Gaussian Primitive Fusion for Any-Resolution Multimodal Images

Multimodal image fusion integrates complementary observations from different modalities into a unified representation. Existing methods typically estimate modality contributions on a common discrete grid. When source modalities have different native resolutions, constructing such a grid generally involves resampling, which may introduce artifacts and affect subsequent fusion decisions. To address this problem, we propose Gaussian Primitive Fusion (GPF), which reformulates heterogeneous-resolution fusion in a shared continuous Gaussian space. Instead of first converting source images to a common resolution, GPF directly fits their native-resolution observations with shared Gaussian geometry and modality-specific content representations, and learns modality contributions within the resulting continuous representation. Once optimized, the fused representation is rendered at the finest native resolution without resampling the sources. With heterogeneous-resolution inputs, GPF produces clearer target contours and finer textures, with less blurring and blocking artifacts than resample-then-fuse pipelines. Quantitatively, it achieves the best results on multiple infrared-visible benchmarks against state-of-the-art fusion methods equipped with interpolation or super-resolution frontends.