  The ability to predict future states is crucial to informed decision-making
while interacting with dynamic environments. With cameras providing a prevalent
and information-rich sensing modality, the problem of predicting future states
from image sequences has garnered a lot of attention. Current state-of-the-art
methods typically train large parametric models for their predictions. Though
often able to predict with accuracy these models often fail to provide
interpretable confidence metrics around their predictions. Additionally these
methods are reliant on the availability of large training datasets to converge
to useful solutions. In this paper, we focus on the problem of predicting
future images of an image sequence with interpretable confidence bounds from
very little training data. To approach this problem, we use non-parametric
models to take a probabilistic approach to image prediction. We generate
probability distributions over sequentially predicted images, and propagate
uncertainty through time to generate a confidence metric for our predictions.
Gaussian Processes are used for their data efficiency and ability to readily
incorporate new training data online. Our methods predictions are evaluated on
a smooth fluid simulation environment. We showcase the capabilities of our
approach on real world data by predicting pedestrian flows and weather patterns
from satellite imagery.
