Title: One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network,
Abstract: There is an inherent need for autonomous cars, drones, and other robots to
have a notion of how their environment behaves and to anticipate changes in the
near future. In this work, we focus on anticipating future appearance given the
current frame of a video. Existing work focuses on either predicting the future
appearance as the next frame of a video, or predicting future motion as optical
flow or motion trajectories starting from a single video frame. This work
stretches the ability of CNNs (Convolutional Neural Networks) to predict an
anticipation of appearance at an arbitrarily given future time, not necessarily
the next video frame. We condition our predicted future appearance on a
continuous time variable that allows us to anticipate future frames at a given
temporal distance, directly from the input video frame. We show that CNNs can
learn an intrinsic representation of typical appearance changes over time and
successfully generate realistic predictions at a deliberate time difference in
the near future.