The Application and challenges of virtual reality technology on modern visual communication design

Introduction: Virtual reality (VR) technology is transforming visual communication design by providing immersive experiences that enhance the production and presentation of visual content. This study explores the application of VR in this field, emphasizing its transformative potential while addressing associated challenges. Methods: We utilized the Golden Eagle Optimized Flexible Bayesian Neural Network (GEO-FBNN) to predict feature distributions, such as element position and color, under specific VR conditions. A dataset comprising high-quality design elements, real-time user interaction patterns, and VR system data was pre-processed for consistency, through min-max normalization and data cleaning. Experiments were conducted using Python 3.8 on Windows 10, ensuring compatibility with VR hardware. Results: The implementation of a deep learning approach significantly improved the real-time processing capabilities of VR systems. It established connections between visual frames, revealing insights that enhance design accuracy and responsiveness. The findings suggest that deep learning effectively mitigates challenges in VR integration, including motion sickness and navigation ease. Conclusions: This research provides a comprehensive overview of how deep learning can address the obstacles to incorporating VR in visual communication design. The results indicate not only a reduction in technical challenges but also an increase in creative opportunities, paving the way for a more integrated future of design in virtual environments.