# Revisiting Adaptive Convolutions for Video Frame Interpolation

arXiv: https://arxiv.org/abs/2011.01280 · HTML: https://mlsanity.com/paper/2011.01280/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 30 of 32)

- [AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation](https://mlsanity.com/paper/2304.09790/) (2023)
- [FILM: Frame Interpolation for Large Motion](https://mlsanity.com/paper/2202.04901/) (2022)
- [Video Frame Interpolation Transformer](https://mlsanity.com/paper/2111.13817/) (2021)
- [Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation](https://mlsanity.com/paper/2303.00440/) (2023)
- [Many-to-many Splatting for Efficient Video Frame Interpolation](https://mlsanity.com/paper/2204.03513/) (2022)
- [Splatting-based Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/2201.10075/) (2022)
- [Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation](https://mlsanity.com/paper/2203.10291/) (2022)
- [3D Moments from Near-Duplicate Photos](https://mlsanity.com/paper/2205.06255/) (2022)
- [A Perceptual Quality Metric for Video Frame Interpolation](https://mlsanity.com/paper/2210.01879/) (2022)
- [Learning Cross-Video Neural Representations for High-Quality Frame Interpolation](https://mlsanity.com/paper/2203.00137/) (2022)
- [PDWN: Pyramid Deformable Warping Network for Video Interpolation](https://mlsanity.com/paper/2104.01517/) (2021)
- [Cross-Attention Transformer for Video Interpolation](https://mlsanity.com/paper/2207.04132/) (2022)
- [AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation](https://mlsanity.com/paper/2506.01061/) (2025)
- [Enhanced Deep Animation Video Interpolation](https://mlsanity.com/paper/2206.12657/) (2022)
- [A unified model for continuous conditional video prediction](https://mlsanity.com/paper/2210.05810/) (2022)
- [High-Resolution Frame Interpolation with Patch-based Cascaded Diffusion](https://mlsanity.com/paper/2410.11838/) (2024)
- [Texture-aware Video Frame Interpolation](https://mlsanity.com/paper/2102.13520/) (2021)
- [Video Frame Interpolation Based on Deformable Kernel Region](https://mlsanity.com/paper/2204.11396/) (2022)
- [E-VFIA : Event-Based Video Frame Interpolation with Attention](https://mlsanity.com/paper/2209.09359/) (2022)
- [Benchmarking Video Frame Interpolation](https://mlsanity.com/paper/2403.17128/) (2024)
- [You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution](https://mlsanity.com/paper/2207.06345/) (2022)
- [Video Frame Interpolation with Many-to-many Splatting and Spatial Selective Refinement](https://mlsanity.com/paper/2310.18946/) (2023)
- [EventDiff: A Unified and Efficient Diffusion Model Framework for Event-based Video Frame Interpolation](https://mlsanity.com/paper/2505.08235/) (2025)
- [MAEVI: Motion Aware Event-Based Video Frame Interpolation](https://mlsanity.com/paper/2303.02025/) (2023)
- [Multi-encoder Network for Parameter Reduction of a Kernel-based Interpolation Architecture](https://mlsanity.com/paper/2205.06723/) (2022)
- [Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information](https://mlsanity.com/paper/2302.13256/) (2023)
- [Restoration-Oriented Video Frame Interpolation with Region-Distinguishable Priors from SAM](https://mlsanity.com/paper/2312.15868/) (2023)
- [A new dataset and comparison for multi-camera frame synthesis](https://mlsanity.com/paper/2508.09068/) (2025)
- [Efficient motion-based metrics for video frame interpolation](https://mlsanity.com/paper/2508.09078/) (2025)
- [DeblurNVS: Geometric Latent Diffusion for Novel View Synthesis from Sparse Motion-Blurred Images](https://mlsanity.com/paper/2606.01315/) (2026)

## Abstract (arXiv)

Video frame interpolation, the synthesis of novel views in time, is an increasingly popular research direction with many new papers further advancing the state of the art. But as each new method comes with a host of variables that affect the interpolation quality, it can be hard to tell what is actually important for this task. In this work, we show, somewhat surprisingly, that it is possible to achieve near state-of-the-art results with an older, simpler approach, namely adaptive separable convolutions, by a subtle set of low level improvements. In doing so, we propose a number of intuitive but effective techniques to improve the frame interpolation quality, which also have the potential to other related applications of adaptive convolutions such as burst image denoising, joint image filtering, or video prediction.