ASTAD: Asymmetric Style Transfer for Synthetic-to-Real Adaptation in Autonomous Driving

Synthetic data mitigates the data scarcity problem in au-tonomous driving perception. However, the synthetic-to-real gap leads toperformance degradation, hindering real-world model generalization. Al-though current methods leverage diffusion models for photorealistic styletransfer to bridge this gap, they critically ignore a practical asymmetry:while synthetic data possesses perfect pixel-level annotations, real-worldstyle reference images generally lack corresponding labels. Consequently,existing methods relying on symmetric semantic guidance suffer fromeither prohibitive annotation costs or severe semantic misalignment. Toaddress this dilemma, we formally propose a novel task: AsymmetricStyle Transfer for Autonomous Driving (ASTAD), which requires se-mantically consistent transfer using only labeled synthetic content andunlabeled real-world references. We further introduce the ASTModel, atraining-free two-stage framework designed to bridge this domain gapunder asymmetric constraints. ASTModel first extracts a coarse seman-tic prior from the unlabeled target, followed by dynamic prior refinementand class-consistent style injection during the denoising process. Exten-sive experiments demonstrate that ASTModel significantly outperformsexisting methods in downstream perception utility and structural fidelity,while offering a 3.2× inference speedup. This work aligns synthetic-to-real adaptation with practical constraints, holding the potential to ac-celerate the scalable deployment of robust autonomous driving systems.Code: https://github.com/Dingyi-Yao/ASTAD.