Real-Time Intermediate Flow Estimation for Video Frame Interpolation
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Cited by 48 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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.
Key facts. Cited by 211 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Nov 2020.
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Citing papers per year: 2022: 10; 2023: 40; 2024: 57; 2025: 58; 2026: 46.
211 papers.
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- 2026Video Reconstruction using Diffusion-based Image-to-Video Generation with Trajectory Guidance14 May 2026
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- 2026FC-VFI: Faithful and Consistent Video Frame Interpolation for High-FPS Slow Motion Video Generation5 Mar 2026
- 2026Motion Prior Distillation in Time Reversal Sampling for Generative Inbetweening13 Feb 2026
- 2026Towards Holistic Modeling for Video Frame Interpolation with Auto-regressive Diffusion Transformers21 Jan 2026
- 2026Efficient Camera-Controlled Video Generation of Static Scenes via Sparse Diffusion and 3D Rendering14 Jan 2026
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- 2026Towards Standardized Light Field Quality Assessment: Hybrid Subjective Benchmarking and Objective Metric Evaluation3 Jul 2026
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