GeoMFlow: Geometry-Guided Multi-Frame Optical Flow Estimation with Visual Geometry Priors

Modern optical-flow estimators primarily rely on appearance correspondence and task-specific motion representations. As pixel-level motion can be viewed as the image-plane projection of underlying 3D scene motion, geometry foundation models may provide scene-level structural cues that complement these representations. Motivated by this observation, we propose GeoMFlow, a lightweight geometry-conditioning framework that preserves the native matching and cost-volume pathways while injecting spatially aligned geometry features into recurrent flow refinement. Experiments on three standard optical-flow benchmarks demonstrate the effectiveness of visual geometry priors for multi-frame flow estimation. GeoMFlow further achieves an F1-all error of 2.84% and ranks #1 among all optical-flow methods on the KITTI online benchmark.