# Quadratic video interpolation

arXiv: https://arxiv.org/abs/1911.00627 · HTML: https://mlsanity.com/paper/1911.00627/

## Key facts

- Cited by 145 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2019.

## Built on by (most cited first, 30 of 145)

- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation](https://mlsanity.com/paper/2304.09790/) (2023)
- [SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow](https://mlsanity.com/paper/2405.14793/) (2024)
- [TimeLens: Event-based Video Frame Interpolation](https://mlsanity.com/paper/2106.07286/) (2021)
- [XVFI: eXtreme Video Frame Interpolation](https://mlsanity.com/paper/2103.16206/) (2021)
- [Video Frame Interpolation Transformer](https://mlsanity.com/paper/2111.13817/) (2021)
- [Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion](https://mlsanity.com/paper/2203.17191/) (2022)
- [IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation](https://mlsanity.com/paper/2205.14620/) (2022)
- [VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution](https://mlsanity.com/paper/2206.04647/) (2022)
- [LDMVFI: Video Frame Interpolation with Latent Diffusion Models](https://mlsanity.com/paper/2303.09508/) (2023)
- [Video Interpolation with Diffusion Models](https://mlsanity.com/paper/2404.01203/) (2024)
- [ToonCrafter: Generative Cartoon Interpolation](https://mlsanity.com/paper/2405.17933/) (2024)
- [Deep Animation Video Interpolation in the Wild](https://mlsanity.com/paper/2104.02495/) (2021)
- [High-resolution Face Swapping via Latent Semantics Disentanglement](https://mlsanity.com/paper/2203.15958/) (2022)
- [CDFI: Compression-Driven Network Design for Frame Interpolation](https://mlsanity.com/paper/2103.10559/) (2021)
- [A Dynamic Multi-Scale Voxel Flow Network for Video Prediction](https://mlsanity.com/paper/2303.09875/) (2023)
- [Generative Inbetweening: Adapting Image-to-Video Models for Keyframe Interpolation](https://mlsanity.com/paper/2408.15239/) (2024)
- [TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation](https://mlsanity.com/paper/2203.13859/) (2022)
- [FloLPIPS: A Bespoke Video Quality Metric for Frame Interpoation](https://mlsanity.com/paper/2207.08119/) (2022)
- [3D Human Texture Estimation from a Single Image with Transformers](https://mlsanity.com/paper/2109.02563/) (2021)
- [ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation](https://mlsanity.com/paper/2111.15483/) (2021)
- [WAFT: Warping-Alone Field Transforms for Optical Flow](https://mlsanity.com/paper/2506.21526/) (2025)
- [Extending Neural P-frame Codecs for B-frame Coding](https://mlsanity.com/paper/2104.00531/) (2021)
- [Optimizing Video Prediction via Video Frame Interpolation](https://mlsanity.com/paper/2206.13454/) (2022)
- [Sparse Global Matching for Video Frame Interpolation with Large Motion](https://mlsanity.com/paper/2404.06913/) (2024)
- [A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild](https://mlsanity.com/paper/2301.05191/) (2023)
- [Event-Based Video Frame Interpolation With Cross-Modal Asymmetric Bidirectional Motion Fields](https://mlsanity.com/paper/2502.13716/) (2025)
- [Video Frame Interpolation without Temporal Priors](https://mlsanity.com/paper/2112.01161/) (2021)
- [Framer: Interactive Frame Interpolation](https://mlsanity.com/paper/2410.18978/) (2024)

## Abstract (arXiv)

Video interpolation is an important problem in computer vision, which helps overcome the temporal limitation of camera sensors. Existing video interpolation methods usually assume uniform motion between consecutive frames and use linear models for interpolation, which cannot well approximate the complex motion in the real world. To address these issues, we propose a quadratic video interpolation method which exploits the acceleration information in videos. This method allows prediction with curvilinear trajectory and variable velocity, and generates more accurate interpolation results. For high-quality frame synthesis, we develop a flow reversal layer to estimate flow fields starting from the unknown target frame to the source frame. In addition, we present techniques for flow refinement. Extensive experiments demonstrate that our approach performs favorably against the existing linear models on a wide variety of video datasets.