Abstract

This paper proposes motion prediction in single still images by learning it from a set of videos. The building assumption is that simi- lar 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 regres- sion derived from first principles, and (iii) shows the value of adding motion predictions in different tasks such as: weak frame-proposals con- taining unexpected events, action recognition, motion saliency. Illustra- tive results indicate that motion prediction is not only feasible, but also provides valuable information for a number of applications.