Retracted: iMetaTown: A Metaverse System with Multiple Interactive Functions Based on Virtual Reality.

Area of Science:

• Computer Science
• Virtual Reality
• Human-Computer Interaction

Background:

• Developing immersive and interactive Metaverse experiences requires advanced Human-Computer Interaction (HCI) systems.
• Existing systems often struggle with realistic and real-time 3D avatar representation and interaction.

Purpose of the Study:

• To pioneer a real-time interactive and immersive Metaverse HCI system using Virtual Reality (VR).
• To enhance player-player interactions through a novel 3D face reconstruction method.
• To optimize 3D model learning and face alignment using a 2D Self-Supervised Learning (2DASL) approach.

Main Methods:

• Implemented a 3D face reconstruction method based on weakly supervised learning.
• Utilized a 2D Self-Supervised Learning (2DASL) approach with 2D face images for improved 3D model learning.
• Developed functional modules for user interactions (hugs, handshakes) and communication (voice, text) via blockchain.
• Addressed challenges of managing multiple simultaneous online users.

Main Results:

• The 2DASL face reconstruction method significantly improved 3D face alignment and interaction capabilities.
• The experimental system achieved real-time processing speeds (up to 18 fps) on a personal computer.
• User feedback indicated high scores for social acceptance, interaction usability, emotional expression, and overall satisfaction.

Conclusions:

• The developed VR HCI system effectively enhances Metaverse interactions through optimized 3D face modeling.
• The system demonstrates real-time performance and high user acceptance, paving the way for more engaging virtual experiences.
• The 2DASL approach offers a robust solution for 3D face reconstruction in VR HCI applications.