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There is an inherent need for autonomous cars, drones, and other robots tohave a notion of how their environment behaves and to anticipate changes in thenear future. In this work, we focus on anticipating future appearance given thecurrent frame of a video. Existing work focuses on either predicting the futureappearance as the next frame of a video, or predicting future motion as opticalflow or motion trajectories starting from a single video frame. This workstretches the ability of CNNs (Convolutional Neural Networks) to predict ananticipation of appearance at an arbitrarily given future time, not necessarilythe next video frame. We condition our predicted future appearance on acontinuous time variable that allows us to anticipate future frames at a giventemporal distance, directly from the input video frame. We show that CNNs canlearn an intrinsic representation of typical appearance changes over time andsuccessfully generate realistic predictions at a deliberate time difference inthe near future.

One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network