiv EPOC+ to collect EEG. Then wavelet package transformation(WPT) is applied to extract specific components of EEG signals. We then build a multi-class supporting vector machine(SVM) to classify different attention levels. The training system is built with the Unity game engine, which can be targeted on both desktops and Oculus VR headsets. We also launched an experiment by applying the system to preliminarily evaluate the effectiveness of our system. The results show that our system can generally improve users' abilities of multitasking and attention level.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128012869","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}
Pub Date : 2019-10-15DOI: 10.1109/AIVR46125.2019.00030
J. Sorger, Manuela Waldner, Wolfgang Knecht, Alessio Arleo
Analysis of large dynamic networks is a thriving research field, typically relying on 2D graph representations. The advent of affordable head mounted displays sparked new interest in the potential of 3D visualization for immersive network analytics. Nevertheless, most solutions do not scale well with the number of nodes and edges and rely on conventional fly-or walk-through navigation. In this paper, we present a novel approach for the exploration of large dynamic graphs in virtual reality that interweaves two navigation metaphors: overview exploration and immersive detail analysis. We thereby use the potential of state-of-the-art VR headsets, coupled with a web-based 3D rendering engine that supports heterogeneous input modalities to enable ad-hoc immersive network analytics. We validate our approach through a performance evaluation and a case study with experts analyzing medical data.
{"title":"Immersive Analytics of Large Dynamic Networks via Overview and Detail Navigation","authors":"J. Sorger, Manuela Waldner, Wolfgang Knecht, Alessio Arleo","doi":"10.1109/AIVR46125.2019.00030","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00030","url":null,"abstract":"Analysis of large dynamic networks is a thriving research field, typically relying on 2D graph representations. The advent of affordable head mounted displays sparked new interest in the potential of 3D visualization for immersive network analytics. Nevertheless, most solutions do not scale well with the number of nodes and edges and rely on conventional fly-or walk-through navigation. In this paper, we present a novel approach for the exploration of large dynamic graphs in virtual reality that interweaves two navigation metaphors: overview exploration and immersive detail analysis. We thereby use the potential of state-of-the-art VR headsets, coupled with a web-based 3D rendering engine that supports heterogeneous input modalities to enable ad-hoc immersive network analytics. We validate our approach through a performance evaluation and a case study with experts analyzing medical data.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130029562","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}
Pub Date : 2019-02-01DOI: 10.1109/AIVR46125.2019.00071
Tomer Weiss, Alan Litteneker, Chenfanfu Jiang, Demetri Terzopoulos
Various methods have been proposed for simulating crowds of agents in recent years. Regrettably, not all are computational scalable as the number of simulated agents grows. Such quality is particularly important for virtual production, gaming, and immersive reality platforms. In this work, we provide an open-source implementation for the recently proposed Position-based dynamics approach to crowd simulation. Position-based crowd simulation was proven to be real-time, and scalable for crowds of up to 100k agents, while retaining dynamic agent and group behaviors. We provide both non-parallel, and GPU-based implementations. Our implementation is demonstrated on several scenarios, including examples from the original work. We witness interactive computation run-times, as well as visually realistic collective behavior.
{"title":"Implementing Position-Based Real-Time Simulation of Large Crowds","authors":"Tomer Weiss, Alan Litteneker, Chenfanfu Jiang, Demetri Terzopoulos","doi":"10.1109/AIVR46125.2019.00071","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00071","url":null,"abstract":"Various methods have been proposed for simulating crowds of agents in recent years. Regrettably, not all are computational scalable as the number of simulated agents grows. Such quality is particularly important for virtual production, gaming, and immersive reality platforms. In this work, we provide an open-source implementation for the recently proposed Position-based dynamics approach to crowd simulation. Position-based crowd simulation was proven to be real-time, and scalable for crowds of up to 100k agents, while retaining dynamic agent and group behaviors. We provide both non-parallel, and GPU-based implementations. Our implementation is demonstrated on several scenarios, including examples from the original work. We witness interactive computation run-times, as well as visually realistic collective behavior.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116920486","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}
Pub Date : 2019-01-04DOI: 10.1109/AIVR46125.2019.00018
Alisha Sharma, Jonathan Ventura
We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. Panoramic depth estimation is an important technology for applications such as virtual reality, 3d modeling, and autonomous robotic navigation. In contrast to previous approaches for applying convolutional neural networks to panoramic imagery, we use the cylindrical panoramic projection which allows for the use of the traditional CNN layers such as convolutional filters and max pooling without modification. Our evaluation of synthetic and real data shows that unsupervised learning of depth and ego-motion on cylindrical panoramic images can produce high-quality depth maps and that an increased field-of-view improves ego-motion estimation accuracy. We also introduce Headcam, a novel dataset of panoramic video collected from a helmet-mounted camera while biking in an urban setting.
{"title":"Unsupervised Learning of Depth and Ego-Motion From Cylindrical Panoramic Video","authors":"Alisha Sharma, Jonathan Ventura","doi":"10.1109/AIVR46125.2019.00018","DOIUrl":"https://doi.org/10.1109/AIVR46125.2019.00018","url":null,"abstract":"We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. Panoramic depth estimation is an important technology for applications such as virtual reality, 3d modeling, and autonomous robotic navigation. In contrast to previous approaches for applying convolutional neural networks to panoramic imagery, we use the cylindrical panoramic projection which allows for the use of the traditional CNN layers such as convolutional filters and max pooling without modification. Our evaluation of synthetic and real data shows that unsupervised learning of depth and ego-motion on cylindrical panoramic images can produce high-quality depth maps and that an increased field-of-view improves ego-motion estimation accuracy. We also introduce Headcam, a novel dataset of panoramic video collected from a helmet-mounted camera while biking in an urban setting.","PeriodicalId":274566,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114756985","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}
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