The ability to envision 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 rely 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 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. We showcase our method by successfully predicting future frames of a smooth fluid simulation environment.

翻译：预见未来状态的能力对于在动态环境中做出明智决策至关重要。鉴于摄像头提供了一种普遍且信息丰富的感知模态，从图像序列预测未来状态的问题已引起广泛关注。当前最先进的方法通常采用大规模参数化模型进行预测。尽管这些模型往往能实现精确预测，但依赖大规模训练数据集才能收敛到有效解。本文聚焦于利用极少量训练数据预测图像序列未来图像的问题。为此，我们采用非参数模型对图像预测采取概率化方法。我们为逐帧预测的图像生成概率分布，并通过时间传播不确定性以生成预测的置信度度量。高斯过程因其数据高效性及在线整合新训练数据的能力而得到应用。通过成功预测平滑流体仿真环境的未来帧，我们展示了该方法的效果。