Castle in the Sky: Dynamic Sky Replacement and Harmonization in Videos.

Abstract

We propose a vision-based framework for dynamic sky replacement and harmonization in videos. Different from previous sky editing methods that either focus on static photos or require real-time pose signal from the camera's inertial measurement units, our method is purely vision-based, without any requirements on the capturing devices, and can be well applied to either online or offline processing scenarios. Our method runs in real-time and is free of manual interactions. We decompose the video sky replacement into several proxy tasks, including motion estimation, sky matting, and image blending. We derive the motion equation of an object at infinity on the image plane under the camera's motion, and propose "flow propagation", a novel method for robust motion estimation. We also propose a coarse-to-fine sky matting network to predict accurate sky matte and design image blending to improve the harmonization. Experiments are conducted on videos diversely captured in the wild and show high fidelity and good generalization capability of our framework in both visual quality and lighting/motion dynamics. We also introduce a new method for content-aware image augmentation and proved that this method is beneficial to visual perception in autonomous driving scenarios. Our code and animated results are available at https://github.com/jiupinjia/SkyAR.