PhysEdit: Physically Consistent Image Editing via Causal Enforcement

While instruction-guided image editing has seen significantstrides, most existing models fail to preserve physical laws and causal-ity. This limitation stems from the sparsity of physical supervision sig-nals, coupled with the static, non-causal architecture of prevailing frame-works, which together hinder a deep comprehension of the physical world.To bridge this gap, we present PhysEdit, a novel framework that en-forces physical consistency in image editing through causal generationand physics-aware reinforcement learning. Specifically, by harnessing theintrinsic physical priors of video generative models, we curate a high-quality dataset, dubbed PhysEdit-50K, wherein every sample encapsu-lates the complex physical causalities and dynamics involved in the tran-sition between input and edited images. Building upon this, a specializedCausal Image Editor is devised to factorize the image editing processalong the temporal evolution of visual transformations, thereby internal-izing the underlying physical laws and boosting causal coherence. Simul-taneously, an additional regularization term is incorporated to maintainvisual consistency in unedited regions beyond the simple flow matchingobjective, mitigating the impact of undesired viewpoint shifts or cam-era motions in the dataset synthesized by video models. Furthermore,we integrate physics-aware reinforcement learning with a tailored fine-grained reward to steer the editing process toward better adherence tophysical laws. Extensive experiments on PICABench and ImgEdit-Benchdemonstrate that our PhysEdit significantly outperforms state-of-the-artbaselines, yielding physically consistent and plausible edits. Code is pub-licly available at: https://github.com/HiDream-ai/PhysEdit/.