# Long-term Video Frame Interpolation via Feature Propagation

arXiv: https://arxiv.org/abs/2203.15427 · HTML: https://mlsanity.com/paper/2203.15427/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 50 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 30 of 34)

- [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)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [Deep multi-scale video prediction beyond mean square error](https://mlsanity.com/paper/1511.05440/) (2015)
- [Softmax Splatting for Video Frame Interpolation](https://mlsanity.com/paper/2003.05534/) (2020)
- [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)
- [Video Frame Interpolation via Adaptive Convolution](https://mlsanity.com/paper/1703.07514/) (2017)
- [Context-aware Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/1803.10967/) (2018)
- [Quadratic video interpolation](https://mlsanity.com/paper/1911.00627/) (2019)
- [Need for Speed: A Benchmark for Higher Frame Rate Object Tracking](https://mlsanity.com/paper/1703.05884/) (2017)
- [AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation](https://mlsanity.com/paper/1907.10244/) (2019)
- [BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation](https://mlsanity.com/paper/2007.12622/) (2020)
- [MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement](https://mlsanity.com/paper/1810.08768/) (2018)
- [PhaseNet for Video Frame Interpolation](https://mlsanity.com/paper/1804.00884/) (2018)
- [Blurry Video Frame Interpolation](https://mlsanity.com/paper/2002.12259/) (2020)
- [Unsupervised Video Interpolation Using Cycle Consistency](https://mlsanity.com/paper/1906.05928/) (2019)
- [All at Once: Temporally Adaptive Multi-Frame Interpolation with Advanced Motion Modeling](https://mlsanity.com/paper/2007.11762/) (2020)
- [Learning Image Matching by Simply Watching Video](https://mlsanity.com/paper/1603.06041/) (2016)
- [Predicting Future Instance Segmentation by Forecasting Convolutional Features](https://mlsanity.com/paper/1803.11496/) (2018)
- [Scene-Adaptive Video Frame Interpolation via Meta-Learning](https://mlsanity.com/paper/2004.00779/) (2020)
- [Segmenting the Future](https://mlsanity.com/paper/1904.10666/) (2019)

## Built on by (most cited first, 8 of 8)

- [JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation](https://mlsanity.com/paper/2206.04231/) (2022)
- [BVI-VFI: A Video Quality Database for Video Frame Interpolation](https://mlsanity.com/paper/2210.00823/) (2022)
- [TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation](https://mlsanity.com/paper/2507.04984/) (2025)
- [Frozen Forecasting: A Unified Evaluation](https://mlsanity.com/paper/2507.13942/) (2025)
- [Video Frame Interpolation with Many-to-many Splatting and Spatial Selective Refinement](https://mlsanity.com/paper/2310.18946/) (2023)
- [A Multi-In-Single-Out Network for Video Frame Interpolation without Optical Flow](https://mlsanity.com/paper/2311.11602/) (2023)
- [Learning Event-guided Exposure-agnostic Video Frame Interpolation via Adaptive Feature Blending](https://mlsanity.com/paper/2510.22565/) (2025)
- [3DTV: A Feedforward Interpolation Network for Real-Time View Synthesis](https://mlsanity.com/paper/2604.11211/) (2026)

## Abstract (arXiv)

Video frame interpolation (VFI) works generally predict intermediate frame(s) by first estimating the motion between inputs and then warping the inputs to the target time with the estimated motion. This approach, however, is not optimal when the temporal distance between the input sequence increases as existing motion estimation modules cannot effectively handle large motions. Hence, VFI works perform well for small frame gaps and perform poorly as the frame gap increases. In this work, we propose a novel framework to address this problem. We argue that when there is a large gap between inputs, instead of estimating imprecise motion that will eventually lead to inaccurate interpolation, we can safely propagate from one side of the input up to a reliable time frame using the other input as a reference. Then, the rest of the intermediate frames can be interpolated using standard approaches as the temporal gap is now narrowed. To this end, we propose a propagation network (PNet) by extending the classic feature-level forecasting with a novel motion-to-feature approach. To be thorough, we adopt a simple interpolation model along with PNet as our full model and design a simple procedure to train the full model in an end-to-end manner. Experimental results on several benchmark datasets confirm the effectiveness of our method for long-term VFI compared to state-of-the-art approaches.