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Related Concept Videos

Nonconscious Mimicry01:13

Nonconscious Mimicry

Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.

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Related Experiment Video

Updated: May 8, 2026

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

SelfBlending: Artificial Intelligence-Driven Augmentation With Hand Interactions for Seamless Reality Blending in

Ahmed Elsharkawy, Bocheon Gim, Aya Ataya

    IEEE Transactions on Visualization and Computer Graphics
    |May 6, 2026
    PubMed
    Summary

    This study introduces SelfBlending, a novel framework for virtual reality (VR). It uses AI hand tracking to seamlessly blend real-world objects into virtual environments (VE), enhancing user presence and interaction.

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    Photorealistic Learned Landscapes for Augmented Reality
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    Photorealistic Learned Landscapes for Augmented Reality

    Published on: June 27, 2025

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    Last Updated: May 8, 2026

    Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
    06:53

    Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

    Published on: March 1, 2017

    Photorealistic Learned Landscapes for Augmented Reality
    06:54

    Photorealistic Learned Landscapes for Augmented Reality

    Published on: June 27, 2025

    Area of Science:

    • Human-Computer Interaction
    • Virtual Reality
    • Computer Vision

    Background:

    • Interacting with real-world objects during immersive virtual reality (VR) is challenging.
    • Current systems lack personalized object recall and seamless cross-reality transitions.
    • Head-mounted displays (HMDs) often disrupt immersion when switching between virtual and real worlds.

    Purpose of the Study:

    • To develop a framework enabling intuitive interaction with real-world objects within VR.
    • To enhance user experience by improving presence and cross-reality continuity.
    • To enable personalized blending of real objects into virtual environments (VE).

    Main Methods:

    • Developed SelfBlending, a framework using AI-based hand tracking for object labeling.
    • Utilized object recognition to blend selected real-world objects into the VE.
    • Evaluated SelfBlending against VR passthrough and manual HMD removal.

    Main Results:

    • SelfBlending enhanced user experience, boosting presence and cross-reality continuity.
    • The framework supported efficient physical interaction with real objects.
    • Selective interaction with real objects was achieved with minimal VR disruption.

    Conclusions:

    • SelfBlending offers a more immersive and intuitive way to interact with real-world objects in VR.
    • The framework addresses limitations of current cross-reality systems.
    • AI-powered hand tracking and object recognition are key to seamless virtual-real integration.