Recent studies show that user’s visual attention during virtual reality museum navigation can be effectively estimated with deep learning models. However, these models rely on large-scale datasets that usually are of high structure complexity and context specific, which is challenging for nonspecialist researchers and designers. Therefore, we present the deep learning model, ALRF, to generalise on real-time user visual attention prediction in virtual reality context. The model combines two parallel deep learning streams to process the compact dataset of temporal–spatial salient features of user’s eye movements and virtual object coordinates. The prediction accuracy outperformed the state-of-the-art deep learning models by reaching record high 91.03%. Importantly, with quick parametric tuning, the model showed flexible applicability across different environments of the virtual reality museum and outdoor scenes. Implications for how the proposed model may be implemented as a generalising tool for adaptive virtual reality application design and evaluation are discussed.

最近的研究表明，使用深度学习模型可以有效地估计用户在虚拟现实博物馆导航期间的视觉注意力。但是，这些模型依赖于大型数据集，这些数据集通常具有较高的结构复杂性和特定于上下文的信息，这对非专业的研究人员和设计人员而言是一个挑战。因此，我们提出了深度学习模型ALRF，以概括虚拟现实环境中的实时用户视觉注意力预测。该模型结合了两个并行的深度学习流，以处理用户眼睛运动和虚拟对象坐标的时空显着特征的紧凑数据集。预测精度超过了最新的深度学习模型，达到了创纪录的91.03％。重要的是，通过快速的参数调整，该模型显示了在虚拟现实博物馆和室外场景的不同环境中的灵活适用性。讨论了如何将建议的模型实现为用于自适应虚拟现实应用程序设计和评估的通用工具的含义。