  Effectively extracting inter-frame motion and appearance information is
important for video frame interpolation (VFI). Previous works either extract
both types of information in a mixed way or elaborate separate modules for each
type of information, which lead to representation ambiguity and low efficiency.
In this paper, we propose a novel module to explicitly extract motion and
appearance information via a unifying operation. Specifically, we rethink the
information process in inter-frame attention and reuse its attention map for
both appearance feature enhancement and motion information extraction.
Furthermore, for efficient VFI, our proposed module could be seamlessly
integrated into a hybrid CNN and Transformer architecture. This hybrid pipeline
can alleviate the computational complexity of inter-frame attention as well as
preserve detailed low-level structure information. Experimental results
demonstrate that, for both fixed- and arbitrary-timestep interpolation, our
method achieves state-of-the-art performance on various datasets. Meanwhile,
our approach enjoys a lighter computation overhead over models with close
performance. The source code and models are available at
https://github.com/MCG-NJU/EMA-VFI.
