  Videos contain various types and strengths of motions that may look
unnaturally discontinuous in time when the recorded frame rate is low. This
paper reviews the first AIM challenge on video temporal super-resolution (frame
interpolation) with a focus on the proposed solutions and results. From
low-frame-rate (15 fps) video sequences, the challenge participants are asked
to submit higher-framerate (60 fps) video sequences by estimating temporally
intermediate frames. We employ the REDS VTSR dataset derived from diverse
videos captured in a hand-held camera for training and evaluation purposes. The
competition had 62 registered participants, and a total of 8 teams competed in
the final testing phase. The challenge winning methods achieve the
state-of-the-art in video temporal superresolution.
