Exploring Upper Limb Segmentation with Deep Learning for Augmented Virtuality

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dc.contributor.authorGruosso, Monicaen_US
dc.contributor.authorCapece, Nicolaen_US
dc.contributor.authorErra, Ugoen_US
dc.contributor.editorFrosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanueleen_US
dc.date.accessioned2021-10-25T11:53:40Z
dc.date.available2021-10-25T11:53:40Z
dc.date.issued2021
dc.description.abstractSense of presence, immersion, and body ownership are among the main challenges concerning Virtual Reality (VR) and freehand-based interaction methods. Through specific hand tracking devices, freehand-based methods can allow users to use their hands for VE interaction. To visualize and make easy the freehand methods, recent approaches take advantage of 3D meshes to represent the user's hands in VE. However, this can reduce user immersion due to their unnatural correspondence with the real hands. We propose an augmented virtuality (AV) pipeline allows users to visualize their limbs in VE to overcome this limit. In particular, they were captured by a single monocular RGB camera placed in an egocentric perspective, segmented using a deep convolutional neural network (CNN), and streamed in the VE. In addition, hands were tracked through a Leap Motion controller to allow user interaction. We introduced two case studies as a preliminary investigation for this approach. Finally, both quantitative and qualitative evaluations of the CNN results were provided and highlighted the effectiveness of the proposed CNN achieving remarkable results in several real-life unconstrained scenarios.en_US
dc.description.sectionheadersAugmented and Virtual Reality
dc.description.seriesinformationSmart Tools and Apps for Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20211483
dc.identifier.isbn978-3-03868-165-6
dc.identifier.issn2617-4855
dc.identifier.pages129-137
dc.identifier.urihttps://doi.org/10.2312/stag.20211483
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20211483
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectVirtual reality
dc.subjectMixed / augmented reality
dc.subjectImage segmentation
dc.subjectNeural networks
dc.subjectPerception
dc.subjectImage processing
dc.titleExploring Upper Limb Segmentation with Deep Learning for Augmented Virtualityen_US
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