MirageDrive: Semantically Aligned Appearance Transfer with an Exemplar for Multi-View Driving Scene Generation

Controllable driving-scene generation provides scalable annotated multi-view data for autonomous driving, but its appearance diversity is often constrained by the training distribution. Expanding this diversity requires incorporating new appearance cues while preserving scene geometry and annotation alignment. We introduce MirageDrive, an exemplar-guided framework that transfers the appearance of a single reference image to multi-view driving scenes under geometric and semantic conditions. Semantic Region Control supplies dense target-layout guidance, while Exemplar-Guided Appearance Transfer combines class-wise AdaIN pseudo-target supervision with Region-to-Region Attention. The pseudo targets introduce exemplar-derived appearance statistics while retaining the target layout, providing spatially aligned supervision for jointly learning structural control and appearance transfer. Region-to-Region Attention directs reference features to matching semantic regions, coordinating appearance guidance with the target structure. Once trained, MirageDrive accepts new exemplars without additional model adaptation. Experiments on nuScenes demonstrate that MirageDrive achieves semantically aligned appearance transfer, improves controllability, and benefits downstream vision tasks through its generated data.