Aberration pre-correction for a simple optical system of HMDs.

Head-mounted displays (HMDs) are becoming increasingly popular as a crucial component of virtual reality (VR). However, contemporary HMDs enforce a simple optical structure due to their constrained form factor, which impedes the use of multiple lens elements that can reduce aberrations in general. As a result, they introduce severe aberrations and imperfections in optical imagery, causing visual fatigue and degrading the immersive experience of being present in VR. To address this issue without modifying the hardware system, we present a novel, to the best of our knowledge, software-driven approach that compensates for the aberrations in HMDs in real time. Our approach involves pre-correction that deconvolves an input image to minimize the difference between its after-lens image and the ideal image. We characterize the specific wavefront aberration and point spread function (PSF) of the optical system using Zernike polynomials. To achieve higher computational efficiency, we improve the conventional deconvolution based on hyper-Laplacian prior by adopting a regularization constraint term based on L2 optimization and the input-image gradient. Furthermore, we implement our solution entirely on a graphics processing unit (GPU) to ensure constant and scalable real-time performance for interactive VR. Our experiments evaluating our algorithm demonstrate that our solution can reliably reduce the aberration of the after-lens images in real time.

Prediction of Specific Anxiety Symptoms and Virtual Reality Sickness Using In Situ Autonomic Physiological Signals During Virtual Reality Treatment in Patients With Social Anxiety Disorder: Mixed Methods Study.

BACKGROUND: Social anxiety disorder (SAD) is the fear of social situations where a person anticipates being evaluated negatively. Changes in autonomic response patterns are related to the expression of anxiety symptoms. Virtual reality (VR) sickness can inhibit VR experiences. OBJECTIVE: This study aimed to predict the severity of specific anxiety symptoms and VR sickness in patients with SAD, using machine learning based on in situ autonomic physiological signals (heart rate and galvanic skin response) during VR treatment sessions. METHODS: This study included 32 participants with SAD taking part in 6 VR sessions. During each VR session, the heart rate and galvanic skin response of all participants were measured in real time. We assessed specific anxiety symptoms using the Internalized Shame Scale (ISS) and the Post-Event Rumination Scale (PERS), and VR sickness using the Simulator Sickness Questionnaire (SSQ) during 4 VR sessions (#1, #2, #4, and #6). Logistic regression, random forest, and naïve Bayes classification classified and predicted the severity groups in the ISS, PERS, and SSQ subdomains based on in situ autonomic physiological signal data. RESULTS: The severity of SAD was predicted with 3 machine learning models. According to the F1 score, the highest prediction performance among each domain for severity was determined. The F1 score of the ISS mistake anxiety subdomain was 0.8421 using the logistic regression model, that of the PERS positive subdomain was 0.7619 using the naïve Bayes classifier, and that of total VR sickness was 0.7059 using the random forest model. CONCLUSIONS: This study could predict specific anxiety symptoms and VR sickness during VR intervention by autonomic physiological signals alone in real time. Machine learning models can predict the severe and nonsevere psychological states of individuals based on in situ physiological signal data during VR interventions for real-time interactive services. These models can support the diagnosis of specific anxiety symptoms and VR sickness with minimal participant bias. TRIAL REGISTRATION: Clinical Research Information Service KCT0003854; https://cris.nih.go.kr/cris/search/detailSearch.do/13508.

Real-Time Dynamic Bokeh Rendering With Efficient Look-Up Table Sampling.

This article presents a real-time bokeh rendering technique that splats pre-computed sprites but takes dynamic visibilities and intrinsic appearances into account at runtime. To attain alias-free looks without excessive sampling on a lens, the visibilities of strong highlights are densely sampled using rasterization, while regular objects are sparsely sampled using conventional defocus-blur rendering. The intrinsic appearance is dynamically transformed from a precomputed look-up table, which encodes radial aberrations against image distances in a compact 2D texture. Our solution can render complex bokeh effects without undersampling artifacts in real time, and greatly improve the photorealism of defocus-blur rendering.

Virtual Reality-Based Psychotherapy in Social Anxiety Disorder: fMRI Study Using a Self-Referential Task.

BACKGROUND: Although it has been well demonstrated that the efficacy of virtual reality therapy for social anxiety disorder is comparable to that of traditional cognitive behavioral therapy, little is known about the effect of virtual reality on pathological self-referential processes in individuals with social anxiety disorder. OBJECTIVE: We aimed to determine changes in self-referential processing and their neural mechanisms following virtual reality treatment. METHODS: We recruited participants with and without a primary diagnosis of social anxiety disorder to undergo clinical assessments (Social Phobia Scale and Post-Event Rumination Scale) and functional magnetic resonance imaging (fMRI) scans. Participants with social anxiety disorder received virtual reality-based exposure treatment for 6 sessions starting immediately after baseline testing. After the sixth session, participants with social anxiety disorder completed follow-up scans during which they were asked to judge whether a series of words (positive, negative, neutral) was relevant to them. RESULTS: Of 25 individuals with social anxiety disorder who participated in the study, 21 completed the sessions and follow-up; 22 control individuals also participated. There were no significant differences in age (P=.36), sex (P=.71), or handedness (P=.51) between the groups. Whole-brain analysis revealed that participants in the social anxiety disorder group had increased neural responses during positive self-referential processing in the medial temporal and frontal cortexes compared with those in the control group. Participants in the social anxiety disorder group also showed increased left insular activation and decreased right middle frontal gyrus activation during negative self-referential processing. After undergoing virtual reality-based therapy, overall symptoms of the participants with social anxiety disorder were reduced, and these participants exhibited greater activity in a brain regions responsible for self-referential and autobiographical memory processes while viewing positive words during postintervention fMRI scans. Interestingly, the greater the blood oxygen level dependent changes related to positive self-referential processing, the lower the tendency to ruminate on the negative events and the lower the social anxiety following the virtual reality session. Compared with that at baseline, higher activation was also found within broad somatosensory areas in individuals with social anxiety disorder during negative self-referential processing following virtual reality therapy. CONCLUSIONS: These fMRI findings might reflect the enhanced physiological and cognitive processing in individuals with social anxiety disorder in response to self-referential information. They also provide neural evidence of the effect of virtual reality exposure therapy on social anxiety and self-derogation.

Effectiveness of a Participatory and Interactive Virtual Reality Intervention in Patients With Social Anxiety Disorder: Longitudinal Questionnaire Study.

BACKGROUND: Social anxiety disorder (SAD) is characterized by excessive fear of negative evaluation and humiliation in social interactions and situations. Virtual reality (VR) treatment is a promising intervention option for SAD. OBJECTIVE: The purpose of this study was to create a participatory and interactive VR intervention for SAD. Treatment progress, including the severity of symptoms and the cognitive and emotional aspects of SAD, was analyzed to evaluate the effectiveness of the intervention. METHODS: In total, 32 individuals with SAD and 34 healthy control participants were enrolled in the study through advertisements for online bulletin boards at universities. A VR intervention was designed consisting of three stages (introduction, core, and finishing) and three difficulty levels (easy, medium, and hard) that could be selected by the participants. The core stage was the exposure intervention in which participants engaged in social situations. The effectiveness of treatment was assessed through Beck Anxiety inventory (BAI), State-Trait Anxiety Inventory (STAI), Internalized Shame Scale (ISS), Post-Event Rumination Scale (PERS), Social Phobia Scale (SPS), Social Interaction Anxiety Scale (SIAS), Brief-Fear of Negative Evaluation Scale (BFNE), and Liebowitz Social Anxiety Scale (LSAS). RESULTS: In the SAD group, scores on the BAI (F=4.616, P=.009), STAI-Trait (F=4.670, P=.004), ISS (F=6.924, P=.001), PERS-negative (F=1.008, P<.001), SPS (F=8.456, P<.001), BFNE (F=6.117, P=.004), KSAD (F=13.259, P<.001), and LSAS (F=4.103, P=.009) significantly improved over the treatment process. Compared with the healthy control group before treatment, the SAD group showed significantly higher scores on all scales (P<.001), and these significant differences persisted even after treatment (P<.001). In the comparison between the VR treatment responder and nonresponder subgroups, there was no significant difference across the course of the VR session. CONCLUSIONS: These findings indicated that a participatory and interactive VR intervention had a significant effect on alleviation of the clinical symptoms of SAD, confirming the usefulness of VR for the treatment of SAD. VR treatment is expected to be one of various beneficial therapeutic approaches in the future. TRIAL REGISTRATION: Clinical Research Information Service (CRIS) KCT0003854; https://cris.nih.go.kr/cris/search/search_result_st01.jsp?seq=13508.

Visuo-Haptic-Based Multimodal Feedback Virtual Reality Solution to Improve Anxiety Symptoms: A Proof-of-Concept Study.

With proper guidance, virtual reality (VR) can provide psychiatric therapeutic strategies within a simulated environment. The visuo-haptic-based multimodal feedback VR solution has been developed to improve anxiety symptoms through immersive experience and feedback. A proof-of-concept study was performed to investigate this VR solution. Nine subjects recently diagnosed with panic disorder were recruited, and seven of them eventually completed the trial. Two VR sessions were provided to each subject. Depression, anxiety, and VR sickness were evaluated before and after each session. Although there was no significant effect of the VR sessions on psychiatric symptoms, we could observe a trend of improvement in depression, anxiety, and VR sickness. The VR solution was effective in relieving subjective anxiety, especially in panic disorder without comorbidity. VR sickness decreased over time. This study is a new proof-of-concept trial to evaluate the therapeutic effect of VR solutions on anxiety symptoms using visuo-haptic-based multimodal feedback simultaneously.

Saliency-driven real-time video-to-tactile translation.

Tactile feedback coordinated with visual stimuli has proven its worth in mediating immersive multimodal experiences, yet its authoring has relied on content artists. This article presents a fully automated framework of generating tactile cues from streaming images to provide synchronized visuotactile stimuli in real time. The spatiotemporal features of video images are analyzed on the basis of visual saliency and then mapped into the tactile cues that are rendered on tactors installed on a chair. We also conducted two user experiments for performance evaluation. The first experiment investigated the effects of visuotactile rendering against visual-only rendering, demonstrating that the visuotactile rendering improved the movie watching experience to be more interesting, immersive, and understandable. The second experiment was performed to compare the effectiveness of authoring methods and found that the automated authoring approach, used with care, can produce plausible tactile effects similar in quality to manual authoring.

Perceptually driven visibility optimization for categorical data visualization.

Visualization techniques often use color to present categorical differences to a user. When selecting a color palette, the perceptual qualities of color need careful consideration. Large coherent groups visually suppress smaller groups and are often visually dominant in images. This paper introduces the concept of class visibility used to quantitatively measure the utility of a color palette to present coherent categorical structure to the user. We present a color optimization algorithm based on our class visibility metric to make categorical differences clearly visible to the user. We performed two user experiments on user preference and visual search to validate our visibility measure over a range of color palettes. The results indicate that visibility is a robust measure, and our color optimization can increase the effectiveness of categorical data visualizations.

Real-time depth-of-field rendering using anisotropically filtered mipmap interpolation.

This article presents a real-time GPU-based post-filtering method for rendering acceptable depth-of-field effects suited for virtual reality. Blurring is achieved by nonlinearly interpolating mipmap images generated from a pinhole image. Major artifacts common in the post-filtering techniques such as bilinear magnification artifact, intensity leakage, and blurring discontinuity are practically eliminated via magnification with a circular filter, anisotropic mipmapping, and smoothing of blurring degrees. The whole framework is accelerated using GPU programs for constant and scalable real-time performance required for virtual reality. We also compare our method to recent GPU-based methods in terms of image quality and rendering performance.

Real-time tracking of visually attended objects in virtual environments and its application to LOD.

This paper presents a real-time framework for computationally tracking objects visually attended by the user while navigating in interactive virtual environments. In addition to the conventional bottom-up (stimulus-driven) saliency map, the proposed framework uses top-down (goal-directed) contexts inferred from the user's spatial and temporal behaviors, and identifies the most plausibly attended objects among candidates in the object saliency map. The computational framework was implemented using GPU, exhibiting high computational performance adequate for interactive virtual environments. A user experiment was also conducted to evaluate the prediction accuracy of the tracking framework by comparing objects regarded as visually attended by the framework to actual human gaze collected with an eye tracker. The results indicated that the accuracy was in the level well supported by the theory of human cognition for visually identifying single and multiple attentive targets, especially owing to the addition of top-down contextual information. Finally, we demonstrate how the visual attention tracking framework can be applied to managing the level of details in virtual environments, without any hardware for head or eye tracking.

High-yield sol-gel synthesis of well-dispersed, colorless ZrO(2) nanocrystals.

A 93% high-yield synthesis of well-dispersed, colorless zirconium dioxide (ZrO(2)) nanocrystals is reported. In this synthesis, hydrolysis and condensation reactions of zirconium(IV) tert-butoxide in the presence of oleic acid (100 degrees C) formed ZrO(2) precursors. The ZrO(2) precursors were made of -Zr-O-Zr- networks surrounded by oleic acid molecules. Annealing (280 degrees C) the precursors dispersed in dioctyl ether caused crystallization of the -Zr-O-Zr- networks, thereby generating 4 nm ZrO(2) nanocrystals stabilized with oleic acid. The particles were highly crystalline and tetragonal phase. A dense ZrO(2) nanocrystal dispersion in toluene (280 mg/mL) showed a transmittance of about 90% (3 mm optical path length) in the whole visible region.