  Research on video frame interpolation has made significant progress in recent
years. However, existing methods mostly use off-the-shelf metrics to measure
the quality of interpolation results with the exception of a few methods that
employ user studies, which is time-consuming. As video frame interpolation
results often exhibit unique artifacts, existing quality metrics sometimes are
not consistent with human perception when measuring the interpolation results.
Some recent deep learning-based perceptual quality metrics are shown more
consistent with human judgments, but their performance on videos is compromised
since they do not consider temporal information. In this paper, we present a
dedicated perceptual quality metric for measuring video frame interpolation
results. Our method learns perceptual features directly from videos instead of
individual frames. It compares pyramid features extracted from video frames and
employs Swin Transformer blocks-based spatio-temporal modules to extract
spatio-temporal information. To train our metric, we collected a new video
frame interpolation quality assessment dataset. Our experiments show that our
dedicated quality metric outperforms state-of-the-art methods when measuring
video frame interpolation results. Our code and model are made publicly
available at \url{https://github.com/hqqxyy/VFIPS}.
