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Updated: Nov 10, 2025

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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LiveObj: Object Semantics-based Viewport Prediction for Live Mobile Virtual Reality Streaming.

Xianglong Feng, Zeyang Bao, Sheng Wei

    IEEE Transactions on Visualization and Computer Graphics
    |April 1, 2021
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    Summary
    This summary is machine-generated.

    LiveObj predicts user viewports in live virtual reality (VR) streaming by detecting and tracking objects. This novel approach significantly saves bandwidth and ensures real-time performance for immersive VR experiences.

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    Area of Science:

    • Computer Science
    • Multimedia Systems
    • Virtual Reality

    Background:

    • Virtual reality (VR) video streaming offers immersive experiences but faces bandwidth challenges due to high data volumes.
    • Existing bandwidth-saving methods for VR streaming often rely on historical data, limiting their application to live streaming scenarios.

    Purpose of the Study:

    • To develop a novel mechanism for real-time viewport prediction in live VR video streaming.
    • To address the bandwidth limitations inherent in high-resolution 360-degree video delivery.

    Main Methods:

    • Implemented LiveObj, a live viewport prediction mechanism utilizing semantic object detection within video frames.
    • Employed a reinforcement learning algorithm to track detected objects and infer the user's viewport in real time.
    • Evaluated the system's performance using 48 users across 10 VR video sessions.

    Main Results:

    • LiveObj demonstrated high prediction accuracy for user viewports in live VR streaming.
    • Significant bandwidth savings were achieved through the selective streaming of predicted viewports.
    • The system exhibited real-time performance with minimal processing delays, suitable for live applications.

    Conclusions:

    • LiveObj effectively enables efficient live VR video streaming by accurately predicting user viewports.
    • The semantic object detection and reinforcement learning approach overcomes limitations of previous methods.
    • This technology paves the way for enhanced, high-quality live VR experiences with reduced bandwidth requirements.