Predicting high dimensional video sequences is a curiously difficult problem.
The number of possible futures for a given video sequence grows exponentially
over time due to uncertainty. This is especially evident when trying to predict
complicated natural video scenes from a limited snapshot of the world. The
inherent uncertainty accumulates the further into the future you predict making
long-term prediction very difficult. In this work we introduce a number of
improvements to existing work that aid in creating Robust Video Predictors
(RoViPs). We show that with a combination of deep Perceptual and
uncertainty-based reconstruction losses we are able to create high quality
short-term predictions. Attention-based skip connections are utilised to allow
for long range spatial movement of input features to further improve
performance. Finally, we show that by simply making the predictor robust to its
own prediction errors, it is possible to produce very long, realistic natural
video sequences using an iterated single-step prediction task.

预测高维视频序列是一个异常困难的问题，由于不确定性，给定视频序列的可能未来数量随时间呈指数增长，尤其在从有限世界的快照中预测复杂的自然视频场景时更为明显。本文介绍了一系列改进现有工作的方法，有助于创建鲁棒的视频预测器（RoViPs）。我们通过深度感知和基于不确定性的重建损失的组合，展示了高质量的短期预测能力。利用基于注意力的跳跃连接允许输入特征的长程空间移动，进一步提高性能。最后，我们展示了通过使预测器对其自身的预测错误具有鲁棒性，可以使用迭代的逐步预测任务生成非常长且逼真的自然视频序列。