@inproceedings{
huang2024motionaware,
title={Motion-aware Latent Diffusion Models for Video Frame Interpolation},
author={Zhilin Huang and Yijie Yu and Ling Yang and Chujun Qin and Bing Zheng and Xiawu Zheng and Zikun Zhou and Yaowei Wang and Wenming Yang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=u0geEr7X2O}
}
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.
Motion-aware Latent Diffusion Models for Video Frame Interpolation
Zhilin Huang Yijie Yu Ling Yang Chujun Qin Bing Zheng Xiawu Zheng Zikun Zhou Yaowei Wang Wenming Yang