Flow4R: Unifying 4D Reconstruction and Tracking with Scene Flow

Reconstructing and tracking dynamic 3D scenes is a funda-mental challenge in computer vision. Existing methods typically decou-ple geometry from motion: static multi-view reconstruction systems as-sume a rigid world, whereas dynamic tracking frameworks rely on explicitego-motion estimation or separate object motion models. In this work,we propose Flow4R, a unified framework that treats relative scene flowas the central representation linking 3D structure, camera ego-motion,and dynamic object motion. Given a two-view input, Flow4R employs ashared Vision Transformer to predict a compact, pixel-aligned propertyset comprising 3D point positions, scene flow, pose weights, and con-fidence maps. This flow-centric formulation allows local geometry andbidirectional motion to be jointly inferred in a single feedforward pass,eliminating the need for explicit pose regression heads or complex bundleadjustment. By training jointly on static and dynamic datasets, Flow4Rachieves state-of-the-art performance on 4D reconstruction and trackingbenchmarks, demonstrating the power of the flow-centric formulation forspatiotemporal scene understanding.