Using CNNs For Users Segmentation In Video See-Through Augmented Virtuality

TL;DR

This paper explores using convolutional neural networks to segment users' bodies in real-time stereo video streams for improved augmented virtuality experiences, enhancing presence and social interaction.

Contribution

It introduces a CNN-based approach for real-time user body segmentation in stereo video streams within augmented virtuality environments, demonstrating system feasibility.

Findings

Feasibility of CNN-based real-time body segmentation shown.

Improved user presence and social interaction potential.

System implementation details provided.

Abstract

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.