FLAVR: Flow-Agnostic Video Representations
for Fast Frame Interpolation

  • 1UC San Diego
  • 2Carnegie Mellon University
  • 3Facebook AI


Project Video



  • A flow-free and single-shot prediction approach for video frame interpolation.
  • Uses 3D convolutions to capture motion trajectories.

SlowMotion Effect Using FLAVR


Ultra SlowMotion on Insect Flights



A majority of approaches solve the problem of video frame interpolation by computing bidirectional optical flow between adjacent frames of a video followed by a suitable warping algorithm to generate the output frames. However, methods relying on optical flow often fail to model occlusions and complex non-linear motions directly from the video and introduce additional bottlenecks unsuitable for easy deployment.

To overcome these limitations, we propose a flexible and efficient architecture that makes use of 3D space-time convolutions to enable end to end learning and inference for the task of video frame interpolation. Our method efficiently learns to reason about non-linear motions, complex occlusions and temporal abstractions resulting in improved performance on video interpolation, while requiring no additional inputs in the form of optical flow or depth maps.

Due to its simplicity, our proposed method improves the inference speed on video frame interpolation. In addition, we evaluate our model on a wide range of challenging settings and consistently demonstrate superior qualitative and quantitative results compared with current methods on various popular benchmarks including Vimeo-90K, UCF101, DAVIS, Adobe, and GoPro. Finally, we demonstrate that video frame interpolation can serve as a useful self-supervised pretext task for action recognition, optical flow estimation, and motion magnification.


We would like to thank Adrian Smith for providing us with the insect videos. The website template was borrowed from Zhenqin Li's project.