Smooth Globally Warp Locally: Video Stabilization using Homography Fields

Abstract

Conceptually, video stabilization is achieved by estimating the camera trajectory throughout the video and then smoothing the trajectory. In practice, the pipeline invariably leads to estimating update transforms that adjust each frame of the video such that the overall sequence appears to be stabilized. Therefore, we argue that estimating good update transforms is more critical to success than accurately modeling and characterizing the motion of the camera. Based on this observation, we propose the usage of homography fields for video stabilization. A homography field is a spatially varying warp that is regularized to be as projective as possible, so as to enable accurate warping while adhering closely to the underlying geometric constraints. We show that homography fields are powerful enough to meet the various warping needs of video stabilization, not just in the core step of stabilization, but also in video inpainting. This enables relatively simple algorithms to be used for motion modeling and smoothing. We demonstrate the merits of our video stabilization pipeline on various public testing videos.