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arXiv · 2606.05915

CamFlow+: Hybrid Motion Bases for 2D Camera Motion Estimation with Stabilization Applications

Haipeng Li·Zhen Liu·Zhanglei Yang·Hai Jiang·Tianhao Zhou·Zhengzhe Liu·Ping Tan·Bing Zeng·Shuaicheng Liu
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Abstract

Estimating 2D camera motion is fundamental to computer vision and computational photography. Existing homography-based methods work well for planar scenes or pure rotation, but struggle with camera translation, depth variation, and local parallax; local homography and mesh-based models improve flexibility but still rely on piecewise planar assumptions. We introduce CamFlow+, a hybrid-basis framework that represents 2D camera motion directly in dense-flow space. CamFlow+ combines homography-derived physical bases, stochastic bases sampled from homography flows, and depth-translational bases derived from depth and camera intrinsics, relaxing the single-plane constraint while preserving camera-motion regularity. A depth-aware smoothness term further regularizes translation-induced parallax in continuous-depth regions while preserving motion changes near depth boundaries. We evaluate CamFlow+ on GHOF-Cam, a camera-motion benchmark that masks out dynamic objects and ill-posed occlusion regions in an optical-flow benchmark to isolate camera-induced motion. Experiments show that CamFlow+ improves sparse and dense camera-motion estimation. In digital video stabilization, CamFlow+ also improves global and local stability, achieving the best top-1 preference rate in a blind user study. Code and datasets will be available on the project page: https://lhaippp.github.io/CamFlow+.

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Haipeng Li, Zhen Liu, Zhanglei Yang, Hai Jiang, Tianhao Zhou, Zhengzhe Liu, Ping Tan, Bing Zeng, Shuaicheng Liu. 2026-06-04. CamFlow+: Hybrid Motion Bases for 2D Camera Motion Estimation with Stabilization Applications. https://arxiv.org/abs/2606.05915

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