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.

As 360 deg cameras and virtual reality headsets become more popular, panorama images have become increasingly ubiquitous. While sounds are essential in delivering immersive and interactive user experiences, most panorama images, however, do not come with native audio. In this paper, we propose an automatic algorithm to augment static panorama images through realistic audio assignment. We accomplish this goal through object detection, scene classification, object depth estimation, and audio source placement. We built an audio file database composed of over $500$ audio files to facilitate this process. We designed and conducted a user study to verify the efficacy of various components in our pipeline. We run our method on a large variety of panorama images of indoor and outdoor scenes. By analyzing the statistics, we learned the relative importance of these components, which can be used in prioritizing for power-sensitive time-critical tasks like mobile augmented reality (AR) applications.

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.