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LDMVFI: Video Frame Interpolation with Latent Diffusion Models

2023-03-16 17:24:41
Duolikun Danier, Fan Zhang, David Bull


Existing works on video frame interpolation (VFI) mostly employ deep neural networks trained to minimize the L1 or L2 distance between their outputs and ground-truth frames. Despite recent advances, existing VFI methods tend to produce perceptually inferior results, particularly for challenging scenarios including large motions and dynamic textures. Towards developing perceptually-oriented VFI methods, we propose latent diffusion model-based VFI, LDMVFI. This approaches the VFI problem from a generative perspective by formulating it as a conditional generation problem. As the first effort to address VFI using latent diffusion models, we rigorously benchmark our method following the common evaluation protocol adopted in the existing VFI literature. Our quantitative experiments and user study indicate that LDMVFI is able to interpolate video content with superior perceptual quality compared to the state of the art, even in the high-resolution regime. Our source code will be made available here.

Abstract (translated)

现有的视频帧插值(VFI)工作大多使用训练以最小化输出与真相帧之间的L1或L2距离的深层神经网络。尽管最近取得了进展,但现有的VFI方法往往产生感觉上较差的结果,特别是对于包括大规模运动和动态纹理等挑战场景的结果。为了开发感觉导向的VFI方法,我们提出了基于隐扩散模型的VFI,即LDMVFI。这种方法从生成角度看待VFI问题,将其表述为条件生成问题。作为解决使用隐扩散模型的VFI的第一步,我们严格基准我们的方法和现有VFI文献所采用的 common evaluation protocol。我们的量化实验和用户研究表明,LDMVFI相对于现有技术水平可以插值出感觉上更好的视频内容,即使在高分辨率状态下也是如此。我们的源代码将在这里提供。



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