AirplaneGen: Skeleton-Guided Generation of Remote Sensing Images with Multi-Instance Airplanes

Generating realistic and controllable aerial images is important for building and evaluating remote sensing recognition systems, especially when real samples of rare aircraft types or dense airport layouts are limited. However, airplane synthesis remains challenging for generic generative models. Aircraft have rigid and symmetric structures, and airport scenes often contain many closely spaced instances; as a result, existing models tend to produce distorted wings and fuselages or merge adjacent airplanes into ambiguous shapes. To address these issues, we propose AirplaneGen, a skeleton-guided latent diffusion framework for multi-airplane remote sensing image generation. AirplaneGen represents each airplane with an editable eight-keypoint skeleton and uses skeleton-derived soft masks to separate instance-level refinement from background-context modeling during denoising. To support this task, we construct MARS20, a benchmark with 2778 high-resolution aerial scenes and 16,673 airplane instances annotated with skeletons, categories, and contextual descriptions. Experiments on MARS20 show that AirplaneGen improves image fidelity, geometric consistency, and instance separation over representative controllable generation methods.