Bundled Camera Paths for Video Stabilization

Shuaicheng Liu1         Lu yuan2         Ping Tan1         Jian Sun2

1. National University of Singapore               2. Microsoft Research Asia



We present a novel video stabilization method which models camera motion with a bundle of (multiple) camera paths. The proposed model is based on a mesh-based, spatially-variant motion representation and an adaptive, space-time path optimization. Our motion representation allows us to fundamentally handle parallax and rolling shutter effects while it does not require long feature trajectories or sparse 3D reconstruction. We introduce the 'as-similar-as-possible' idea to make motion estimation more robust. Our space-time path smoothing adaptively adjusts smoothness strength by considering discontinuities, cropping size and geometrical distortion in a unified optimization framework. The evaluation on alarge variety of consumer videos demonstrates the merits of ourmethod.



@ article{BundledPaths2013,
author = {Shuaicheng Liu and Lu Yuan and Ping Tan and Jian Sun},
title = {Bundled camera Paths for Video Stabilization},
journal = {ACM Transactions on Graphics (TOG) (Proceedings of SIGGRAPH 2013)},
volume = {32},
number = {4},
year = {2013},





As-similar-as-possible warping and motion model estimation

  Videos grouped by category

Related Projects:

Shuaicheng Liu, Mingyu Li, Shuyuan Zhu, Bing Zeng: CodingFlow: Enable Video Coding for Video Stabilization. IEEE Transactions on Image Processing (TIP), vol. 26, no. 7, pp. 3291-3302, 2017. [PDF]

Shuaicheng Liu, Ping Tan, Lu Yuan, Jian Sun, Bing Zeng: MeshFlow: Minimum Laency Online Video Stabilization. European Conference on Computer Vision (ECCV). 2016. [PDF][Video][Model Code]

Shuaicheng Liu, Lu Yuan, Ping Tan, Jian Sun: SteadyFlow: Spatially Smooth Optical Flow for Video Stabilization. IEEE Conference on Computer Vision and Patten Recognition(CVPR) 2014 [PDF][project page]

Shuaicheng Liu, Yinting Wang, Lu Yuan, Jiajun Bu, Ping Tan, Jian Sun: Video Stabilization with a Depth Camera. IEEE Conference on Computer Vision and Patten Recognition(CVPR) 2012 [PDF][project page]


Video:  Download [66MB]


2 Failure cases:

Large Oclussion

Rolling Shutter + Oclussion