Harnessing Meta-Learning for Controllable Full-Frame Video Stabilization

2025-08-26

AI Summary

In video stabilization, pixel-level full-frame synthesis methods often suffer from poor generalization due to diverse motion patterns and scene content. To address this, we propose a meta-learning-driven adaptive stabilization framework. It introduces a jitter localization module to identify highly unstable segments and employs a directional adaptive strategy to dynamically adjust synthesis parameters within a single optimization iteration. Furthermore, differentiable optimization and feature-space alignment are incorporated to enable end-to-end jitter-aware modeling. Our approach preserves the controllability of classical methods while enhancing robustness and visual quality. Extensive experiments on multiple real-world benchmarks demonstrate significant improvements over state-of-the-art methods. The framework is compatible with diverse full-frame generative models and consistently boosts performance across downstream vision tasks.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Video stabilization remains a fundamental problem in computer vision, particularly pixel-level synthesis solutions for video stabilization, which synthesize full-frame outputs, add to the complexity of this task. These methods aim to enhance stability while synthesizing full-frame videos, but the inherent diversity in motion profiles and visual content present in each video sequence makes robust generalization with fixed parameters difficult. To address this, we present a novel method that improves pixel-level synthesis video stabilization methods by rapidly adapting models to each input video at test time. The proposed approach takes advantage of low-level visual cues available during inference to improve both the stability and visual quality of the output. Notably, the proposed rapid adaptation achieves significant performance gains even with a single adaptation pass. We further propose a jerk localization module and a targeted adaptation strategy, which focuses the adaptation on high-jerk segments for maximizing stability with fewer adaptation steps. The proposed methodology enables modern stabilizers to overcome the longstanding SOTA approaches while maintaining the full frame nature of the modern methods, while offering users with control mechanisms akin to classical approaches. Extensive experiments on diverse real-world datasets demonstrate the versatility of the proposed method. Our approach consistently improves the performance of various full-frame synthesis models in both qualitative and quantitative terms, including results on downstream applications.

Problem

• Addressing robust generalization in full-frame video stabilization
• Adapting models to diverse motion profiles per input video
• Improving stability and visual quality via rapid adaptation

Innovation

• Uses meta-learning for rapid video model adaptation
• Implements jerk localization for targeted stabilization
• Enables single-pass adaptation with control mechanisms