  Video frame interpolation(VFI) has witnessed great progress in recent years.
While existing VFI models still struggle to achieve a good trade-off between
accuracy and efficiency: fast models often have inferior accuracy; accurate
models typically run slowly. However, easy samples with small motion or clear
texture can achieve competitive results with simple models and do not require
heavy computation. In this paper, we present an integrated pipeline which
combines difficulty assessment with video frame interpolation. Specifically, it
firstly leverages a pre-assessment model to measure the interpolation
difficulty level of input frames, and then dynamically selects an appropriate
VFI model to generate interpolation results. Furthermore, a large-scale VFI
difficulty assessment dataset is collected and annotated to train our
pre-assessment model. Extensive experiments show that easy samples pass through
fast models while difficult samples inference with heavy models, and our
proposed pipeline can improve the accuracy-efficiency trade-off for VFI.
