  This paper addresses the task of space-time video super-resolution (ST-VSR).
Existing methods generally suffer from inaccurate motion estimation and motion
compensation (MEMC) problems for large motions. Inspired by recent progress in
physics-informed neural networks, we model the challenges of MEMC in ST-VSR as
a mapping between two continuous function spaces. Specifically, our approach
transforms independent low-resolution representations in the coarse-grained
continuous function space into refined representations with enriched
spatiotemporal details in the fine-grained continuous function space. To
achieve efficient and accurate MEMC, we design a Galerkin-type attention
function to perform frame alignment and temporal interpolation. Due to the
linear complexity of the Galerkin-type attention mechanism, our model avoids
patch partitioning and offers global receptive fields, enabling precise
estimation of large motions. The experimental results show that the proposed
method surpasses state-of-the-art techniques in both fixed-size and continuous
space-time video super-resolution tasks.
