PhyEditBench: A Real-World Multi-Stage Benchmark for Physics-Aware Image Editing

While instruction-based image editing, enabled by multi-modal generative models, has advanced significantly, existing bench-marks lack comprehensive evaluation of physics-based reasoning—a criti-cal capability for handling real-world scenarios. To address this, we intro-duce PhyEditBench, a benchmark designed to assess the physical under-standing of editing models. Guided by a hierarchical taxonomy, we estab-lish 4 primary classes and 12 subclasses. It comprises 238 high-quality,high-resolution, real-world instances—meticulously extracted fromvideos to capture authentic physical dynamics, alongside 35 syntheticAnti-Physics instances. Our empirical analysis of current SOTA editingmethods exposes substantial limitations in their physics-based reasoning.We further propose a training-free baseline named PhyWorld that usestest-time scaling and a latent reduction strategy. PhyWorld outperformscomparable models and suggests that the video generation process caneffectively serve as a reasoning mechanism for image editing. The projectpage is available at https://github.com/Previsior/PhyEditBench.