# Harnessing Meta-Learning for Improving Full-Frame Video Stabilization

arXiv: https://arxiv.org/abs/2403.03662 · HTML: https://mlsanity.com/paper/2403.03662/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 56 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 24 of 24)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [The Contextual Loss for Image Transformation with Non-Aligned Data](https://mlsanity.com/paper/1803.02077/) (2018)
- [Generalized Inner Loop Meta-Learning](https://mlsanity.com/paper/1910.01727/) (2019)
- [Tracking by Instance Detection: A Meta-Learning Approach](https://mlsanity.com/paper/2004.00830/) (2020)
- [Self-Supervised Video Representation Learning with Meta-Contrastive Network](https://mlsanity.com/paper/2108.08426/) (2021)
- [Deep Iterative Frame Interpolation for Full-frame Video Stabilization](https://mlsanity.com/paper/1909.02641/) (2019)
- [Bilevel Fast Scene Adaptation for Low-Light Image Enhancement](https://mlsanity.com/paper/2306.01343/) (2023)
- [Hybrid Neural Fusion for Full-frame Video Stabilization](https://mlsanity.com/paper/2102.06205/) (2021)
- [Scene-Adaptive Video Frame Interpolation via Meta-Learning](https://mlsanity.com/paper/2004.00779/) (2020)
- [Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning](https://mlsanity.com/paper/2111.15056/) (2021)
- [DUT: Learning Video Stabilization by Simply Watching Unstable Videos](https://mlsanity.com/paper/2011.14574/) (2020)
- [One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video](https://mlsanity.com/paper/2103.12988/) (2021)
- [MetaPix: Few-Shot Video Retargeting](https://mlsanity.com/paper/1910.04742/) (2019)
- [Minimum Latency Deep Online Video Stabilization](https://mlsanity.com/paper/2212.02073/) (2022)
- [Video Deblurring by Fitting to Test Data](https://mlsanity.com/paper/2012.05228/) (2020)
- [GlobalFlowNet: Video Stabilization using Deep Distilled Global Motion Estimates](https://mlsanity.com/paper/2210.13769/) (2022)
- [Out-of-boundary View Synthesis Towards Full-Frame Video Stabilization](https://mlsanity.com/paper/2108.09041/) (2021)
- [Meta Learning Deep Visual Words for Fast Video Object Segmentation](https://mlsanity.com/paper/1812.01397/) (2018)
- [Self-Supervised Real-time Video Stabilization](https://mlsanity.com/paper/2111.05980/) (2021)
- [Deep Motion Blind Video Stabilization](https://mlsanity.com/paper/2011.09697/) (2020)
- [Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning](https://mlsanity.com/paper/2108.02832/) (2021)

## Built on by (most cited first, 5 of 5)

- [GaVS: 3D-Grounded Video Stabilization via Temporally-Consistent Local Reconstruction and Rendering](https://mlsanity.com/paper/2506.23957/) (2025)
- [Harnessing Meta-Learning for Controllable Full-Frame Video Stabilization](https://mlsanity.com/paper/2508.18859/) (2025)
- [Self-Aug: Query and Entropy Adaptive Decoding for Large Vision-Language Models](https://mlsanity.com/paper/2510.13315/) (2025)
- [No Labels, No Look-Ahead: Unsupervised Online Video Stabilization with Classical Priors](https://mlsanity.com/paper/2602.23141/) (2026)
- [Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding](https://mlsanity.com/paper/2604.13313/) (2026)

## Abstract (arXiv)

Video stabilization is a longstanding computer vision problem, particularly pixel-level synthesis solutions for video stabilization which synthesize full frames add to the complexity of this task. These techniques aim to stabilize videos by synthesizing full frames while enhancing the stability of the considered video. This intensifies the complexity of the task due to the distinct mix of unique motion profiles and visual content present in each video sequence, making robust generalization with fixed parameters difficult. In our study, we introduce a novel approach to enhance the performance of pixel-level synthesis solutions for video stabilization by adapting these models to individual input video sequences. The proposed adaptation exploits low-level visual cues accessible during test-time to improve both the stability and quality of resulting videos. We highlight the efficacy of our methodology of "test-time adaptation" through simple fine-tuning of one of these models, followed by significant stability gain via the integration of meta-learning techniques. Notably, significant improvement is achieved with only a single adaptation step. The versatility of the proposed algorithm is demonstrated by consistently improving the performance of various pixel-level synthesis models for video stabilization in real-world scenarios.