Animation video frame prediction based on ConvGRU fine-grained synthesis flow

Abstract

Due to the complexity and dynamism of animated scenes, frame prediction in animated videos is a challenging task. In order to improve the playback frame rate of animated videos, an innovative convolutional neural network combined with convolutional gated recursive unit method is used to refine the synthesized stream in frame prediction of animated videos. The obtained results indicated that the average prediction accuracy of the proposed model was 99.64%, and the training effect was good. The peak signal-to-noise ratios on the three datasets were 31.26, 36.63, and 22.15 dB, respectively, and the structural similarities were 0.958, 0.886, and 0.813, respectively. The maximum Learned Perceptual Image Patch Similarity of the proposed model was 0.144. This indicates that the model has achieved excellent performance in prediction accuracy and visual quality, which can successfully capture complex dynamics and fine details in animated scenes. The contribution of this study is to provide a technical support for improving the accuracy of frame prediction in animated videos, which will help promote the intelligent development of the animation production field.