  With the advancement of AIGC, video frame interpolation (VFI) has become a
crucial component in existing video generation frameworks, attracting
widespread research interest. For the VFI task, the motion estimation between
neighboring frames plays a crucial role in avoiding motion ambiguity. However,
existing VFI methods always struggle to accurately predict the motion
information between consecutive frames, and this imprecise estimation leads to
blurred and visually incoherent interpolated frames. In this paper, we propose
a novel diffusion framework, motion-aware latent diffusion models (MADiff),
which is specifically designed for the VFI task. By incorporating motion priors
between the conditional neighboring frames with the target interpolated frame
predicted throughout the diffusion sampling procedure, MADiff progressively
refines the intermediate outcomes, culminating in generating both visually
smooth and realistic results. Extensive experiments conducted on benchmark
datasets demonstrate that our method achieves state-of-the-art performance
significantly outperforming existing approaches, especially under challenging
scenarios involving dynamic textures with complex motion.
