# Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow

arXiv: https://arxiv.org/abs/2208.09127 · HTML: https://mlsanity.com/paper/2208.09127/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 19 Aug 2022.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [Depth-Aware Video Frame Interpolation](https://mlsanity.com/paper/1904.00830/) (2019)
- [Video Frame Interpolation via Adaptive Separable Convolution](https://mlsanity.com/paper/1708.01692/) (2017)
- [Video Frame Synthesis using Deep Voxel Flow](https://mlsanity.com/paper/1702.02463/) (2017)
- [View Synthesis by Appearance Flow](https://mlsanity.com/paper/1605.03557/) (2016)
- [Context-aware Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/1803.10967/) (2018)
- [TimeLens: Event-based Video Frame Interpolation](https://mlsanity.com/paper/2106.07286/) (2021)
- [Quadratic video interpolation](https://mlsanity.com/paper/1911.00627/) (2019)
- [Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera](https://mlsanity.com/paper/1811.10180/) (2018)
- [BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation](https://mlsanity.com/paper/2007.12622/) (2020)
- [Event-based High Dynamic Range Image and Very High Frame Rate Video Generation using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1811.08230/) (2018)
- [Learning Event-Based Motion Deblurring](https://mlsanity.com/paper/2004.05794/) (2020)
- [Event Enhanced High-Quality Image Recovery](https://mlsanity.com/paper/2007.08336/) (2020)
- [Enhanced Quadratic Video Interpolation](https://mlsanity.com/paper/2009.04642/) (2020)
- [Learning Image Matching by Simply Watching Video](https://mlsanity.com/paper/1603.06041/) (2016)
- [TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation](https://mlsanity.com/paper/2203.13859/) (2022)

## Built on by (most cited first, 18 of 18)

- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Mitigating Motion Blur in Neural Radiance Fields with Events and Frames](https://mlsanity.com/paper/2403.19780/) (2024)
- [Event-Based Video Frame Interpolation With Cross-Modal Asymmetric Bidirectional Motion Fields](https://mlsanity.com/paper/2502.13716/) (2025)
- [Towards Robust Event-based Networks for Nighttime via Unpaired Day-to-Night Event Translation](https://mlsanity.com/paper/2407.10703/) (2024)
- [TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion](https://mlsanity.com/paper/2505.03116/) (2025)
- [AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation](https://mlsanity.com/paper/2506.01061/) (2025)
- [HR-INR: Continuous Space-Time Video Super-Resolution via Event Camera](https://mlsanity.com/paper/2405.13389/) (2024)
- [Self-supervised Learning of Event-guided Video Frame Interpolation for Rolling Shutter Frames](https://mlsanity.com/paper/2306.15507/) (2023)
- [Revisiting Event-based Video Frame Interpolation](https://mlsanity.com/paper/2307.12558/) (2023)
- [Event-based Video Frame Interpolation with Edge Guided Motion Refinement](https://mlsanity.com/paper/2404.18156/) (2024)
- [EventDiff: A Unified and Efficient Diffusion Model Framework for Event-based Video Frame Interpolation](https://mlsanity.com/paper/2505.08235/) (2025)
- [Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey](https://mlsanity.com/paper/2509.09971/) (2025)
- [IDO-VFI: Identifying Dynamics via Optical Flow Guidance for Video Frame Interpolation with Events](https://mlsanity.com/paper/2305.10198/) (2023)
- [MambaFlow: A Mamba-Centric Architecture for End-to-End Optical Flow Estimation](https://mlsanity.com/paper/2503.07046/) (2025)
- [UniE2F: A Unified Diffusion Framework for Event-to-Frame Reconstruction with Video Foundation Models](https://mlsanity.com/paper/2602.19202/) (2026)
- [LongE2V: Long-Horizon Event-based Video Reconstruction, Prediction, and Frame Interpolation with Video Diffusion Models](https://mlsanity.com/paper/2607.08770/) (2026)
- [FlowPainter: Inpainting Optical Flow via Confidence-Guided Completion](https://mlsanity.com/paper/2607.10140/) (2026)

## Abstract (arXiv)

Video frame interpolation is a challenging task due to the ever-changing real-world scene. Previous methods often calculate the bi-directional optical flows and then predict the intermediate optical flows under the linear motion assumptions, leading to isotropic intermediate flow generation. Follow-up research obtained anisotropic adjustment through estimated higher-order motion information with extra frames. Based on the motion assumptions, their methods are hard to model the complicated motion in real scenes. In this paper, we propose an end-to-end training method A^2OF for video frame interpolation with event-driven Anisotropic Adjustment of Optical Flows. Specifically, we use events to generate optical flow distribution masks for the intermediate optical flow, which can model the complicated motion between two frames. Our proposed method outperforms the previous methods in video frame interpolation, taking supervised event-based video interpolation to a higher stage.