# Deep Iterative Frame Interpolation for Full-frame Video Stabilization

arXiv: https://arxiv.org/abs/1909.02641 · HTML: https://mlsanity.com/paper/1909.02641/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 23 of 23)

- [Hybrid Neural Fusion for Full-frame Video Stabilization](https://mlsanity.com/paper/2102.06205/) (2021)
- [StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability](https://mlsanity.com/paper/2308.04904/) (2023)
- [Minimum Latency Deep Online Video Stabilization](https://mlsanity.com/paper/2212.02073/) (2022)
- [Deep Online Fused Video Stabilization](https://mlsanity.com/paper/2102.01279/) (2021)
- [Fast Full-frame Video Stabilization with Iterative Optimization](https://mlsanity.com/paper/2307.12774/) (2023)
- [From Sim-to-Real: Toward General Event-based Low-light Frame Interpolation with Per-scene Optimization](https://mlsanity.com/paper/2406.08090/) (2024)
- [GlobalFlowNet: Video Stabilization using Deep Distilled Global Motion Estimates](https://mlsanity.com/paper/2210.13769/) (2022)
- [3D Multi-frame Fusion for Video Stabilization](https://mlsanity.com/paper/2404.12887/) (2024)
- [NTIRE 2023 Quality Assessment of Video Enhancement Challenge](https://mlsanity.com/paper/2307.09729/) (2023)
- [Out-of-boundary View Synthesis Towards Full-Frame Video Stabilization](https://mlsanity.com/paper/2108.09041/) (2021)
- [Harnessing Meta-Learning for Improving Full-Frame Video Stabilization](https://mlsanity.com/paper/2403.03662/) (2024)
- [GaVS: 3D-Grounded Video Stabilization via Temporally-Consistent Local Reconstruction and Rendering](https://mlsanity.com/paper/2506.23957/) (2025)
- [Self-Supervised Real-time Video Stabilization](https://mlsanity.com/paper/2111.05980/) (2021)
- [Eliminating Warping Shakes for Unsupervised Online Video Stitching](https://mlsanity.com/paper/2403.06378/) (2024)
- [StabStitch++: Unsupervised Online Video Stitching with Spatiotemporal Bidirectional Warps](https://mlsanity.com/paper/2505.05001/) (2025)
- [Render In-between: Motion Guided Video Synthesis for Action Interpolation](https://mlsanity.com/paper/2111.01029/) (2021)
- [FoV-Net: Field-of-View Extrapolation Using Self-Attention and Uncertainty](https://mlsanity.com/paper/2204.01267/) (2022)
- [Harnessing Meta-Learning for Controllable Full-Frame Video Stabilization](https://mlsanity.com/paper/2508.18859/) (2025)
- [Leveraging Synthetic Data to Learn Video Stabilization Under Adverse Conditions](https://mlsanity.com/paper/2208.12763/) (2022)
- [STint: Self-supervised Temporal Interpolation for Geospatial Data](https://mlsanity.com/paper/2309.00059/) (2023)
- [Beyond Wide-Angle Images: Structure-to-Detail Video Portrait Correction via Unsupervised Spatiotemporal Adaptation](https://mlsanity.com/paper/2504.00401/) (2025)
- [No Labels, No Look-Ahead: Unsupervised Online Video Stabilization with Classical Priors](https://mlsanity.com/paper/2602.23141/) (2026)
- [VS3R: Robust Full-frame Video Stabilization via Deep 3D Reconstruction](https://mlsanity.com/paper/2603.05851/) (2026)

## Abstract (arXiv)

Video stabilization is a fundamental and important technique for higher quality videos. Prior works have extensively explored video stabilization, but most of them involve cropping of the frame boundaries and introduce moderate levels of distortion. We present a novel deep approach to video stabilization which can generate video frames without cropping and low distortion. The proposed framework utilizes frame interpolation techniques to generate in between frames, leading to reduced inter-frame jitter. Once applied in an iterative fashion, the stabilization effect becomes stronger. A major advantage is that our framework is end-to-end trainable in an unsupervised manner. In addition, our method is able to run in near real-time (15 fps). To the best of our knowledge, this is the first work to propose an unsupervised deep approach to full-frame video stabilization. We show the advantages of our method through quantitative and qualitative evaluations comparing to the state-of-the-art methods.