vehicle-damage

HERS presents a domain-adaptive diffusion framework for controllable, realistic, and trustworthy vehicle damage synthesis. The method decomposes complex damage generation into a set of risk-specific expert modules, each specializing in a particular damage type such as dents, scratches, broken lights, or cracked paint, and trained using self-supervised image–text pairs without manual annotation. These experts are later integrated into a unified diffusion model that balances specialization with generalization, enabling precise control over damage attributes while maintaining visual coherence. Extensive experiments across multiple diffusion backbones demonstrate consistent improvements in text–image alignment and human preference over standard fine-tuning baselines. Beyond visual fidelity, HERS highlights broader implications for auditability, fraud prevention, and the responsible deployment of generative models in high-stakes domains, underscoring the need for trustworthy and risk-aware diffusion systems in applications such as automated insurance assessment.