D3F-IR: Dual-Domain Deterministic Flow Matching for Visible-to-Infrared Translation

Visible-to-infrared translation provides a scalable pathway tosynthesize infrared imagery from abundant visible data, offering a prac-tical complement to costly cross-modal paired data acquisition. However,most existing generative approaches formulate the task as a unified pro-cess that implicitly entangles macroscopic thermal semantics with mi-croscopic spatial detail reconstruction. This entanglement often forces asub-optimal trade-off, leading to blurred target boundaries or physicallyunnatural thermal artifacts. In this paper, we reformulate the transla-tion process into two explicitly decoupled but tightly coordinated con-tinuous flows, proposing a novel framework named D3F-IR. Leverag-ing the smooth and deterministic trajectories of flow matching, D3F-IRintroduces a semantic-domain flow to model macroscopic thermal dis-tributions within a pretrained latent space, alongside an independentpixel-domain flow to generate fine-grained spatial structures directly inthe image space. To seamlessly coordinate the two domains, we design aPixel-space Velocity Predictor equipped with SEmantic-Aligned Modula-tion (SEAM) Layers, which establish explicit correspondence between se-mantic tokens and spatial patches through patch-wise alignment. Exper-iments on three benchmarks demonstrate that D3F-IR achieves strongperformance in both thermal semantic plausibility and pixel-level spa-tial fidelity. These results highlight that explicit dual-domain decouplingoffers a highly effective paradigm for high-quality visible-to-infrared gen-eration. Code is available at https://github.com/WanrenZeng/D3F-IR.