Virtual Tourism Scene Generation Empowered by Generative Adversarial Networks (GAN) for Cross-Cultural Immersive Experience Optimization and User Behavior Preference Interpretability Analysis

Virtual tourism has emerged as a transformative domain in digital heritage and cultural dissemination, offering users the ability to explore remote, historically significant, or culturally rich sites through immersive and interactive virtual environments. This field plays a pivotal role in enhancing cross-cultural exchange, educational access, and inclusive tourism experiences, particularly for those unable to travel physically. However, generating realistic and semantically accurate virtual tourism scenes remains a significant challenge. Traditional methods often rely on manual 3D modeling and texture design, which are time-consuming, costly, and difficult to scale. Meanwhile, automated approaches frequently struggle with maintaining visual realism, semantic coherence, and robustness when handling sparse or incomplete input data. In this work, we propose a novel solution leveraging Generative Adversarial Networks (GANs) for the automatic creation of high-fidelity virtual tourism scenes. Our model, SceneCraftNet, features a multi-branch architecture that integrates structural inference, texture synthesis, and semantic alignment. This enables the system to construct large-scale outdoor environments with high levels of detail and cultural authenticity. To further enhance visual realism, SceneCraftNet incorporates adaptive rendering techniques that dynamically optimize lighting, textures, and perspective effects based on scene context. Through extensive experiments and comparative evaluations, our method demonstrates superior performance in generating visually compelling and semantically rich environments. The proposed approach significantly advances the field of virtual tourism by enabling scalable, culturally aware, and personalized virtual experiences. It lays the groundwork for future innovations in immersive media, digital heritage preservation, and AI-driven cultural storytelling.