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The ability to envision future states is crucial to informed decision makingwhile interacting with dynamic environments. With cameras providing a prevalentand information rich sensing modality, the problem of predicting future statesfrom image sequences has garnered a lot of attention. Current state of the artmethods typically train large parametric models for their predictions. Thoughoften able to predict with accuracy, these models rely on the availability oflarge training datasets to converge to useful solutions. In this paper we focuson the problem of predicting future images of an image sequence from verylittle training data. To approach this problem, we use non-parametric models totake a probabilistic approach to image prediction. We generate probabilitydistributions over sequentially predicted images and propagate uncertaintythrough time to generate a confidence metric for our predictions. GaussianProcesses are used for their data efficiency and ability to readily incorporatenew training data online. We showcase our method by successfully predictingfuture frames of a smooth fluid simulation environment.

Towards Non-Parametric Models for Confidence Aware Image Prediction from Low Data using Gaussian Processes