# Blurry Video Frame Interpolation

arXiv: https://arxiv.org/abs/2002.12259 · HTML: https://mlsanity.com/paper/2002.12259/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 30 of 57)

- [XVFI: eXtreme Video Frame Interpolation](https://mlsanity.com/paper/2103.16206/) (2021)
- [Deep Image Deblurring: A Survey](https://mlsanity.com/paper/2201.10700/) (2022)
- [Unifying Motion Deblurring and Frame Interpolation with Events](https://mlsanity.com/paper/2203.12178/) (2022)
- [FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring](https://mlsanity.com/paper/2401.03707/) (2024)
- [A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild](https://mlsanity.com/paper/2301.05191/) (2023)
- [Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network](https://mlsanity.com/paper/2106.16028/) (2021)
- [Event-guided Deblurring of Unknown Exposure Time Videos](https://mlsanity.com/paper/2112.06988/) (2021)
- [Blur Interpolation Transformer for Real-World Motion from Blur](https://mlsanity.com/paper/2211.11423/) (2022)
- [Video Frame Interpolation without Temporal Priors](https://mlsanity.com/paper/2112.01161/) (2021)
- [Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones](https://mlsanity.com/paper/2204.05532/) (2022)
- [Splatting-based Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/2201.10075/) (2022)
- [DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting](https://mlsanity.com/paper/2111.09985/) (2021)
- [Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance](https://mlsanity.com/paper/2207.10123/) (2022)
- [Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time](https://mlsanity.com/paper/2303.15043/) (2023)
- [Bringing Rolling Shutter Images Alive with Dual Reversed Distortion](https://mlsanity.com/paper/2203.06451/) (2022)
- [TTVFI: Learning Trajectory-Aware Transformer for Video Frame Interpolation](https://mlsanity.com/paper/2207.09048/) (2022)
- [Motion-blurred Video Interpolation and Extrapolation](https://mlsanity.com/paper/2103.02984/) (2021)
- [Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos](https://mlsanity.com/paper/2111.14465/) (2021)
- [Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects](https://mlsanity.com/paper/2106.08762/) (2021)
- [Towards Interpretable Video Super-Resolution via Alternating Optimization](https://mlsanity.com/paper/2207.10765/) (2022)
- [MoBluRF: Motion Deblurring Neural Radiance Fields for Blurry Monocular Video](https://mlsanity.com/paper/2312.13528/) (2023)
- [AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation](https://mlsanity.com/paper/2506.01061/) (2025)
- [Long-term Video Frame Interpolation via Feature Propagation](https://mlsanity.com/paper/2203.15427/) (2022)
- [Enhanced Deep Animation Video Interpolation](https://mlsanity.com/paper/2206.12657/) (2022)
- [BVI-VFI: A Video Quality Database for Video Frame Interpolation](https://mlsanity.com/paper/2210.00823/) (2022)
- [EA-Net: Edge-Aware Network for Flow-based Video Frame Interpolation](https://mlsanity.com/paper/2105.07673/) (2021)
- [Rolling Shutter Inversion: Bring Rolling Shutter Images to High Framerate Global Shutter Video](https://mlsanity.com/paper/2210.03040/) (2022)
- [Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2104.07473/) (2021)
- [Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Coding](https://mlsanity.com/paper/2112.14768/) (2021)
- [Meta Transferring for Deblurring](https://mlsanity.com/paper/2210.08036/) (2022)

## Abstract (arXiv)

Existing works reduce motion blur and up-convert frame rate through two separate ways, including frame deblurring and frame interpolation. However, few studies have approached the joint video enhancement problem, namely synthesizing high-frame-rate clear results from low-frame-rate blurry inputs. In this paper, we propose a blurry video frame interpolation method to reduce motion blur and up-convert frame rate simultaneously. Specifically, we develop a pyramid module to cyclically synthesize clear intermediate frames. The pyramid module features adjustable spatial receptive field and temporal scope, thus contributing to controllable computational complexity and restoration ability. Besides, we propose an inter-pyramid recurrent module to connect sequential models to exploit the temporal relationship. The pyramid module integrates a recurrent module, thus can iteratively synthesize temporally smooth results without significantly increasing the model size. Extensive experimental results demonstrate that our method performs favorably against state-of-the-art methods.