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Real-Time Intermediate Flow Estimation for Video Frame Interpolation

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arxiv 2011.06294 v12 pith:3772UFOA submitted 2020-11-12 cs.CV cs.LG

Zhewei Huang , Tianyuan Zhang , Wen Heng , Boxin Shi , Shuchang Zhou This is my paper · ORCID
classification cs.CVcs.LG
keywords rifeflowframeintermediateinterpolationreal-timevideoencoding
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Real-time video frame interpolation (VFI) is very useful in video processing, media players, and display devices. We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for VFI. To realize a high-quality flow-based VFI method, RIFE uses a neural network named IFNet that can estimate the intermediate flows end-to-end with much faster speed. A privileged distillation scheme is designed for stable IFNet training and improve the overall performance. RIFE does not rely on pre-trained optical flow models and can support arbitrary-timestep frame interpolation with the temporal encoding input. Experiments demonstrate that RIFE achieves state-of-the-art performance on several public benchmarks. Compared with the popular SuperSlomo and DAIN methods, RIFE is 4--27 times faster and produces better results. Furthermore, RIFE can be extended to wider applications thanks to temporal encoding. The code is available at https://github.com/megvii-research/ECCV2022-RIFE.

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  1. FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases

    cs.CV 2025-09 conditional novelty 6.0 of 10

    FlowSeek integrates a frozen depth foundation model and classical motion bases into a SEA-RAFT-style flow network, achieving state-of-the-art zero-shot generalization when trained on a single GPU.

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