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Related Experiment Video

Updated: Jun 12, 2026

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment
10:14

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment

Published on: May 10, 2024

Tri-attention complex-valued CNN for multimodal holographic digital human interaction.

Xuan-Ye Wang, Yu Zhao, Zhong Xu

    Optics Express
    |June 11, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces a voice-interactive holographic digital human avatar system for AR/VR. It enhances full-color holographic reconstruction and real-time interaction using advanced AI and a novel Tri-Attention Complex-valued Convolutional Neural Network (TA-CCNN).

    Area of Science:

    • Computer Vision
    • Holography
    • Artificial Intelligence

    Background:

    • Holographic displays offer immersive AR/VR experiences but face challenges in full-color generation, visual consistency, and real-time interaction.
    • Existing systems struggle with high-fidelity holographic reconstruction and seamless user interaction.

    Purpose of the Study:

    • To propose a multimodal voice-interactive holographic digital human avatar system.
    • To enhance the quality of full-color computer-generated holography (CGH) and enable real-time interaction.

    Main Methods:

    • Utilized an RGB-D digital human avatar generated by AI-generated content (AIGC) technology for personalized configurations.
    • Implemented an end-to-end semantic interaction architecture ('ASR + Dual-LLM + TTS') for multi-agent voice interaction.

    Related Experiment Videos

    Last Updated: Jun 12, 2026

    Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment
    10:14

    Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment

    Published on: May 10, 2024

  • Developed a Tri-Attention Complex-valued Convolutional Neural Network (TA-CCNN) with an Energy-Aware branch for high-fidelity holographic reconstruction.

    • Main Results:

    • Achieved end-to-end RGB-D generation with personalized avatar configurations (outfit, personality, voice cloning).
    • Enabled high-quality, real-time voice interaction through the multi-agent system.
    • Significantly improved contrast in high-energy regions, enhancing color reproduction and visual effects with the TA-CCNN.

    Conclusions:

    • The proposed system successfully integrates voice interaction with high-fidelity holographic avatars.
    • The TA-CCNN advances full-color holographic reconstruction, offering superior visual quality for immersive applications.
    • This work paves the way for more interactive and visually compelling holographic experiences in AR/VR.