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Leveraging Synthetic Data to Learn Video Stabilization Under Adverse Conditions. (arXiv:2208.12763v1 [cs.CV])
Aug. 29, 2022, 1:14 a.m. | Abdulrahman Kerim, Washington L. S. Ramos, Leandro Soriano Marcolino, Erickson R. Nascimento, Richard Jiang
cs.CV updates on arXiv.org arxiv.org
Video stabilization plays a central role to improve videos quality. However,
despite the substantial progress made by these methods, they were, mainly,
tested under standard weather and lighting conditions, and may perform poorly
under adverse conditions. In this paper, we propose a synthetic-aware adverse
weather robust algorithm for video stabilization that does not require real
data and can be trained only on synthetic data. We also present Silver, a novel
rendering engine to generate the required training data with an …
More from arxiv.org / cs.CV updates on arXiv.org
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