This article discusses the depth range which automultiscopic 3D (A3D) displays should reproduce for ensuring an adequate perceptual quality of substantially deep scenes. These displays usually need sufficient depth reconstruction capabilities covering the whole scene depth, but due to the inherent hardware restriction of these displays this is often difficult, particularly for showing deep scenes. Previous studies have addressed this limitation by introducing depth compression that contracts the scene depth into a smaller depth range by modifying the scene geometry, assuming that the scenes were represented as CG data. The previous results showed that reconstructing only a physical depth of 1 m is needed to show scenes with much deeper depth and without large perceptual quality degradation. However, reconstructing a depth of 1 m is still challenging for actual A3D displays. In this study, focusing on a personal viewing situation, we introduce a dynamic depth compression that combines viewpoint tracking with the previous approach and examines the extent to which scene depths can be compressed while keeping the original perceptual quality. Taking into account the viewer's viewpoint movements, which were considered a cause of unnaturalness in the previous approach, we performed an experiment with an A3D display simulator and found that a depth of just 10 cm was sufficient for showing deep scenes without inducing a feeling of unnaturalness. Next, we investigated whether the simulation results were valid even on a real A3D display and found that the dynamic approach induced better perceptual quality than the static one even on the real A3D display and that it had a depth enhancing effect without any hardware updates. These results suggest that providing a physical depth of 10 cm on personalized A3D displays is general enough for showing any deeper 3D scenes with appealing subjective quality.

This paper describes how the screen size of 3D displays affect the subjective impressions of 3D-visualized content. The key requirement for 3D displays is the presentation of depth cues comprising binocular disparities and/or motion parallax; however, the development of displays and production of content that include these cues leads to an increase in costs. Given the variety of screen sizes, it is expected that 3D characteristics are experienced differently by viewers depending on the screen size. We asked 48 participants to evaluate the 3D experience when using three different-sized stereoscopic displays (11.5, 55, and 200 inches) with head trackers. The participants were asked to score presented stimuli on 20 opposite-term pairs based on the semantic differential method after viewing each of six stimuli. Using factor analysis, we extracted three principal factors: power , related to strong three-dimensionality, real, etc.; visibility , related to stable, natural, etc.; and space , related to agile, open, etc., which had proportions of variances of 0.317, 0.277, and 0.251, respectively; their cumulation was 0.844. We confirmed that the three different-sized displays did not produce the same subjective impressions of the 3D characteristics. In particular, on the small-sized display, we found larger effects on power and space impressions from motion parallax (η 2 = 0.133 and 0.161, respectively) than for the other two sizes. We found degradation of the visibility impressions from binocular disparities, which might be caused by artifacts from stereoscopy. The effects of 3D viewing on subjective impression depends on the display size, and small-sized displays offer the largest benefits by adding 3D characteristics to 2D visualization.

A key challenge for virtual reality level designers is striking a balance between maintaining the immersiveness of VR and providing users with on-screen aids after designing a virtual experience. These aids are often necessary for wayfinding in virtual environments with complex paths. We introduce a novel adaptive aid that maintains the effectiveness of traditional aids, while equipping designers and users with the controls of how often help is displayed. Our adaptive aid uses gaze patterns in predicting user's need for navigation aid in VR and displays mini-maps or arrows accordingly. Using a dataset of gaze angle sequences of users navigating a VR environment and markers of when users requested aid, we trained an LSTM to classify user's gaze sequences as needing navigation help and display an aid. We validated the efficacy of the adaptive aid for wayfinding compared to other commonly-used wayfinding aids.

Eye-tracking enables researchers to conduct complex analysis on human behavior. With the recent introduction of eye-tracking into consumer-grade virtual reality headsets, the barrier of entry to visual attention analysis in virtual environments has been lowered significantly. Whether for arranging artwork in a virtual museum, posting banners for virtual events or placing advertisements in virtual worlds, analyzing visual attention patterns provides a powerful means for guiding visual element placement. In this work, we propose a novel data-driven optimization approach for automatically analyzing visual attention and placing visual elements in 3D virtual environments. Using an eye-tracking virtual reality headset, we collect eye-tracking data which we use to train a regression model for predicting gaze duration. We then use the predicted gaze duration output of our regressors to optimize the placement of visual elements with respect to certain visual attention and design goals. Through experiments in several virtual environments, we demonstrate the effectiveness of our optimization approach for predicting gaze duration and for placing visual elements in different practical scenarios. Our approach is implemented as a useful plug-in that level designers can use to automatically populate visual elements in 3D virtual environments.

Digitization of lengthy personal experiences would be made possible by constant recording using wearable video cameras. It is conceivable that the resulting amount of video content would be extraordinarily large. In order to retrieve and browse the desired scenes, a vast amount of video would need to be organized with structural information. In this paper, we attempt to develop a "wearable imaging system" that is capable of constantly capturing data, not only from a wearable video camera, but also from various kinds of sensors, such as a GPS, an accelerometer and a gyro sensor. The data from these sensors are appropriately extracted and processed by hidden Markov model (HMM) to achieve efficient video retrieval and browsing.

Auto-summarization of video contents has become an important topic following the growing amount of multimedia contents. Researches in this area have shown the effectiveness of low-level video and audio features in categorizing video contents. The use of brainwaves to reflect personal interests is also proven to be practical. In this paper, we model the relationship between audio/video features and brainwaves (/spl alpha/-waves) using the support vector machine (SVM). Based on the SVM model, we summarized wearable videos by personal interests, using only low-level video and audio features. Here we define "wearable videos" as continuous recordings of personal experiences using wearable video camera and computer. Our experiment results showed over 90% of accuracy on summarization of a 25-minute video clip with an SVM model created by another resembling 25-minute video clip.