SimSew: Towards Simulation-Ready Sewing Pattern Generation

In this paper, we focus on sewing pattern generation for digital garments. Existing methods are primarily trained with supervised objectives on ground-truth sewing patterns. However, such objectives do not directly constrain key factors related to simulation success, such as garment constructability and mesh quality. Tackling this issue, we present SimSew, a reinforcement-learning post-training framework for simulation-ready sewing pattern generation. SimSew directly incorporates downstream garment-construction feedback into post-training, encouraging the generator to produce patterns that are more suitable for physical simulation. To provide informative learning signals, we introduce failure-aware diagnostic rewards for unsuccessful generations and mesh-quality rewards for successfully constructed garments. The former differentiates diverse construction failures even when all sampled candidates are unsuccessful, while the latter further promotes high-quality meshes for downstream simulation. Extensive experiments demonstrate that SimSew improves downstream simulation readiness while preserving sewing-pattern quality. On GCD-MM, SimSew increases the simulation success rate from $71.89\%$ to $76.92\%$; reward-aligned evaluation further improves One-to-one Stitch from $86.76\%$ to $93.56\%$ and reduces Corner MAE from $18.97^{\circ }$ to $10.66^{\circ }$.