  This paper proposes motion prediction in single still images by learning it
from a set of videos. The building assumption is that similar motion is
characterized by similar appearance. The proposed method learns local motion
patterns given a specific appearance and adds the predicted motion in a number
of applications. This work (i) introduces a novel method to predict motion from
appearance in a single static image, (ii) to that end, extends of the
Structured Random Forest with regression derived from first principles, and
(iii) shows the value of adding motion predictions in different tasks such as:
weak frame-proposals containing unexpected events, action recognition, motion
saliency. Illustrative results indicate that motion prediction is not only
feasible, but also provides valuable information for a number of applications.
