The goal of Video Frame Interpolation is to synthesize several frames in the middle of two adjacent frames of the original video. Video Frame Interpolation can be applied to generate slow motion video, increase video frame rate, and frame recovery in video streaming.
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A na\"ive method is to decompose it into two sub-tasks: video frame interpolation (VFI) and video super-resolution (VSR).
In this paper, we firstly present a dataset (X4K1000FPS) of 4K videos of 1000 fps with the extreme motion to the research community for video frame interpolation (VFI), and propose an extreme VFI network, called XVFI-Net, that first handles the VFI for 4K videos with large motion.
DNN-based frame interpolation--that generates the intermediate frames given two consecutive frames--typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e. g., mobile devices.
Ranked #1 on Video Frame Interpolation on Middlebury (LPIPS metric)
A majority of methods for video frame interpolation compute bidirectional optical flow between adjacent frames of a video, followed by a suitable warping algorithm to generate the output frames.
We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for Video Frame Interpolation (VFI).
Ranked #1 on Video Frame Interpolation on Vimeo90k
Video frame interpolation aims at synthesizing intermediate frames from nearby source frames while maintaining spatial and temporal consistencies.
We propose a novel deep-learning-based video interpolation algorithm based on bilateral motion estimation.
Ranked #2 on Video Frame Interpolation on Middlebury