This paper presents EXController, a new controller-mounted finger posture recognition device specially designed for VR handheld controllers. We seek to provide additional input through real-time vision sensing by attaching a near infrared (NIR) camera onto the controller. We designed and implemented an exploratory prototype with a HTC Vive controller. The NIR camera is modified from a traditional webcam and applied with a data-driven Convolutional Neural Network (CNN) classifier. We designed 12 different finger gestures and trained the CNN classifier with a dataset from 20 subjects, achieving an average accuracy of 86.17% across - subjects, and, approximately more than 92% on three of the finger postures, and more than 89% on the top-4 accuracy postures. We also developed a Unity demo that shows matched finger animations, running at approximately 27 fps in real-time.
{"title":"EXController","authors":"Junjian Zhang, Yaohao Chen, Satoshi Hashizume, Naoya Muramatsu, Kotaro Omomo, Riku Iwasaki, Kaji Wataru, Yoichi Ochiai","doi":"10.1145/3281505.3283385","DOIUrl":"https://doi.org/10.1145/3281505.3283385","url":null,"abstract":"This paper presents EXController, a new controller-mounted finger posture recognition device specially designed for VR handheld controllers. We seek to provide additional input through real-time vision sensing by attaching a near infrared (NIR) camera onto the controller. We designed and implemented an exploratory prototype with a HTC Vive controller. The NIR camera is modified from a traditional webcam and applied with a data-driven Convolutional Neural Network (CNN) classifier. We designed 12 different finger gestures and trained the CNN classifier with a dataset from 20 subjects, achieving an average accuracy of 86.17% across - subjects, and, approximately more than 92% on three of the finger postures, and more than 89% on the top-4 accuracy postures. We also developed a Unity demo that shows matched finger animations, running at approximately 27 fps in real-time.","PeriodicalId":138249,"journal":{"name":"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122726671","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}
Sylvia Rothe, Boris Kegeles, Mathias Allary, H. Hussmann
Watching a 360° movie with Head Mounted Displays (HMDs) the viewer feels to be inside the movie and can experience it in an immersive way. The head of the viewer is exactly in the same place as the camera was when the scene was recorded. Viewing a movie by HMDs from the perspective of the camera can raise some challenges, e.g. heights of well-known objects can irritate the viewer in the case the camera height does not correspond to the physical eye height. The aim of this work is to study how the position of the camera influences presence, sickness and the user experience of the viewer. For that we considered several watching postures as well as various camera heights. The results of our experiments suggest that differences between camera and eye heights are more accepted, if the camera position is lower than the viewer's own eye height. Additionally, sitting postures are preferred and can be adapted easier than standing postures. These results can be applied to improve guidelines for 360° filmmakers.
{"title":"The impact of camera height in cinematic virtual reality","authors":"Sylvia Rothe, Boris Kegeles, Mathias Allary, H. Hussmann","doi":"10.1145/3281505.3283383","DOIUrl":"https://doi.org/10.1145/3281505.3283383","url":null,"abstract":"Watching a 360° movie with Head Mounted Displays (HMDs) the viewer feels to be inside the movie and can experience it in an immersive way. The head of the viewer is exactly in the same place as the camera was when the scene was recorded. Viewing a movie by HMDs from the perspective of the camera can raise some challenges, e.g. heights of well-known objects can irritate the viewer in the case the camera height does not correspond to the physical eye height. The aim of this work is to study how the position of the camera influences presence, sickness and the user experience of the viewer. For that we considered several watching postures as well as various camera heights. The results of our experiments suggest that differences between camera and eye heights are more accepted, if the camera position is lower than the viewer's own eye height. Additionally, sitting postures are preferred and can be adapted easier than standing postures. These results can be applied to improve guidelines for 360° filmmakers.","PeriodicalId":138249,"journal":{"name":"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131276263","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}
Juno Kim, Matthew Moroz, Benjamin Arcioni, S. Palmisano
We measured presence and perceived scene stability in a virtual environment viewed with different head-to-display lag (i.e., system lag) on the Oculus Rift (CV1). System lag was added on top of the measured benchmark system latency (22.3 ms) for our visual scene rendered in OpenGL Shading Language (GLSL). Participants made active head oscillations in pitch at 1.0Hz while viewing displays. We found that perceived scene instability increased and presence decreased when increasing system lag, which we attribute to the effect of multisensory visual-vestibular interactions on the interpretation of the visual information presented.
{"title":"Effects of head-display lag on presence in the oculus rift","authors":"Juno Kim, Matthew Moroz, Benjamin Arcioni, S. Palmisano","doi":"10.1145/3281505.3281607","DOIUrl":"https://doi.org/10.1145/3281505.3281607","url":null,"abstract":"We measured presence and perceived scene stability in a virtual environment viewed with different head-to-display lag (i.e., system lag) on the Oculus Rift (CV1). System lag was added on top of the measured benchmark system latency (22.3 ms) for our visual scene rendered in OpenGL Shading Language (GLSL). Participants made active head oscillations in pitch at 1.0Hz while viewing displays. We found that perceived scene instability increased and presence decreased when increasing system lag, which we attribute to the effect of multisensory visual-vestibular interactions on the interpretation of the visual information presented.","PeriodicalId":138249,"journal":{"name":"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127577240","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}
P. Wozniak, Antonio Capobianco, N. Javahiraly, D. Curticapean
We present results of a preliminary study on our planned system for the detection of obstacles in the physical environment by means of an RGB-D sensor and their unobtrusive signalling using metaphors within the virtual environment (VE).
{"title":"Towards unobtrusive obstacle detection and notification for VR","authors":"P. Wozniak, Antonio Capobianco, N. Javahiraly, D. Curticapean","doi":"10.1145/3281505.3283391","DOIUrl":"https://doi.org/10.1145/3281505.3283391","url":null,"abstract":"We present results of a preliminary study on our planned system for the detection of obstacles in the physical environment by means of an RGB-D sensor and their unobtrusive signalling using metaphors within the virtual environment (VE).","PeriodicalId":138249,"journal":{"name":"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133141419","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}
Purpose: This study explores eyestrain and its possible impacts on learning performances and quality of experience using different apparatuses and imaging. Materials and Methods: 69 participants played a serious game simulating a job interview with a Samsung Gear VR Head Mounted Display (HMD) or a computer screen. The study was conducted according to a double-blinded protocol. Participants were randomly assigned to 3 groups: PC, HMD biocular and HMD stereoscopy (S3D). Participants played the game twice, allowing between group analyses. Eyestrain was assessed pre- and post-exposure on a chin-head rest with optometric measures. Learning traces were obtained in-game by registering response time and scores. Quality of experience was measured with questionnaires assessing Presence, Flow and Visual Comfort. Results: eyestrain was significantly higher with HMDs than PC based on Punctum Proximum of accommodation and visual acuity variables and tends to be higher with S3D. Learning was more efficient in HMDs conditions based on time for answering but the group with stereoscopy performed lower than the binocular imaging one. Quality of Experience was better based on visual discomfort with the PC condition than with HMDs. Conclusion: learning expected answers from a job interview is more efficient while using HMDs than a computer screen. However, eyestrain tends to be higher while using HMDs and S3D. The quality of experience was also negatively impacted with HMDs compared to computer screen. Not using S3D or lowering its impact should be explored to provide comfortable learning experience.1
{"title":"Eyestrain impacts on learning job interview with a serious game in virtual reality: a randomized double-blinded study","authors":"Alexis D. Souchet, Stéphanie Philippe, Dimitri Zobel, Floriane Ober, Aurélien Léveque, Laure Leroy","doi":"10.1145/3281505.3281509","DOIUrl":"https://doi.org/10.1145/3281505.3281509","url":null,"abstract":"Purpose: This study explores eyestrain and its possible impacts on learning performances and quality of experience using different apparatuses and imaging. Materials and Methods: 69 participants played a serious game simulating a job interview with a Samsung Gear VR Head Mounted Display (HMD) or a computer screen. The study was conducted according to a double-blinded protocol. Participants were randomly assigned to 3 groups: PC, HMD biocular and HMD stereoscopy (S3D). Participants played the game twice, allowing between group analyses. Eyestrain was assessed pre- and post-exposure on a chin-head rest with optometric measures. Learning traces were obtained in-game by registering response time and scores. Quality of experience was measured with questionnaires assessing Presence, Flow and Visual Comfort. Results: eyestrain was significantly higher with HMDs than PC based on Punctum Proximum of accommodation and visual acuity variables and tends to be higher with S3D. Learning was more efficient in HMDs conditions based on time for answering but the group with stereoscopy performed lower than the binocular imaging one. Quality of Experience was better based on visual discomfort with the PC condition than with HMDs. Conclusion: learning expected answers from a job interview is more efficient while using HMDs than a computer screen. However, eyestrain tends to be higher while using HMDs and S3D. The quality of experience was also negatively impacted with HMDs compared to computer screen. Not using S3D or lowering its impact should be explored to provide comfortable learning experience.1","PeriodicalId":138249,"journal":{"name":"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121983962","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}
Daniel Medeiros, R. K. D. Anjos, Daniel Mendes, J. Pereira, A. Raposo, Joaquim Jorge
Head-Mounted Displays are useful to place users in virtual reality (VR). They do this by totally occluding the physical world, including users' bodies. This can make self-awareness problematic. Indeed, researchers have shown that users' feeling of presence and spatial awareness are highly influenced by their virtual representations, and that self-embodied representations (avatars) of their anatomy can make the experience more engaging. On the other hand, recent user studies show a penchant towards a third-person view of one's own body to seemingly improve spatial awareness. However, due to its unnaturality, we argue that a third-person perspective is not as effective or convenient as a first-person view for task execution in VR. In this paper, we investigate, through a user evaluation, how these perspectives affect task performance and embodiment, focusing on navigation tasks, namely walking while avoiding obstacles. For each perspective, we also compare three different levels of realism for users' representation, specifically a stylized abstract avatar, a mesh-based generic human, and a real-time point-cloud rendering of the users' own body. Our results show that only when a third-person perspective is coupled with a realistic representation, a similar sense of embodiment and spatial awareness is felt. In all other cases, a first-person perspective is still better suited for navigation tasks, regardless of representation.
{"title":"Keep my head on my shoulders!: why third-person is bad for navigation in VR","authors":"Daniel Medeiros, R. K. D. Anjos, Daniel Mendes, J. Pereira, A. Raposo, Joaquim Jorge","doi":"10.1145/3281505.3281511","DOIUrl":"https://doi.org/10.1145/3281505.3281511","url":null,"abstract":"Head-Mounted Displays are useful to place users in virtual reality (VR). They do this by totally occluding the physical world, including users' bodies. This can make self-awareness problematic. Indeed, researchers have shown that users' feeling of presence and spatial awareness are highly influenced by their virtual representations, and that self-embodied representations (avatars) of their anatomy can make the experience more engaging. On the other hand, recent user studies show a penchant towards a third-person view of one's own body to seemingly improve spatial awareness. However, due to its unnaturality, we argue that a third-person perspective is not as effective or convenient as a first-person view for task execution in VR. In this paper, we investigate, through a user evaluation, how these perspectives affect task performance and embodiment, focusing on navigation tasks, namely walking while avoiding obstacles. For each perspective, we also compare three different levels of realism for users' representation, specifically a stylized abstract avatar, a mesh-based generic human, and a real-time point-cloud rendering of the users' own body. Our results show that only when a third-person perspective is coupled with a realistic representation, a similar sense of embodiment and spatial awareness is felt. In all other cases, a first-person perspective is still better suited for navigation tasks, regardless of representation.","PeriodicalId":138249,"journal":{"name":"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124129973","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}
Recently, VR technology is rapidly developing and attracting public attention. However, VR Sickness is a problem that is still not solved in the VR experience. The VR sickness is presumed to be caused by crosstalk between sensory and cognitive systems [1]. However, since there is no objective way to measure sensory and cognitive systems, it is difficult to measure VR sickness. In this paper, we collect EEG data while participants experience VR videos. We propose a Deep Neural Network (DNN) deep learning algorithm by measuring VR sickness through electroencephalogram (EEG) data. Experiments have been conducted to search for an appropriate EEG data preprocessing method and DNN structure suitable for the deep learning, and the accuracy of 99.12% is obtained in our study.
{"title":"VR sickness measurement with EEG using DNN algorithm","authors":"D. Jeong, Sangbong Yoo, Yun Jang","doi":"10.1145/3281505.3283387","DOIUrl":"https://doi.org/10.1145/3281505.3283387","url":null,"abstract":"Recently, VR technology is rapidly developing and attracting public attention. However, VR Sickness is a problem that is still not solved in the VR experience. The VR sickness is presumed to be caused by crosstalk between sensory and cognitive systems [1]. However, since there is no objective way to measure sensory and cognitive systems, it is difficult to measure VR sickness. In this paper, we collect EEG data while participants experience VR videos. We propose a Deep Neural Network (DNN) deep learning algorithm by measuring VR sickness through electroencephalogram (EEG) data. Experiments have been conducted to search for an appropriate EEG data preprocessing method and DNN structure suitable for the deep learning, and the accuracy of 99.12% is obtained in our study.","PeriodicalId":138249,"journal":{"name":"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126123766","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}
There are real-time training systems to learn the correct golf swing form by providing visual feedback to the users. However, real-time visual feedback requires the users to see the display during their motion that leads to the wrong posture. This paper proposed a real-time golf-swing training system using sonification and sound image localization. The system provides real-time audio feedback based on the difference between the pre-recorded model data and real-time user data, which consists of the roll, pitch, and yaw angles of a golf club shaft. The system also used sound image localization so that the user can hear the audio feedback from the direction of the club head. The user can recognize the current posture of the club without moving their gaze.
{"title":"A real-time golf-swing training system using sonification and sound image localization","authors":"Yuka Tanaka, Homare Kon, H. Koike","doi":"10.1145/3281505.3281604","DOIUrl":"https://doi.org/10.1145/3281505.3281604","url":null,"abstract":"There are real-time training systems to learn the correct golf swing form by providing visual feedback to the users. However, real-time visual feedback requires the users to see the display during their motion that leads to the wrong posture. This paper proposed a real-time golf-swing training system using sonification and sound image localization. The system provides real-time audio feedback based on the difference between the pre-recorded model data and real-time user data, which consists of the roll, pitch, and yaw angles of a golf club shaft. The system also used sound image localization so that the user can hear the audio feedback from the direction of the club head. The user can recognize the current posture of the club without moving their gaze.","PeriodicalId":138249,"journal":{"name":"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129805925","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}
Augmented Reality (AR) provides real-time information by superimposing virtual information onto users' view of the real world. Our work is the first to explore how peripheral vision, instead of central vision, can be used to read text on AR and smart glasses. We present PeriTextAR, a multiword reading interface using rapid serial visual presentation (RSVP)[5]. This enables users to observe the real world using central vision, while using peripheral vision to read virtual information. We first conducted a lab-based study to determine the effect of different text transformation by comparing reading efficiency among 3 capitalization schemes, 2 font faces, 2 text animation methods, and 3 different numbers of words for RSVP paradigm. Another lab-based study followed, investigating the performance of the PeriTextAR against control text, and the results showed significant better performance.
{"title":"PeriTextAR: utilizing peripheral vision for reading text on augmented reality smart glasses","authors":"Yu-Chih Lin, L. Hsu, Mike Y. Chen","doi":"10.1145/3281505.3284396","DOIUrl":"https://doi.org/10.1145/3281505.3284396","url":null,"abstract":"Augmented Reality (AR) provides real-time information by superimposing virtual information onto users' view of the real world. Our work is the first to explore how peripheral vision, instead of central vision, can be used to read text on AR and smart glasses. We present PeriTextAR, a multiword reading interface using rapid serial visual presentation (RSVP)[5]. This enables users to observe the real world using central vision, while using peripheral vision to read virtual information. We first conducted a lab-based study to determine the effect of different text transformation by comparing reading efficiency among 3 capitalization schemes, 2 font faces, 2 text animation methods, and 3 different numbers of words for RSVP paradigm. Another lab-based study followed, investigating the performance of the PeriTextAR against control text, and the results showed significant better performance.","PeriodicalId":138249,"journal":{"name":"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115845370","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}
Katherine Tarre, Adam S. Williams, Lukas Borges, N. Rishe, A. Barreto, F. Ortega
Understanding gaming expertise is important in user studies. We present a study comprised of 60 participants playing a First Person Shooter Game (Counter-Strike: Global Offensive). This study provides results related to a keyboard model used to determine an objective measurement of gamers' skill. We also show that there is no correlation between frequency questionnaires and user skill.
{"title":"Towards first person gamer modeling and the problem with game classification in user studies","authors":"Katherine Tarre, Adam S. Williams, Lukas Borges, N. Rishe, A. Barreto, F. Ortega","doi":"10.1145/3281505.3281590","DOIUrl":"https://doi.org/10.1145/3281505.3281590","url":null,"abstract":"Understanding gaming expertise is important in user studies. We present a study comprised of 60 participants playing a First Person Shooter Game (Counter-Strike: Global Offensive). This study provides results related to a keyboard model used to determine an objective measurement of gamers' skill. We also show that there is no correlation between frequency questionnaires and user skill.","PeriodicalId":138249,"journal":{"name":"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116223126","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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