  An agent that is capable of predicting what happens next can perform a
variety of tasks through planning with no additional training. Furthermore,
such an agent can internally represent the complex dynamics of the real-world
and therefore can acquire a representation useful for a variety of visual
perception tasks. This makes predicting the future frames of a video,
conditioned on the observed past and potentially future actions, an interesting
task which remains exceptionally challenging despite many recent advances.
Existing video prediction models have shown promising results on simple narrow
benchmarks but they generate low quality predictions on real-life datasets with
more complicated dynamics or broader domain. There is a growing body of
evidence that underfitting on the training data is one of the primary causes
for the low quality predictions. In this paper, we argue that the inefficient
use of parameters in the current video models is the main reason for
underfitting. Therefore, we introduce a new architecture, named FitVid, which
is capable of severe overfitting on the common benchmarks while having similar
parameter count as the current state-of-the-art models. We analyze the
consequences of overfitting, illustrating how it can produce unexpected
outcomes such as generating high quality output by repeating the training data,
and how it can be mitigated using existing image augmentation techniques. As a
result, FitVid outperforms the current state-of-the-art models across four
different video prediction benchmarks on four different metrics.
