  We present a multi-scale predictive coding model for future video frames
prediction. Drawing inspiration on the ``Predictive Coding" theories in
cognitive science, it is updated by a combination of bottom-up and top-down
information flows, which can enhance the interaction between different network
levels. However, traditional predictive coding models only predict what is
happening hierarchically rather than predicting the future. To address the
problem, our model employs a multi-scale approach (Coarse to Fine), where the
higher level neurons generate coarser predictions (lower resolution), while the
lower level generate finer predictions (higher resolution). In terms of network
architecture, we directly incorporate the encoder-decoder network within the
LSTM module and share the final encoded high-level semantic information across
different network levels. This enables comprehensive interaction between the
current input and the historical states of LSTM compared with the traditional
Encoder-LSTM-Decoder architecture, thus learning more believable temporal and
spatial dependencies. Furthermore, to tackle the instability in adversarial
training and mitigate the accumulation of prediction errors in long-term
prediction, we propose several improvements to the training strategy. Our
approach achieves good performance on datasets such as KTH, Moving MNIST and
Caltech Pedestrian. Code is available at https://github.com/Ling-CF/MSPN.
