TaylorSwiftNet: Taylor Driven Temporal Modeling for Swift Future Frame Prediction

Abstract

While recurrent neural networks (RNNs) demonstrate outstanding capabilities in future video frame prediction, they model dynamics in a discrete time space and sequentially go through all frames until the desired future temporal step is reached. RNNs are therefore prone to accumulate the error as the number of future frames increases. In contrast, partial differential equations (PDEs) model physical phenomena like dynamics in continuous time space, however, current PDE-based approaches discretize the PDEs using e.g., the forward Euler method. In this work, we therefore propose to approximate the motion in a video by a continuous function using the Taylor series. To this end, we introduce TayloSwiftNet, a novel convolutional neural network that learns to estimate the higher order terms of the Taylor series for a given input video. TayloSwiftNet can swiftly predict any desired future frame in just one forward pass and change the temporal resolution on-the-fly. The experimental results on various datasets demonstrate the superiority of our model.