# MAEVI: Motion Aware Event-Based Video Frame Interpolation

arXiv: https://arxiv.org/abs/2303.02025 · HTML: https://mlsanity.com/paper/2303.02025/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 20 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Depth-Aware Video Frame Interpolation](https://mlsanity.com/paper/1904.00830/) (2019)
- [Video Frame Interpolation via Adaptive Convolution](https://mlsanity.com/paper/1703.07514/) (2017)
- [TimeLens: Event-based Video Frame Interpolation](https://mlsanity.com/paper/2106.07286/) (2021)
- [Quadratic video interpolation](https://mlsanity.com/paper/1911.00627/) (2019)
- [AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation](https://mlsanity.com/paper/1907.10244/) (2019)
- [XVFI: eXtreme Video Frame Interpolation](https://mlsanity.com/paper/2103.16206/) (2021)
- [Video Frame Interpolation Transformer](https://mlsanity.com/paper/2111.13817/) (2021)
- [BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation](https://mlsanity.com/paper/2007.12622/) (2020)
- [Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion](https://mlsanity.com/paper/2203.17191/) (2022)
- [FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation](https://mlsanity.com/paper/2012.08512/) (2020)
- [PhaseNet for Video Frame Interpolation](https://mlsanity.com/paper/1804.00884/) (2018)
- [Revisiting Adaptive Convolutions for Video Frame Interpolation](https://mlsanity.com/paper/2011.01280/) (2020)
- [An Asynchronous Kalman Filter for Hybrid Event Cameras](https://mlsanity.com/paper/2012.05590/) (2020)
- [FastRIFE: Optimization of Real-Time Intermediate Flow Estimation for Video Frame Interpolation](https://mlsanity.com/paper/2105.13482/) (2021)
- [E-VFIA : Event-Based Video Frame Interpolation with Attention](https://mlsanity.com/paper/2209.09359/) (2022)

## Built on by (most cited first, 1 of 1)

- [Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey](https://mlsanity.com/paper/2509.09971/) (2025)

## Abstract (arXiv)

Utilization of event-based cameras is expected to improve the visual quality of video frame interpolation solutions. We introduce a learning-based method to exploit moving region boundaries in a video sequence to increase the overall interpolation quality.Event cameras allow us to determine moving areas precisely; and hence, better video frame interpolation quality can be achieved by emphasizing these regions using an appropriate loss function. The results show a notable average textitPSNR improvement of 1.3 dB for the tested data sets, as well as subjectively more pleasing visual results with less ghosting and blurry artifacts.