EditAnyShape: Shape-Aware Image Editing via Trajectory-Guided Region Control

While recent flow-based image editing models demonstrate general-purpose capabilities across diverse tasks, they often struggle to specialize in challenging scenarios---particularly those involving large-scale shape transformations. When performing such structural edits, these methods either fail to achieve the intended shape change or inadvertently alter non-target regions, resulting in degraded background quality. We propose $\textbf{EditAnyShape}$, a training-free and mask-free framework that supports precise and controllable editing of object shapes while strictly preserving non-target content. Motivated by the divergence between inversion and editing trajectories, we compute a $\textbf{Trajectory Divergence Map (TDM)}$ by comparing token-wise velocity differences between the inversion and denoising paths. The TDM enables precise localization of editable regions and guides a $\textbf{Scheduled KV Injection}$ mechanism that ensures stable and faithful editing. To facilitate a rigorous evaluation, we introduce $\textbf{ReShapeBench}$, a new benchmark comprising 120 new images and enriched prompt pairs specifically curated for shape-aware editing. Experiments demonstrate that our method achieves superior editability and visual fidelity, particularly in tasks requiring large-scale shape replacement.