  Video frame interpolation (VFI) aims to synthesize intermediate frames in
between existing frames to enhance visual smoothness and quality. Beyond the
conventional methods based on the reconstruction loss, recent works employ the
high quality generative models for perceptual quality. However, they require
complex training and large computational cost for modeling on the pixel space.
In this paper, we introduce disentangled Motion Modeling (MoMo), a
diffusion-based approach for VFI that enhances visual quality by focusing on
intermediate motion modeling. We propose disentangled two-stage training
process, initially training a frame synthesis model to generate frames from
input pairs and their optical flows. Subsequently, we propose a motion
diffusion model, equipped with our novel diffusion U-Net architecture designed
for optical flow, to produce bi-directional flows between frames. This method,
by leveraging the simpler low-frequency representation of motions, achieves
superior perceptual quality with reduced computational demands compared to
generative modeling methods on the pixel space. Our method surpasses
state-of-the-art methods in perceptual metrics across various benchmarks,
demonstrating its efficacy and efficiency in VFI. Our code is available at:
https://github.com/JHLew/MoMo
