  Currently, one of the major challenges in deep learning-based video frame
interpolation (VFI) is the large model sizes and high computational complexity
associated with many high performance VFI approaches. In this paper, we present
a distillation-based two-stage workflow for obtaining compressed VFI models
which perform competitively to the state of the arts, at a greatly reduced
model size and complexity. Specifically, an optimisation-based network pruning
method is first applied to a recently proposed frame interpolation model,
ST-MFNet, which outperforms many other VFI methods but suffers from large model
size. The resulting new network architecture achieves a 91% reduction in
parameters and 35% increase in speed. Secondly, the performance of the new
network is further enhanced through a teacher-student knowledge distillation
training process using a Laplacian distillation loss. The final low complexity
model, ST-MFNet Mini, achieves a comparable performance to most existing
high-complex VFI methods, only outperformed by the original ST-MFNet. Our
source code is available at https://github.com/crispianm/ST-MFNet-Mini
