Modeling the Impact of Head-Body Rotations on Audio-Visual Spatial Perception for Virtual Reality Applications.

Humans perceive the world by integrating multimodal sensory feedback, including visual and auditory stimuli, which holds true in virtual reality (VR) environments. Proper synchronization of these stimuli is crucial for perceiving a coherent and immersive VR experience. In this work, we focus on the interplay between audio and vision during localization tasks involving natural head-body rotations. We explore the impact of audio-visual offsets and rotation velocities on users' directional localization acuity for various viewing modes. Using psychometric functions, we model perceptual disparities between visual and auditory cues and determine offset detection thresholds. Our findings reveal that target localization accuracy is affected by perceptual audio-visual disparities during head-body rotations, but remains consistent in the absence of stimuli-head relative motion. We then showcase the effectiveness of our approach in predicting and enhancing users' localization accuracy within realistic VR gaming applications. To provide additional support for our findings, we implement a natural VR game wherein we apply a compensatory audio-visual offset derived from our measured psychometric functions. As a result, we demonstrate a substantial improvement of up to 40% in participants' target localization accuracy. We additionally provide guidelines for content creation to ensure coherent and seamless VR experiences.

D-SAV360: A Dataset of Gaze Scanpaths on 360° Ambisonic Videos.

Understanding human visual behavior within virtual reality environments is crucial to fully leverage their potential. While previous research has provided rich visual data from human observers, existing gaze datasets often suffer from the absence of multimodal stimuli. Moreover, no dataset has yet gathered eye gaze trajectories (i.e., scanpaths) for dynamic content with directional ambisonic sound, which is a critical aspect of sound perception by humans. To address this gap, we introduce D-SAV360, a dataset of 4,609 head and eye scanpaths for 360° videos with first-order ambisonics. This dataset enables a more comprehensive study of multimodal interaction on visual behavior in virtual reality environments. We analyze our collected scanpaths from a total of 87 participants viewing 85 different videos and show that various factors such as viewing mode, content type, and gender significantly impact eye movement statistics. We demonstrate the potential of D-SAV360 as a benchmarking resource for state-of-the-art attention prediction models and discuss its possible applications in further research. By providing a comprehensive dataset of eye movement data for dynamic, multimodal virtual environments, our work can facilitate future investigations of visual behavior and attention in virtual reality.