Using CNNs For Users Segmentation In Video See-Through Augmented Virtuality
This work addresses the challenge of improving user experience in augmented virtuality simulations, but it appears incremental as it applies existing deep learning techniques to a specific scenario.
The paper tackled the problem of integrating users' bodies into video see-through augmented virtuality to enhance presence and performance, proposing a convolutional neural network for real-time semantic segmentation of bodies in stereoscopic RGB video streams and demonstrating its feasibility.
In this paper, we present preliminary results on the use of deep learning techniques to integrate the users self-body and other participants into a head-mounted video see-through augmented virtuality scenario. It has been previously shown that seeing users bodies in such simulations may improve the feeling of both self and social presence in the virtual environment, as well as user performance. We propose to use a convolutional neural network for real time semantic segmentation of users bodies in the stereoscopic RGB video streams acquired from the perspective of the user. We describe design issues as well as implementation details of the system and demonstrate the feasibility of using such neural networks for merging users bodies in an augmented virtuality simulation.