  In existing Video Frame Interpolation (VFI) approaches, the motion estimation
between neighboring frames plays a crucial role. However, the estimation
accuracy in existing methods remains a challenge, primarily due to the inherent
ambiguity in identifying corresponding areas in adjacent frames for
interpolation. Therefore, enhancing accuracy by distinguishing different
regions before motion estimation is of utmost importance. In this paper, we
introduce a novel solution involving the utilization of open-world segmentation
models, e.g., SAM (Segment Anything Model), to derive Region-Distinguishable
Priors (RDPs) in different frames. These RDPs are represented as
spatial-varying Gaussian mixtures, distinguishing an arbitrary number of areas
with a unified modality. RDPs can be integrated into existing motion-based VFI
methods to enhance features for motion estimation, facilitated by our designed
play-and-plug Hierarchical Region-aware Feature Fusion Module (HRFFM). HRFFM
incorporates RDP into various hierarchical stages of VFI's encoder, using
RDP-guided Feature Normalization (RDPFN) in a residual learning manner. With
HRFFM and RDP, the features within VFI's encoder exhibit similar
representations for matched regions in neighboring frames, thus improving the
synthesis of intermediate frames. Extensive experiments demonstrate that HRFFM
consistently enhances VFI performance across various scenes.
