One of the challenging tasks in virtual scene design for Virtual Reality (VR) is causing it to invoke a particular mood in viewers. The subjective nature of moods brings uncertainty to the purpose. We propose a novel approach to automatic adjustment of the colors of textures for objects in a virtual indoor scene, enabling it to match a target mood. A dataset of 25,000 images, including building/home interiors, was used to train a classifier with the features extracted via deep learning. It contributes to an optimization process that colorizes virtual scenes automatically according to the target mood. Our approach was tested on four different indoor scenes, and we conducted a user study demonstrating its efficacy through statistical analysis with the focus on the impact of the scenes experienced with a VR headset.

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.

Wayfinding signs play an important role in guiding users to navigate in a virtual environment and in helping pedestrians to find their ways in a real-world architectural site. Conventionally, the wayfinding design of a virtual environment is created manually, so as the wayfinding design of a real-world architectural site. The many possible navigation scenarios, as well as the interplay between signs and human navigation, can make the manual design process overwhelming and non-trivial. As a result, creating a wayfinding design for a typical layout can take months to several years. In this paper, we introduce the Way to Go! approach for automatically generating a wayfinding design for a given layout. The designer simply has to specify some navigation scenarios; our approach will automatically generate an optimized wayfinding design with signs properly placed considering human agents' visibility and possibility of making mistakes during a navigation. We demonstrate the effectiveness of our approach in generating wayfinding designs for different layouts such as a train station, a downtown and a canyon. We evaluate our results by comparing different wayfinding designs and show that our optimized wayfinding design can guide pedestrians to their destinations effectively and efficiently. Our approach can also help the designer visualize the accessibility of a destination from different locations, and correct any "blind zone" with additional signs.

Existing literature reveals that during reading, gaze fixations within a word are not necessarily close to the optimal reading position. The optimal position for reading represents the gaze fixation near the center of each word for which recognition time is minimal. In our study, we examined the match between optimal position and initial fixation position, which has not been studied before. We did this in conventional reading and in two text-viewing tasks. For the text-viewing tasks, we employed multiple words that were non-isolated in the presentation but could be examined individually in rhythm and linguistic context. We discovered that for the text-viewing tasks, the initial fixation position tends to be close to the optimal position, which was in contrast to the conventional reading task. This finding will help us understand the relationship between optimal position and reading context and inform the development of new reading applications.

The Enhanced Restricted Focus Viewer (ERFV) is a unique software tool for tracking the visual attention of users in hyperlinked environments, such as Web sites. The software collects data, such as mouse clicks along with the path of users' visual attention, as they browse a site. Unlike traditional eye-tracking procedures, the ERFV requires no hardware to operate other than a personal computer. In addition to time and cost savings, the ERFV allows the administration of usability testing to groups of participants simultaneously. A laboratory test comparing the ERFV to a hardware-based eye-tracking system showed that the two methods compared favorably in terms of how well they track a user's visual attention. The value of the ERFV as a usability testing tool was demonstrated through an experiment that evaluated two Web sites that were equivalent in content but that differed in terms of design. Although several open issues concerning the ERFV still remain, some of these issues are being addressed through ongoing research efforts.