ReflectWorld: Seeing Two Worlds Through Glass via Multi-View Geometry-Routed Diffusion

Image reflection separation aims to recover the transmission and reflection layers from mixed observations captured through glass. Recent advances in large-scale pretrained models have substantially improved reflection recovery, yet most existing methods remain limited to single-image inference, where severe layer entanglement leaves the decomposition fundamentally ambiguous. Multi-view observations offer a natural way to reduce this ambiguity, as transmission is largely consistent across viewpoints whereas reflection is more view-dependent. However, directly exploiting such cross-view cues is difficult because reflections themselves corrupt the geometric correspondences required to establish reliable associations across views. To address this, we propose ReflectWorld, a geometry-routed diffusion framework that employs GeoRCA to establish reliable cross-view associations from corrupted multi-view geometry, while MGR-Diff drives differential generation with the distinct cross-view variations of the two layers. In addition, we introduce MVRefSep, a multi-view reflection separation dataset that captures the physical characteristics of reflections across views, establishing a benchmark for evaluating both transmission and reflection recovery. Experiments demonstrate that ReflectWorld achieves state-of-the-art performance. The code and dataset will be publicly released.