Follow-Your-Shape: 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
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.
689aabdffab6fdd2e52ac471
Zeqian Long
689aabdffab6fdd2e52ac472
Mingzhe Zheng
689aabdffab6fdd2e52ac473
Kunyu Feng
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Xinhua Zhang
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Hongyu Liu
689aabdffab6fdd2e52ac476
Harry Yang
689aabdffab6fdd2e52ac477
Linfeng Zhang
689aabdffab6fdd2e52ac478
Qifeng Chen
689aabdffab6fdd2e52ac479
Yue Ma
Follow-Your-Shape: 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
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.
Follow-Your-Shape framework uses a Trajectory Divergence Map and Scheduled KV Injection to enable precise and controllable shape editing in images while preserving non-target content.
flow-based image editing Trajectory Divergence Map token-wise velocity differences Scheduled KV Injection ReShapeBench shape-aware editing