  This work addresses continuous space-time video super-resolution (C-STVSR)
that aims to up-scale an input video both spatially and temporally by any
scaling factors. One key challenge of C-STVSR is to propagate information
temporally among the input video frames. To this end, we introduce a space-time
local implicit neural function. It has the striking feature of learning forward
motion for a continuum of pixels. We motivate the use of forward motion from
the perspective of learning individual motion trajectories, as opposed to
learning a mixture of motion trajectories with backward motion. To ease motion
interpolation, we encode sparsely sampled forward motion extracted from the
input video as the contextual input. Along with a reliability-aware splatting
and decoding scheme, our framework, termed MoTIF, achieves the state-of-the-art
performance on C-STVSR. The source code of MoTIF is available at
https://github.com/sichun233746/MoTIF.
