AeroGen: Parametric 3D Aircraft Generation from Structured Design Specifications

Recent advances in generative models have created new opportunities for design automation in graphics, manufacturing, robotics, and games. However, conceptual designs of aircraft require rigorous physical plausibility, functional constraints, and controllable edibility. Such precision remains a bottleneck for common text- or image-conditioned generative models. Motivated by a standard aircraft design workflow, we therefore adopt structured parameters as a complete and interpretable conditioning interface to describe component hierarchies, topological structures, and continuous geometric parameters. We propose a parameter-conditioned aircraft generation framework, where the Parameter Extractor encodes variable-length hierarchical design parameters into conditioning tokens for Diffusion Transformer, while an attribute predictor improves the model's sensitivity to aircraft-level parameters by recovering them from generated geometry. Experiments on our built benchmark with 40,348 samples across 71 classes show that our method outperforms image-conditioned baselines in geometric fidelity and shows promising schema-consistent and parameter-sensitive editing.