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Photorealistic Learned Landscapes for Augmented Reality
Published on: June 27, 2025
Art management platform based on virtual scenes
Jialiang He1,2, Xin Jiang3, Hong Tao3
1College of Art and Media, Quanzhou Vocational and Technical University, Quanzhou, 362000, China. hjl@dlnu.edu.cn.
Scientific Reports
|May 21, 2026
Summary
This study introduces a novel VR+Art platform recommendation system using graph neural networks and exhibition design. It significantly improves recommendation accuracy and enhances user engagement in virtual art environments.
Area of Science:
- Computer Science
- Virtual Reality
- Art Management
Background:
- Virtual reality (VR) art platforms require sophisticated recommendation systems.
- Existing algorithms struggle with dynamic user preferences and diverse interaction needs in art contexts.
Purpose of the Study:
- To design and develop an integrated VR+Art platform.
- To propose an improved recommendation algorithm for art management platforms.
Main Methods:
- Utilized Unity 3D and 3ds Max for front-end development.
- Developed the back-end using IntelliJ IDEA, SSM framework, and MySQL database.
- Implemented a recommendation algorithm combining graph neural networks (GNNs) with exhibition design rules and an attention mechanism.
Main Results:
- Achieved interactive connection between front-end scenes and back-end management with data transmission.
- The proposed GNN-based recommendation algorithm significantly improved accuracy over traditional methods.
- Enhanced display aesthetics and user engagement through integrated exhibition design rules.
Conclusions:
- The developed VR+Art platform offers integrated display, trading, and socializing functionalities.
- The novel recommendation system effectively addresses limitations of existing algorithms in virtual art environments.
- The integration of GNNs and exhibition design rules provides a more practical and aesthetically optimized recommendation solution.
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