Novel CNN approach for video prediction based on FitVid

Video prediction is a task in computer vision that predicts future frames from the past few frames of video. In video prediction, a simple CNN-based approach called SimVP has marked remarkable performance without using RNN or vison transformer (ViT). In this paper, we propose a model structure to improve performance of video prediction based on FitVid. FitVid is a regression-based method of predicting future videos using not only video but also motion. We focus on video prediction only conditioned on videos. To this goal, we introduce network modules used in SimVP to FitVid. Experimental results show that the proposed structure shows better prediction accuracy compared to SimVP.