Sim-∞: Simple Simulations Improve Physics in Photorealistic Video Generation

Can deliberately simple, non-photorealistic simulations improve physics in photorealistic video generation? In this work, we show that a carefully designed fine-tuning recipe substantially improves the physical fidelity of a pretrained image-to-video model across multiple challenging physical skills, while preserving photorealism and general-domain video quality. We find that visually abstract, minimally textured simulations paired with detailed captions effectively teach targeted physical skills. Meanwhile, co-training with randomly sampled real videos preserves the model’s photorealism. Our experiments investigate three challenging physical skills: object permanence under occlusion, rigid-body collision dynamics, and deformable-body wave propagation. On a benchmark with independently constructed photorealistic physical scenes, a single model trained jointly on all three skills significantly outperforms a real-video-only baseline across all physics types. Physics-IQ-Verified increases from 47.98 to 49.80, while VBench++ scores remain closely matched. Ablations examine how the simulation-to-real-video ratio and caption design affect skill transfer and appearance retention. The method requires neither expensive photorealistic simulation rendering nor a simulator at inference time, offering a scalable way to expand the physical capabilities of video world models.