  Future frame prediction has been approached through two primary methods:
autoregressive and non-autoregressive. Autoregressive methods rely on the
Markov assumption and can achieve high accuracy in the early stages of
prediction when errors are not yet accumulated. However, their performance
tends to decline as the number of time steps increases. In contrast,
non-autoregressive methods can achieve relatively high performance but lack
correlation between predictions for each time step. In this paper, we propose
an Implicit Stacked Autoregressive Model for Video Prediction (IAM4VP), which
is an implicit video prediction model that applies a stacked autoregressive
method. Like non-autoregressive methods, stacked autoregressive methods use the
same observed frame to estimate all future frames. However, they use their own
predictions as input, similar to autoregressive methods. As the number of time
steps increases, predictions are sequentially stacked in the queue. To evaluate
the effectiveness of IAM4VP, we conducted experiments on three common future
frame prediction benchmark datasets and weather\&climate prediction benchmark
datasets. The results demonstrate that our proposed model achieves
state-of-the-art performance.
