SGP2: Coarse-to-Fine Controllable Multimodal Remote Sensing Image Generation

Generating high-fidelity remote sensing images is severelychallenged by their specific overhead perspectives, complex structures,and rigorous physical attributes. Although recent controllable RS gen-eration methods have made progress, they are still limited by two keybottlenecks. The first is semantic conflict, where dominant global conceptsoften overshadow fine-grained details. The second is physical inconsis-tency, which easily leads to structural collapse and radiometric distortionduring the generation process. To address these issues, we propose SGP2(Synergizing Geometric and Physical Priors), a novel coarse-to-fine frame-work for controllable multimodal RS image generation. Specifically, toresolve semantic conflicts, we introduce the Grassmann Miner, whichconstructs dynamic geodesic trajectories on the Grassmann Manifold,enabling the model to gradually shift its focus from global backgrounds tofine-grained local details during the denoising process. To ensure physicalconsistency, we propose Physical Prior Expert, a module that dynamicallyaligns multi-level features from Visual Foundation Models and employsa frequency-aware Spectral Loss to enforce both amplitude and phaseconsistency. Furthermore, to bridge RS data gaps, we present MMEarth-1.5M, a 1.5 million text-image dataset featuring strictly co-registeredoptical and SAR pairs for robust cross-modal simulation. Our approach,without bells and whistles, achieves favorable performance in controllablemulti-modal remote sensing image generation task. The dataset will beavailable in https://github.com/cpy0029/MMEarth-1.5M.