Learning a Deep Agent to Predict Head Movement in 360-Degree Images

Abstract

Virtual reality adequately stimulates senses to trick users into accepting the virtual environment. To create a sense of immersion, high-resolution images are required to satisfy human visual system, and low latency is essential for smooth operations, which put great demands on data processing and transmission. Actually, when exploring in the virtual environment, viewers only perceive the content in the current field of view. Therefore, if we can predict the head movements that are important behaviors of viewers, more processing resources can be allocated to the active field of view. In this article, we propose a model to predict the trajectory of head movement. Deep reinforcement learning is employed to mimic the decision making. In our framework, to characterize each state, features for viewport images are extracted by convolutional neural networks. In addition, the spherical coordinate maps and visited maps are generated for each viewport image, which facilitate the multiple dimensions of the state information by considering the impact of historical head movement and position information. To ensure the accurate simulation of visual behaviors during the watching of panoramas, we stipulate that the model imitates the behaviors of human demonstrators. To allow the model to generalize to more conditions, the intrinsic motivation is employed to guide the agent’s action toward reducing uncertainty, which can enhance robustness during the exploration. The experimental results demonstrate the effectiveness of the proposed stepwise head movement predictor.