9 research outputs found
Effects of virtual acoustics on dynamic auditory distance perception
Sound propagation encompasses various acoustic phenomena including
reverberation. Current virtual acoustic methods, ranging from parametric
filters to physically-accurate solvers, can simulate reverberation with varying
degrees of fidelity. We investigate the effects of reverberant sounds generated
using different propagation algorithms on acoustic distance perception, i.e.,
how faraway humans perceive a sound source. In particular, we evaluate two
classes of methods for real-time sound propagation in dynamic scenes based on
parametric filters and ray tracing. Our study shows that the more accurate
method shows less distance compression as compared to the approximate,
filter-based method. This suggests that accurate reverberation in VR results in
a better reproduction of acoustic distances. We also quantify the levels of
distance compression introduced by different propagation methods in a virtual
environment.Comment: 8 Pages, 7 figure
An Overview of Enhancing Distance Learning Through Augmented and Virtual Reality Technologies
Although distance learning presents a number of interesting educational
advantages as compared to in-person instruction, it is not without its
downsides. We first assess the educational challenges presented by distance
learning as a whole, and identify 4 main challenges that distance learning
currently presents as compared to in-person instruction: the lack of social
interaction, reduced student engagement and focus, reduced comprehension and
information retention, and the lack of flexible and customizable instructor
resources. After assessing each of these challenges in-depth, we examine how
AR/VR technologies might serve to address each challenge along with their
current shortcomings, and finally outline the further research that is required
to fully understand the potential of AR/VR technologies as they apply to
distance learning.Comment: 12 pages, 7 figures, submitted to TVC
Towards Fully Mobile 3D Face, Body, and Environment Capture Using Only Head-worn Cameras
We propose a new approach for 3D reconstruction of dynamic indoor and outdoor scenes in everyday environments, leveraging only cameras worn by a user. This approach allows 3D reconstruction of experiences at any location and virtual tours from anywhere. The key innovation of the proposed ego-centric reconstruction system is to capture the wearer's body pose and facial expression from near-body views, e.g. cameras on the user's glasses, and to capture the surrounding environment using outward-facing views. The main challenge of the ego-centric reconstruction, however, is the poor coverage of the near-body views - that is, the user's body and face are observed from vantage points that are convenient for wear but inconvenient for capture. To overcome these challenges, we propose a parametric-model-based approach to user motion estimation. This approach utilizes convolutional neural networks (CNNs) for near-view body pose estimation, and we introduce a CNN-based approach for facial expression estimation that combines audio and video. For each time-point during capture, the intermediate model-based reconstructions from these systems are used to re-target a high-fidelity pre-scanned model of the user. We demonstrate that the proposed self-sufficient, head-worn capture system is capable of reconstructing the wearer's movements and their surrounding environment in both indoor and outdoor situations without any additional views. As a proof of concept, we show how the resulting 3D-plus-time reconstruction can be immersively experienced within a virtual reality system (e.g., the HTC Vive). We expect that the size of the proposed egocentric capture-and-reconstruction system will eventually be reduced to fit within future AR glasses, and will be widely useful for immersive 3D telepresence, virtual tours, and general use-anywhere 3D content creation