Any2Any: Unified Arbitrary Modality Translation for Remote Sensing

• URL: http://arxiv.org/abs/2603.04114v1
• Date: Wed, 04 Mar 2026 14:33:26 GMT
• Title: Any2Any: Unified Arbitrary Modality Translation for Remote Sensing
• Abstract summary: Multi-modal remote sensing imagery provides complementary observations of the same geographic scene.<n> Existing cross-modal translation methods treat each modality pair as an independent task.<n>We formulate Any-to-Any translation as inference over a shared latent representation of the scene.
• Score: 41.79987718008628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal remote sensing imagery provides complementary observations of the same geographic scene, yet such observations are frequently incomplete in practice. Existing cross-modal translation methods treat each modality pair as an independent task, resulting in quadratic complexity and limited generalization to unseen modality combinations. We formulate Any-to-Any translation as inference over a shared latent representation of the scene, where different modalities correspond to partial observations of the same underlying semantics. Based on this formulation, we propose Any2Any, a unified latent diffusion framework that projects heterogeneous inputs into a geometrically aligned latent space. Such structure performs anchored latent regression with a shared backbone, decoupling modality-specific representation learning from semantic mapping. Moreover, lightweight target-specific residual adapters are used to correct systematic latent mismatches without increasing inference complexity. To support learning under sparse but connected supervision, we introduce RST-1M, the first million-scale remote sensing dataset with paired observations across five sensing modalities, providing supervision anchors for any-to-any translation. Experiments across 14 translation tasks show that Any2Any consistently outperforms pairwise translation methods and exhibits strong zero-shot generalization to unseen modality pairs. Code and models will be available at https://github.com/MiliLab/Any2Any.

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