  We propose a self-supervised method for learning motion-focused video
representations. Existing approaches minimize distances between temporally
augmented videos, which maintain high spatial similarity. We instead propose to
learn similarities between videos with identical local motion dynamics but an
otherwise different appearance. We do so by adding synthetic motion
trajectories to videos which we refer to as tubelets. By simulating different
tubelet motions and applying transformations, such as scaling and rotation, we
introduce motion patterns beyond what is present in the pretraining data. This
allows us to learn a video representation that is remarkably data efficient:
our approach maintains performance when using only 25\% of the pretraining
videos. Experiments on 10 diverse downstream settings demonstrate our
competitive performance and generalizability to new domains and fine-grained
actions.
