Leveraging Synthetic Data To Learn Video Stabilization Under Adverse Conditions

Abdulrahman Kerim, Washington L. S. Ramos, Leandro Soriano Marcolino, Erickson R. Nascimento, Richard Jiang; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 6931-6940

Stabilization plays a central role in improving the quality of videos. However, current methods perform poorly under adverse conditions. In this paper, we propose a synthetic-aware adverse weather video stabilization algorithm that dispenses real data for training, relying solely on synthetic data. Our approach leverages specially generated synthetic data to avoid the feature extraction issues faced by current methods. To achieve this, we present a novel data generator to produce the required training data with an automatic ground-truth extraction procedure. We also propose a new dataset, VSAC105Real, and compare our method to five recent video stabilization algorithms using two benchmarks. Our method generalizes well on real-world videos across all weather conditions and does not require large-scale synthetic training data. Implementations for our proposed video stabilization algorithm, generator, and datasets are available at https://github.com/A-Kerim/SyntheticData4VideoStabilization_WACV_2024.