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

• URL: http://arxiv.org/abs/2606.05915v1
• Date: Thu, 04 Jun 2026 09:22:07 GMT
• Title: CamFlow+: Hybrid Motion Bases for 2D Camera Motion Estimation with Stabilization Applications
• Abstract summary: Existing homography-based methods struggle with camera translation, depth variation, and local parallax.<n>We introduce CamFlow+, a hybrid-basis framework that represents 2D camera motion directly in dense-flow space.<n>In digital video stabilization, CamFlow+ also improves global and local stability, achieving the best top-1 preference rate in a blind user study.
• Score: 80.50125916369686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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