  For video frame interpolation (VFI), existing deep-learning-based approaches
strongly rely on the ground-truth (GT) intermediate frames, which sometimes
ignore the non-unique nature of motion judging from the given adjacent frames.
As a result, these methods tend to produce averaged solutions that are not
clear enough. To alleviate this issue, we propose to relax the requirement of
reconstructing an intermediate frame as close to the GT as possible. Towards
this end, we develop a texture consistency loss (TCL) upon the assumption that
the interpolated content should maintain similar structures with their
counterparts in the given frames. Predictions satisfying this constraint are
encouraged, though they may differ from the pre-defined GT. Without the bells
and whistles, our plug-and-play TCL is capable of improving the performance of
existing VFI frameworks. On the other hand, previous methods usually adopt the
cost volume or correlation map to achieve more accurate image/feature warping.
However, the O(N^2) ({N refers to the pixel count}) computational complexity
makes it infeasible for high-resolution cases. In this work, we design a
simple, efficient (O(N)) yet powerful cross-scale pyramid alignment (CSPA)
module, where multi-scale information is highly exploited. Extensive
experiments justify the efficiency and effectiveness of the proposed strategy.
