Follow-Your-Shape: Shape-Aware Image Editing via Trajectory-Guided
  Region Control
Authors: name: Zeqian Long, name: Mingzhe Zheng, name: Kunyu Feng, name: Xinhua Zhang, name: Hongyu Liu, name: Harry Yang, name: Linfeng Zhang, name: Qifeng Chen, name: Yue Ma

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
Follow-Your-Shape, 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 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 Scheduled KV
Injection mechanism that ensures stable and faithful editing. To facilitate a
rigorous evaluation, we introduce 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.