Research on Character Trajectory Prediction Method with Fused Perspective Information in Virtual Scene

Abstract

Character trajectory prediction in virtual scene is conducive to improving the preloading of scene resources. Most state-of-the-art trajectory prediction methods are devoted to the pedestrian trajectory in the real world. These methods cannot work well in virtual scene when encountering simple role interaction, more random motion and limited types of accessible data. In this paper, we proposed a role trajectory prediction method based on viewpoint information (RIPA algorithm, Role Intent Prediction Algorithm), which includes role interest calculation module based on viewpoint information and a role trajectory prediction module based on information of intention. Firstly, the role interest calculation module based on view information is used to predict the role’s interest in the virtual object. Secondly, the role interest is integrated into the trajectory prediction model, and the attention mechanism is introduced to improve the prediction performance. The experimental results show that, compared with the traditional LSTM model, the role trajectory prediction method proposed in this paper can improve the prediction accuracy by about 2%, and can predict the future target scene and role target position at least 1s in advance.