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arXiv · 2508.08134

Follow-Your-Shape: Shape-Aware Image Editing via Trajectory-Guided Region Control

Zeqian Long·Mingzhe Zheng·Kunyu Feng·Xinhua Zhang·Hongyu Liu·Harry Yang·Linfeng Zhang·Qifeng Chen·Yue Ma
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Abstract

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.

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Zeqian Long, Mingzhe Zheng, Kunyu Feng, Xinhua Zhang, Hongyu Liu, Harry Yang, Linfeng Zhang, Qifeng Chen, Yue Ma. 2026-02-21. Follow-Your-Shape: Shape-Aware Image Editing via Trajectory-Guided Region Control. https://arxiv.org/abs/2508.08134

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