  We present All-Pairs Multi-Field Transforms (AMT), a new network architecture
for video frame interpolation. It is based on two essential designs. First, we
build bidirectional correlation volumes for all pairs of pixels, and use the
predicted bilateral flows to retrieve correlations for updating both flows and
the interpolated content feature. Second, we derive multiple groups of
fine-grained flow fields from one pair of updated coarse flows for performing
backward warping on the input frames separately. Combining these two designs
enables us to generate promising task-oriented flows and reduce the
difficulties in modeling large motions and handling occluded areas during frame
interpolation. These qualities promote our model to achieve state-of-the-art
performance on various benchmarks with high efficiency. Moreover, our
convolution-based model competes favorably compared to Transformer-based models
in terms of accuracy and efficiency. Our code is available at
https://github.com/MCG-NKU/AMT.
