509 research outputs found
Unmasking Communication Partners: A Low-Cost AI Solution for Digitally Removing Head-Mounted Displays in VR-Based Telepresence
Face-to-face conversation in Virtual Reality (VR) is a challenge when
participants wear head-mounted displays (HMD). A significant portion of a
participant's face is hidden and facial expressions are difficult to perceive.
Past research has shown that high-fidelity face reconstruction with personal
avatars in VR is possible under laboratory conditions with high-cost hardware.
In this paper, we propose one of the first low-cost systems for this task which
uses only open source, free software and affordable hardware. Our approach is
to track the user's face underneath the HMD utilizing a Convolutional Neural
Network (CNN) and generate corresponding expressions with Generative
Adversarial Networks (GAN) for producing RGBD images of the person's face. We
use commodity hardware with low-cost extensions such as 3D-printed mounts and
miniature cameras. Our approach learns end-to-end without manual intervention,
runs in real time, and can be trained and executed on an ordinary gaming
computer. We report evaluation results showing that our low-cost system does
not achieve the same fidelity of research prototypes using high-end hardware
and closed source software, but it is capable of creating individual facial
avatars with person-specific characteristics in movements and expressions.Comment: 9 pages, IEEE 3rd International Conference on Artificial Intelligence
& Virtual Realit
RGB-D-based Action Recognition Datasets: A Survey
Human action recognition from RGB-D (Red, Green, Blue and Depth) data has
attracted increasing attention since the first work reported in 2010. Over this
period, many benchmark datasets have been created to facilitate the development
and evaluation of new algorithms. This raises the question of which dataset to
select and how to use it in providing a fair and objective comparative
evaluation against state-of-the-art methods. To address this issue, this paper
provides a comprehensive review of the most commonly used action recognition
related RGB-D video datasets, including 27 single-view datasets, 10 multi-view
datasets, and 7 multi-person datasets. The detailed information and analysis of
these datasets is a useful resource in guiding insightful selection of datasets
for future research. In addition, the issues with current algorithm evaluation
vis-\'{a}-vis limitations of the available datasets and evaluation protocols
are also highlighted; resulting in a number of recommendations for collection
of new datasets and use of evaluation protocols
Distributed human 3D pose estimation and action recognition.
In this paper, we propose a distributed solution for3D human pose estimation using a RGBD camera network. Thekey feature of our method is a dynamic hybrid consensus filter(DHCF) is introduced to fuse the multiple view informationof cameras. In contrast to the centralized fusion solution,the DHCF algorithm can be used in a distributed network,which requires no central information fusion center. Therefore,the DHCF based fusion algorithm can benefit from manyadvantages of distributed network. We also show that theproposed fusion algorithm can handle the occlusion problemseffectively, and achieve higher action recognition rate comparedto the ones using only single view information
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