Video generation has made remarkable progress in recent years, especially
since the advent of the video diffusion models. Many video generation models
can produce plausible synthetic videos, e.g., Stable Video Diffusion (SVD).
However, most video models can only generate low frame rate videos due to the
limited GPU memory as well as the difficulty of modeling a large set of frames.
The training videos are always uniformly sampled at a specified interval for
temporal compression. Previous methods promote the frame rate by either
training a video interpolation model in pixel space as a postprocessing stage
or training an interpolation model in latent space for a specific base video
model. In this paper, we propose a training-free video interpolation method for
generative video diffusion models, which is generalizable to different models
in a plug-and-play manner. We investigate the non-linearity in the feature
space of video diffusion models and transform a video model into a
self-cascaded video diffusion model with incorporating the designed hidden
state correction modules. The self-cascaded architecture and the correction
module are proposed to retain the temporal consistency between key frames and
the interpolated frames. Extensive evaluations are preformed on multiple
popular video models to demonstrate the effectiveness of the propose method,
especially that our training-free method is even comparable to trained
interpolation models supported by huge compute resources and large-scale
datasets.

我们提出了一种对生成式视频扩散模型的训练无关的视频插值方法，通过将视频模型转换为自级联视频扩散模型并结合设计的隐藏状态校正模块，保持关键帧与插值帧之间的时间一致性。在多个受欢迎的视频模型上进行了广泛的评估，证明了该方法的有效性，特别是我们的训练无关方法甚至可以与由大量计算资源和大规模数据集支持的训练插值模型相媲美。