Follow-Your-Shape: Shape-Aware Image Editing via Trajectory-Guided Region Control
Authors: Zeqian Long
Mingzhe Zheng
Kunyu Feng
Xinhua Zhang
Hongyu Liu
Harry Yang
Linfeng Zhang
Qifeng Chen
Yue Ma
Venue: ICLR 2026 Poster (2026)
Keywords: image editing
Primary area: applications to computer vision, audio, language, and other modalities

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 $\textbf{Follow-Your-Shape}$, a training- 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 $\textit{\textbf{ReShapeBench}}$, 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.

Published as a conference paper at ICLR 2026
SHAPE-AWARE IMAGE EDIT- ING VIA TRAJECTORY-GUDDE REGION CONTROL
TOLLOW-YOUR-SHET
Zeqian Long 2t, Mingzhe Zheng 1f
Kunyu Feng', Xinhua Zhang, Hongyu Liu '
Harry Yang Linfeng Zhang Qifeng Chen ', Yue Ma 1
HKUST, University of Illinois Urbana-Chane SiranaaChampain, 3 Shanghai Jiao Tong University
Source Image
Edited Result
Single Object
parrot
hot.
coconul
A guitor
Letters 'OVE
robbit
coT
A Teddy bear
A backpack
A soccer ball
A lizord
Malti Objects
TwO binds
Two drogonflies
Two caps
Two robot birds...
Two origami birds
Two poker cords
Figure I: We propose Follow- Shapee, training- and mask-free image editing framework that excels at prompt-driven shape transformation. Our method enables flexible modification of arbitrary object shapes while strictly maintaining non-target content. The examples demonstrate both single- object and multi-object cases involving significant shape transformation.
ABSTRACT
While recent flow-based image editing models demonstrate general-purpose pabilities 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 back- ground quality. We propose Follow-Your-Sher Your-Shape, training- and mask-free frame- work 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 editable regions and guides a Scheduled KV Injection mechanism that ensures stable and faithful editing, To facilitate a rigorous evaluation, we introduce ReShapeBench, new benchmark comprising 120 new images and enriched prompt pairs specifically curated for shape-aware editing. Experiments demonstrate that our method achieves supe- rior editability and visual fidelity, partieularly in tasks requiring large-scale shape replacement.
Equal contribution Corresponding author

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Published as a conference paper at ICLR 2026
INTRODUCTION
Recent advances in generative models have greatly expanded the scope of visual generation. en- abling more controllable and realistic modifications across diverse scenarios. Image editing methods based on diffusion (Cao et al., 2023; Tumanyan et al., 2023; Feng et al., 2025) and flow models (Lipp man et al., 2022; Labs, 2024; Labs et al., 2025; Kulikov et al., 2025) have demonstrated considerable success in general tasks, yet they often fail when faced with complex, large-scale shape transforma- tions. These models can struggle to modify an object's structure as intended or may inadvertently alter background regions, which degrades the overall image quality. This limitation indicates criti- cal gap in their ability perform precise structural edits while maintaining the integrity of unedited content.
The primary cause for this limitation lies in the inadequacy of existing region control strategies (Zhu et al., 2025; Cao et al., 2023). Methods that rely on external binary masks are often too rigid and struggle with the fine details of object boundaries. Alternatively, strategies that use cross-attention maps to infer editable regions are frequently unreliable, as these maps can be noisy and inconsistent. While unconditional Key- Value (KV) injection can preserve background structure, lacks selectiv- ity and tends to suppress the intended edits (Avrahami et al., 2025; Wang et al., 2024). We argue that a breakthrough requires a new approach: one that derives the editable region dynamically from the editing process itself by analyzing how the model's behavior shifts between the source and target conditions.
To address these challenges, we propose Follow- Shal-See,u--,ppp,, a training- and mask-free framework for precise and controllable shape editing. As illustrated in Figure 2, the core innovation of our pipeline the Trajectory Divergence Map (TDM). The TDM generated by computing the token- wise difference between the denoising velocity fields of the source and target prompts. This map accurately localizes the regions intended for editing, which in turn guides a selective KV injection mechanism to ensure that modifications are applied precisely where needed while preserving the background.
However, directly applying TDM-guided injection across all denoising timesteps is suboptimal be- cause the TDM can be unstable in the early, high-noise stages of the process. We therefore introduce Scheduled KV Injection strategy that adapts its guidance throughout the denoising process. As visualized in Figure 2, this staged approach first performs unconditional KV injection to stabilize the initial trajectory, and only then applies TDM-guided editing has formed. This scheduling and staged editing pipeline ensures a more robust and faithful editing outcome compared to direct application.
coherent latent structure
To validate our approach, we introduce ReShapeBench, a new benchmark with paired images and refined text prompts specifically designed for evaluating large-scale shape modifications. Beyond this new dataset, we further evaluate Follow-Your-Shape on the public PIE-Bench (Ju et al., 2023) ensure generalizability. Follow-Yours Your-Shape achieves state-of-the-at performance on both bench- marks, demonstrating superior background preservation, text- eext-imae alignment, and overall visual quality, confirming its effectiveness in both shape-aware and general editing tasks.
Our primary contributions are summarized as follows:
A novel and training-free editing framework, Follow-Your-Shape that utilizes Trajec- tory Divergence Map (TDM) to achieve precise, large-scale shape transformations while preserving background content.
A trajectory- guided scheduled injection strategy that improves editing stability by adapt- ing the guidance mechanism throughout the denoising process.
A new benchmark, ReShapeBench, designed for the systematic evaluation of shape-awane aware image editing methods.
Reproducibility: We provide our dataset and source code facilitate reproducibility and future research.
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2 RELATED WORK
Region-Specific Image Editing. A central challenge in image editing is localizing modifications to specific regions (Barnes et al., 2009; Zhang et al., 2024b; Liu al., 2025a; Huang et al., 2025; Ma et al., 2025c;d; Long et al., 2025; Shen et al., 2025; Chen et al., 2025e; Feng et al., 2025). Early methods often relied explicit user-provided masks to delineate editable areas (Lugmayr et al., 2022; Avrahami et al., 2023; Chen et al., 2023; Xiong et al., 2025; Wan et al., 2024). While effective for certain tasks, this approach requires manual annotation, limiting its applicability. To ad- dress this, subsequent work explored techniques to infer editable regions directly from text prompts. Methods such as Prompt-to-Pronee manipulate cross-attention maps to associate textual tokens with spatial areas, enabling localized edits without explicit masks. Other approaches, such as DiffEdit (Couairon al., 2022), generate a mask by computing differences between diffusion model predictions conditioned on source and tar- get prompts. However, attention-based localization can be imprecise and unstable, especially during large-scale shape transformations where object boundaries change significantly (Pang et al., 2024; Cao et al., 2023). In contrast, Follow- Your-Shape provides a training- free and mask-free method for identifying editable regions directly from the model's behavior, avoiding the need for external masks or noisy attention maps.
(Hertz al., 2022) and Plug-and-Play (Tumanyan et al., 2023)
Structure Preservation via Inversion and Feature Reuse. Preserving non-target regions is equally critical for high-fidelity editing. and this is closely tied to the quality of the model's ' in- version process. For diffusion models, significant research has focused on improving DDIM in- version (Song et al., 2020) to better reconstruct a source image from noise. Previous works like null-text inversion (Mokady et al., 2023) and optimization-bart based methods (Wallace et al., 2023) aim to reduce the discrepancy between the reconstruction and editing trajectories. With the shift toward flow-based models, inversion fidelity has become even more important due to their deterministic na- ture. RF-Inversion (Rout et al., 2024) formulates the inversion process dynamic optimal control problem, while RF-Solver (Wang al., 2024) achieve more accurate reconstructions by incorporat- ing higher-order derivative information. Beyond improving inversion, another line of work focuses on explicitly reusing modules or features from the source image's generation process (Zheng et al., 2024: Ma et al., 2025a; Yan et al., 2025). Techniques based on Key-Value Value (KV) caching (Zhu al., 2025; Avrahami et al., 2025) or feature injection (Wang et al., 2024; Feng et al., 2025) enforce struc- tural consistency by propagating source-image features into the new generation process. In contrast to prior methods that rely on simple heuristics, Follow-Your Your-Shape employs a trajectory-guided scheduled injection strategy achieve more precise, content-aware control.
3 METHODOLOGY
Our goal is to enable precise object shape-aware editing while strictly preserving the background. Motivated by the limitations of existing region control strategies and the need for more adaptive mechanism, we introduce Trajectory Divergence Map (TDM) that quantifies token-wise semantic deviation between inversion and editing trajectories, as shown in Figure 2. The overall pipeline of Follow-Your-Shaer is shown in Figure 3.
3.1 MOTIVATION
Effective image editing requires a precise balance between introducing new content and preserv- ing the original structure. As illustrated in Figure 2 (left), traditional preserrng editing approaches often produce unstable denoising trajectories that deviate significantly from the stable reconstruction path, leading to severe structural degradation and undesired artifacts. Moreover, prior methods for localizing edits have notable drawbacks:
Rely on external tools (Kirillov al., 2023; Ronneberger et al., 2015), introducing overhead and a dependency on mask quality. Their rigid bound- aries hinder large-scale shape changes and often produce artifacts.
Binary Segmentation Masks:
Cross-Attentin Masks: Inferred from model's cross attention during the diffusion pro- cess, these maps are often noisy and inconsistent, proving unreliable for localizing edits, especially during significant shape transformations.
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RF Solver Inversion
RF Solver Inwersion
Latent P-Pi
Reconstruction
dr
Image x-Po
Latent z-pi
Cdring
Image x-Pr
vola, I. Suctee)
Editing
Stage
Stage
Stoge
Input Image
Inverted Latent
Vonilla Edited Image
Our Edited Image
Motivation for Trajectory Divergence Map (TDM) Guided Editing. Left: Vanilla editing methods (red) often produce unstable trajectories compared to the stable reconstruction path (orange). Right:
Figure 2:
Our staged editing approach better resembles the ideal editing path. The TDM visualizes the dynamically localized editing region across different timesteps, with different border colors corresponding to different stages.
Unconditional Feature Injection: This strategy preserves structure by globally injecting source features, but its lack of selectivity suppresses intentional edits, creating a conflict between editability and consistency.
To address these limitations, we propose a new approach from dynamical systems perspective. We posit that the semantic difference between the source and target concepts can be measured by divergence between their respective denoising trajectories. Based on this, we achieved precise and mask-free method (shown in Figure 3) to stabilize the editing trajectory and perform targeted, shape-awar modifications without relying on external masks or rigid heuristics.
3.2 FOLLOW- YOULOW-YOUR-SIUPE
We perform shape-aware editing through staged editing process that combines scheduled Key- Value (KV) injection with structural guidance, where the edit is localized by the Trajectory Diver- gence Map (TDM).
3.2.1 TRAIECTORY DIVERGENCE MAP
Our approach is grounded in the perspective of flow trajectories within the latent space, extending concepts from flow-matching frameworks to the inference setting. As illustrated in Figure (left), a standard reconstruction follows a stable denoising trajectory guided by the source prompt Cae. In an editing task, conditioning on target prompt Cigi alters the velocity field, causing the denoising trajectory to deviate from this initial path. We posit that the magnitude of this deviation spatially localizes the semantic difference between the two prompts. Regions intended for modification will exhibit significant divergence, while background areas will follow nearly identical trajectories. To formalize this, let (x, be the latent sequence from the source image inversion, and let YT be the corresponding sequence during the editing (denoising) process. We define the token-wing wise Trajectory Divergence Map (TDM) &, timestep f. the L2 norm of the difference between the velocity vectors predicted under the two prompts:
d.0 = 10 Cigi) - vo (x()) (.
where the velocity fields are evaluated at their respective trajectory latents, z, and x,. To enhance interpretability and prepare the map for temporal aggregation, apply min-max normalization
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Prompt:
Inversion
MM-
VAE
Stage
Xa
ON Proceserr
Stractindl and Sananic
Butrel
Trajectry Biorgente Mop
Zuriel Trajestrry
Shab lozteer
WW--DT
Decade
Denoising
EdiTPrompp: A woodin hoot
Flonting...
Text Tickens
Image Tokera
Pred Naise
Consatenated
Feature
Festare
Extrasted
Figure 3: Overview of our proposed pipeline. Given source image and the corresponding prompt, we first perform inversion to obtain the initial noisy latent code IT. The editing process then divided into three stages. In Stage 1, stabilize the initial denoising trajectory by reusing key- value (KV) features recorded during inversion and injecting them into the early denoising steps. In Stage 2, we compute Trajectory Divergence Map (TDM) by comparing denoising trajectories generated under the source and edit prompts, and process this map to precisely localize regions intended for editing. In Stage guided by the TDM, blended KV features are injected into the final attention blocks to introduce new semantics while preserving non-targed content. Simultaneously, ControlNet conditions are applied to provide auxiliary structural guidance.
across all spatial tokens at each timestep:
( ming 6,5))
(2)
max, 8:3) min, 6(2))
As shown in Figure (right), this produces a normalized TDM, ( which quantifies the localized editing strength on scale of [0, 1..
3.2.2 STAGED EDITING AND STRUCTURAL. GUIDANCE
Directly applying TDM-guided injection across all timesteps is suboptimal due to the instability of the TDM in early, high-noise regimes (Figure 2 right). Early latents provide weak and noisy spatial signals, which can mislocalize edits aggressive guidance applied too soon. To address this, we introduce scheduled injection strategy that partitions the N denoising steps into three distinct phases and adapts the guidance mechanism to the latent state: the first phase emphasizes stabilization, the second collects and aggregates TDM evidence while allowing exploration, and the third enforces structural and semantic conformance.
Stage 1: Initial Trajectory Stabilization. For an initial set of kneat timesteps, we perform un- conditional KV injection from source inversion path across all spatial tokens. This operation enforces a global reconstruction objective, equivalent to setting the edit mask Ms = O, which sta- bilizes the trajectory and prevents semantic drift while the latent representation z; is still dominated by noise. Intuitively, the model first anchors to faithful reconstruction manifold before any region- specific modification is attempted, reducing the risk of spurious changes to background layout or texture.
Stage 2: Editing and TDM Aggregation. Once a stable latent structure has emerged, we begin the editing phase over predefined window of timesteps N. During this window, we perform edit- ing by setting the edit mask Mg at every step, allowing the model to explore target-guided generation path. Simultancously, we compute and store the normalized TDMs ò, at each timestep within N. capturing the trajectory divergence guided by the source and target prompts. After this editing window concludes, aggregate the stored TDMs (ō,) across time to construct a temporally consistent and spatially coherent edit mask. Specifically, throughout the denoising process, a token that appears unchanged an individual timestep may still experience evolution at subsequent steps.
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Therefore, to ensure that the aggregation faithfully captures such temporal dynamics, employ softmax -weightd temporal fusion for each token i:
exp(o!")
800
Σ IEN
(6) at
8(0)
where
Exexexp(( (5(0)
To ensure spatial coherence and suppress noisy edges, the resulting map 8 is further refined via convolution with Gaussian kernel G, obtain Ms E [0, 1)"x::
Ms = * G
We observe that the distribution of values in Ms typically ex- hibits skewed unimodal shape (as shown in Figure 4), charac- terized by dominant background mode and a long-tailed fore- ground response. Such a distribution well suited for Otsu's method (Otsu et al., 1975), which selects the threshold T that that maximizes the inter-class variance of values. Formally, for a candidate threshold let wo, po and w1, f1 denote the class probabilities and means of the background (Ms S 7) and foreground (Ms > T), respectively. The between-class variance is defined as:
Smoothed values distr buttiottion
Obu
20-
2.
0.2
0.5
1.
Figure 4: Distribution values of Mg. The red dashed line indicates the Otsu threshold T.
(T) = (o(() (T) (HO(T)) - 2
(5)
The optimal threshold is then given by:
arg max (T).
(6)
The final binary mask Mg is thus obtained by applying this threshold:
Ms 1 Ms > r+)
Ms E {0, 1)
where [[:] denotes the indicator function.
Stage3: Structural and Semantic Conformance. Our framework enforces structural confor- mance by jointly leveraging TDM-guided feature injection for background preservation and Con- trolNet residual conditioning for stabilizing structural patterns. The mask MM obtained in Stage modulates the fusion of Key- Value features, activating the target features (Ketss Vigi) in edited re- gions and reverting to the source features (Kinv, Vinx elsewhere. This feature-blending operation is formulated as:
K V+ < Ms {K V (1 Ms) {Kinh, Vine..
For structural guidance, ControlNet conditions the process on structural information Crond by inject- ing residual stream into each block of the denoising model vp. For latent representation z, at given block, the output z is computed as:
z Block(z,) + B. ControlNettlooc(2z, Crund);
(9)
where 3 controls the guidance strength. Concurrently, our feature injection mechanism builds on RF-Edit's background preservation by replacing the standard self-attention with a TDM-guided vari- ant. The modified attention output F is computed using the blended key-value pairs from Eq. 8:
F' = Attention(Q"* K V*).
(10)
This synergy between ControlNet's geometric enforcement and our TDM- semantic preser- vation enables precise, high-fidelity edits. The algorithmic implementation can be found Algo- rithm 1 in Appendix.

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Tmage Collectio Anaonation
The an Condiant
Stop Checklar Validattor
Step Eeiriy Aci Fackeging
Searcs
clede vittgge drivs
diee htd
Wuth Siyper
3.
The bechlarnnd Tenune forected
Algrnee
The catril creariaen sureea
redder Sppreo
ReShopaBlench
Qieen
Queen
Figure 5: Construction Process of ReShapeBench. Note that images in Step 3 are generated after benchmark construction to serve as visual references. Checklist validation is performed on prompt.
4 RESHAPEBENCH: A BENCHMARK FOR LARGE-SCALE SHAPE TRANSFORMATIONS
Existing benchmarks for image editing (Wang et al., 2023; Ju et al., 2023; Zhang et al., 2023) are not tailored to the demands of shape-aware editing, where the goal to change object geometry while preserving the surrounding background. In particular, PIE-Bench (Ju et al., 2023) (700 images) uses concise prompts that often lack spatial or structural detail, and it aggregates het- erogencous tasks (object replacement, stylization, background modification) rather than isolating shape transformation as a first-class target. These properties make it difficult to diagnose whether method truly performs structural change relies side effects such as texture shifts or back- ground re-synthesis. We therefore introduce ReShapeBench, a benchmark that centers on mask- free, prompt-driven shape transformation with paired prompts and controlled background settings. This design isolates the factors relevant to structural change and reduces confounds from style or background alterations, enabling a targeted and reproducible evaluation protocol. It also serves as a targeted complement to existing evaluation suites, providing shape-focused test cases that fill the gap left by current general- purpose editing benchmarks.
Overview.
Benchmark Construction. ReShapeBench contains 120 newly collected images split into three subsets: 70 single-object scenes for precise shape editing, 50 multi-object scenes for targeted mask- free edits, and general evaluation set of 50 images that combines samples from both subsets with curated PEE-Bench cases to assess generalization. All images are standardized to 512 x 512 to normalize spatial scale and reduce variability across methods and backbones. Each new image is paired with two distinct shape transformations, yielding 240 editing cases across the single- and multi-object subsets, plus 50 cases in the general set, for total of 290 shape-aware editing cases; this pairing increases task coverage and controls difficulty by varying the magnitude of structural change. Source-target prompts follow a structured template and differ only in the foreground object description, which stabilizes eext-to-imag alignment while holding background fixed. All prompt pairs are generated by Qwen-2.5-VL (Bai et al., 2025) and validated by human raters ensure alignment and that the transformation satisfies the predefined shape criteria; ambiguous cases are double-checked to maintain consistency. Figure and the Appendix B present the construction pro- cedure and sample cases, including the selection checklist and prompt schema used during curation.
5 EXPERIMENT
5.1 EXPERIMENTAL SETUP
We use the open-source FLUX. 1-[de]] model (Labs, 2024) as the base and run all experiments in PyTorch on an NVIDIA A100 (40 GB). We set the number of denoising steps to 14, guidance scale to 2.0, and kifrott to 2. We evaluate both ControlNet-free and ControlNet-enableed variants of our method. If enabled, we apply multi-Controlle the normalized denoising interval [03], with respective strengths 2.5 and 3.5. Unless otherwise stated, we keep the same inference scheduler and tokenizer as the official release and fix random seeds for reproducibilityy; additional implementation details and runtime/memory profiles are pro- vided in Appendix C.
conditioning with depth and Canny branches over
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Source
Ours
MasoCtri
PnPIneersion
DiT4Edit
RF-Edit
FlowEdit
KV-Edit
FLUX Fill
FLUX1 Kontext
K with saf-hhaped loft of
with anulesed lafte
grow
h
Figure 6: Qualitative comparisons on various shape-aware editing cases. Follow-Yon Your-Shape successfully performs large-scale shape transformations while preserving the background, demon- strating advantages in both editing ability and visual consistency over existing baselines.
5.2
COMPARISON WITH BASELINES
5.2.1
QUALITATIVE COMPARISON
We compare Follow-Yore Your-Shape with diffusion-based and flow-based methods. Diffusion-based baselines include MasaCtrl (Cao al., 2023), PnPlnversion (Ju al., 2023), and Dit4Edit (Feng et al., 2025), which modulate attention and conditions during the diffusion process. Flow-based baselines include RF-Edit (Wang et al., 2024), FlowEdit (Kulikov et al., 2025), KV-Edit (Zhu al., 2025), FLUX.1 I Fill (Labs, 2024), and FLUX.1 Kontext (Labs et al., 2025), which build on Rec- tified Flow. FLUX.1 Fill is designed for prompt-based masked image completion, while FLUX.1 Kontext leverages context-token concatenation for in-context editing. Figure 6 shows that Follow- Your-Shape achieves stronger shape-aware editing and background preservation. Diffusion-basee methods tend to degrade the background under structural edits and may fail on high- magnntude shape changes, while flow-based methods produce higher-quality images but still exhibit detail jitter, ghosting, incomplete transformations in difficult cases. Follow- Miclo--You-Soe Your--oorrppppp performs large-scale shape transformations while preserving non-target regions.
5.2.2 QUANTITATIVE COMPARISON
We conduct quantitative evaluations on both ReShapeBench and PIE- against diffusion- and flow-based baselines to assess both shape-aware editing and general editing performances. To ensure fairness, we use identical source and target prompts and the same number of denoising steps across methods. Because follow RF-Solver with second-order scheme, double the number of steps for methods without a second-order update to match the number function evaluations (NFE). We disable the ControlNet modules isolate the effect of TDM-guided editing.
As shown in Table 1, we evaluate background consistency with PSNR (Huynh- Thu & Ghanbari, 2008) and LPIPS (Zhang et al., 2018), image quality with the LAION Aesthetic Score (Schuhmann et al., 2022), and text alignment with CLIP similarity (Radford et al., 2021). Appendix B.3 demon- strates the implementation details, including the preprocessing and metric computation settings used
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Table I: Quantitative comparison with state-of-the-art methods ReShapeBench and PIE- Bench. Bold and underlined values denote the best and second-best results respectively.
Dutasets
ReshapeBench
PIE-Bench
Image Quality
Background
Image Quality
Background Preservatinn
Text
Metrics
Preservation
Align
Align
Methods
7
PSNR
LPIPS 108 4 CLI Sim 1
AS
PSNR 7
LPIPS x
CLIP Sim
MusoCuu(Caa et al., 2023)
5.83
23.54
125.36
20.84
5.61
21.58
130.71
19.53
Pu et 2023)
6.11
24.77
102.91
19.23
5.94
22.69
108.45
24.62
DisFFdirFeng al., 2025)
83.75
6.03
22.74
23.87
RE-Edi Wang al.
17.53
6.49
31.97
15.34
Hlwwkdi(Kulllon al., 20225)
6.42
32.46
18.92
28.94
6.37
32.68
16.42
28 91
KV-EditZha ol., 2025)
6.51
34.73
16.42
26.97
6.47
33.45
13.72
28.
HLUX, IKb. 2024)
31.57
19.04
28.75
6.33
32.76
17.43
26.59
PLUX, Konttxnt als et al,, 2025)
6.53
32,91
18.35
28.53
6.47
34.91
14.62
28.79
Our (w/l ConmolNeti
6.52
34.85
9.04
32.97
6.49
35.62
9.74
32.47
35.79
8.23
33.51
for all methods. Our method outperforms baselines across metrics. Additionally, without the ControlNet module does not lead to significant degradation in editing performance, indicating the improvements of our model are independent of this module. The region-controleed editing strategy improves fine-grained shape-aware editing while the mask Ms preserves background content.
5.3
ABLATION STUDY
Initial trajectory stabilization and the timing and strength of ControlNet conditioning have the largest impact on editing performance. We therefore ablate these components: the former regulates early trajectory stability, while the latter controls structural guidance during mid-late steps.
5.3. INITIAL TRAJECTORY STABILIZATION
Table 2: Ablation study on different kifrott
To assess the role of initial trajectory stabilization, we vary the number of stabilization steps krrnn from O 4. As shown in Figure 7 (i), small kiron leads to drift and structural deviation, while large kfron re- stricts the intended shape change. Table shows that larger kinm improves background preservation but reduces CLIP similarity. indicating a trade-off be- tween stability and editability. knom 2 provides the best balance, yielding stable trajectories while maintaining sufficient freedom for large shape transitions.
Image Quality
Background Preservation
Text Align
Rine
Aesthetic Score
PSNR LPPPS
CLIP Sim
32.79
6.55
34.38
9.88
32.56
6.57
35.79
8.23
33.71
31.25
29.41
30.41
12.37
27.66
Keve
35
25
Scom
POUL
LIFES xtab
CLI Si
p..
0.50
Figure 7: (i) Ablation study on Anrnnt (ii.a) Control- Net conditioning applied within five subranges of the normalized denoising interval [0, 1]. (ii.b) Con- trolNet conditioning strength of the depth and canny branches, denoted as (depth, canny).
Figure Visualization of ablation on Kfront- While the aesthetic score remains relatively stable, PSNR, LPIPS, and CLIP reveal clear trade-off between editing strength and background preservation.

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5.3.2 CONTROLNET CONDITIONING TIMESTEP AND STRENGTH
To explore the effect of ControlNet conditioning timestep, vary the injection interval within the normalized denoising range [0, 1I. Figure 7 (ii.a) shows that earlier injection yields better results, as latent features are less noisy and more receptive structural guidance. We also vary the depth and Canny strengths. As shown in Figure 7 (ii.b), moderate values (e.g., (2.5, 3.5)) best balance structure preservation and editability, while overly weak or strong signals under- or over-constrain the edit. These results suggest that early, moderate guidance best stabilizes geometry without suppressing desired semantic changes in the edited regions.
CONCLUSION
We introduce Follow-Your Your-Shape, a framework that enables large-scale object shape transformation by using novel trajectory-based region control mechanism. Our method achieves precise, mask- free edits while preserving background integrity by dynamically localizing modifications through Trajectory Divergence Map with scheduled injection. To properly evaluate this task, devel- oped ReShapeBench, a new benchmark tailored for complex shape-aware editing. To the best of our knowledge, Follow- Follow-Your-Shape Your-Shape is the first work systematically address prompt-driven shape edit- ing. Extensive qualitative and quantitative experiments validate its state-of-the-ar performance on the proposed benchmark. Our work thus opens promising new avenues for controllable generation in image, vidco, and 3D content creation.
ETHICS STATEMENT
Our method is designed for image editing tasks involving large shape transformations. To mitigate potential misuse such as malicious editing, the final model output incorporates an NSFW filtering component. The benchmark dataset used in this work is entirely collected from publicly available sources (https: : //t////www /www pexels com/), which explicitly permit free usage and modification of images and videos. A processed version of the dataset will be released solely for research purposes after further optimization. Beyond these considerations, our work does not involve any sensitive or personally identifiable data.
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CONTENTS
1. Introduction
2. Related Work
3. Methodology
3.1 Motivation
3.2 Follow-Your-Shape
4
3.2.1 Trajectory Divergence Map
4
3.2.2 Staged Editing and Structural Guidance
4. ReShapeBench: A Benchmark for Large-Scale Shape Transformations
5. Experiment
6. Conclusion
10
A. Preliminaries
18
A.1 Rectified Flow
18
A.2 KV Injection
18
B. Additional Details on ReShapeBench Construction
20
B.1 Shape Transformation
20
B.2 Image and Prompt Examples
21
B.3 Evaluation Metrics
22
C. Additional Implementation Details
23
C.1 Pseudocode
23
C.2 Hyperparameters
24
C.3 Running Time and Memory Usage
24
C.4 Post-hoc Analysis of Structural Localization Signals
25
D. Limitations and Future Work
26
D.1 Failure Cases
26
D.2 Extending to Video Editing
26
E. More Editing Results
27
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A PRELIMINARIES
A.1 RECTIFIED FLOW (RF)
Let Po and Pi denote the source and target distributions, respectively. Flow Matching (Lipman al., 2022) models the transport between them by learning a time-dependent velocity field v(t, x) that defines a continuous transformation (x) via the ordinary differential equation:
d()(()) = u(t, (x)),, Vu(z) ~ Po, 1/1 (z) ~ P1-
(11)
dt
Rectified Flow (RF) (Liu et al., 2022) simplifies this by assuming linear trajectory between Xo ~ Po and X1 Pr:
X, (1 Xo + 1X1, E [0, 1].
(12)
with the associated velocity field becomes:
(X,,t) = X1 Xo-
(13)
The model is trained by minimizing the conditional flow matching loss:
CCFM = Ex..X,, [lo(X,, t) - (X1 - Xo)]l
(14)
During inference, the learned velocity field is used to generate new samples by solving the reverse- time ODE:
dx2 - =
(15)
di
starting from a sample X1 ~ NV( I). Since closed-form solution is not available in general, we perform numerical integration over a discretized set of timesteps {t: 1 A standard choice uses first-order solvers such as Euler or Heun's 's method to approximate the trajectory:
X Xx, h v(N,,, t.),
(16)
where / 4 4--1 is the integration step size.
However, first-order solvers can suffer from numerical instability and truncation error, especially in high-dimensional generation tasks. Several recent works (Lu et al., 2022: Rout al., 2024; Lv al., 2025; Chen et al., 2025c; Wang et al., 2024) explore higher-order integration strategies or adaptive solvers to improve generation fidelity. Specifically, RF-Solver (Wang et al., 2024) introduces second-order update derived from Taylor expansion of the velocity field:
_ = Xt - - (4) h22 a,v(X,,,
(17)
where (x,,X,, t2) is the time derivative of the learned velocity field. This correction term reduces local integration error and leads more accurate inversion and sampling trajectories, which is par- ticularly important for downstream editing tasks that require high structural fidelity.
A.2 KV INJECTION
Key-Value Value (KV) injection is adapted from the KV caching mechanism originally used in Trans- formers (Vaswani al., 2017) accelerate autoregressive inference (Pope et al., 2023). large language models, cached key and value tensors allow reuse of past attention computations, enabling efficient decoding without recomputing earlier tokens.
When extended from language to vision models, KV reuse often generalizes beyond strict token caching. In U-Net based models (Cao et al., 2023; Qi et al., 2023), a common practice is to reuse intermediate attention maps or inject features derived from the inverted source image into self- attention layers. This feature-level injection plays a role similar to KV caching in LLMs by enforc- ing spatial consistency and anchoring the generative process the source structure. In DiT-based architectures (Peebles Xie, 2023), this idea extends to reusing value (V) matrices or full KV pairs, providing finer-grained control over how structural information is preserved during denoising.
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To reduce memory cost and avoid limiting foreground flexibility, recent work such Stable- Flow (Avrahami et al., 2025) explores the vital layers within DiT crucial for image formation. Therefore, by only reusing KV pairs in subset of layers, it can balance structural fidelity and editability while effectively reducing memory usage.
In work, demonstrate that KV injection provides modular and interpretable mechanism for controllable image editing and particularly effective in shape-aware tasks where edits must stay localized without affecting the broader scene.
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B ADDITIONAL DETAILS ON RESHAPEBENCH CONSTRUCTION
B.1 SHAPE TRANSFORMATION
While recent image editing models exhibit strong general-purpose editing capabilities, the concept of shape transformation remains ambiguous in the literature. In practice, object modifications usually include detail adjustments, color changes, or limited geometric variations, often relying on masks or ControlNet images; however, such operations cannot be explicitly framed as shape transforma- tions. When constructing ReShapeBench, we need clear definition categorization of shape transformation guide data curation and enable meaningful evaluation.
From a gcometric perspective, shape transformation a structural change beyond local affine opera- tions such scaling, rotation, minor warping. It reconfigures the object's s global contour and part topology, and may involve shift in semantic class. At the same time, the transformed object must remain spatially coherent in the scene, occupying similar anchor position and continuing serve as the subject context. Guided by these principles, we propose four criteria cross- cross-contour, cross- semantic, structural transition, and subject tontinuity-that together capture the essential properties of shape transformation.
The object's boundary undergoes a substantial change, exceeding local warping or affine resizing. This captures large-scale alterations to the external shape.
Cross-contour:
The transformation shifts the object into different semantic class, in- dicating a categorical rather than attributive change, while preserving overall scene coher- ence.
Cross-semantic:
Structural transition:
The internal part topology is reconfigured, requiring modifications across multiple components instead of only simple attributes such as color or texture.
Subject continuity: The transformed object retains spatial anchor and role the scene, remaining contextually consistent despite the change in shape and semantic.
Almost every editing case in the paper be classified shape transformation. Figure provides an additional illustrative example. Note that we exclude posture or viewpoint changes (standing > sitting), these involve articulation The most challenging part in shape transformation that it requires the model to localize and rein- terpret object shape while maintaining consistency in background and composition. Unlike existing editing benchmarks that cover diverse editing tasks, our benchmark emphasizes large-scale shape transformation under prompt guidance, without relying on masks external conditioning.
perspective variation rather than structural transformation.
swan
boat
flamingo
crocodile
turtle
Figure Visualization of Shape Transformation. The object's contour, semantic, structure changed while ensuring its subject continuity.
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B.2 IMAGE AND PROMPT EXAMPLES
As described in Section 3.2.2, the benchmark constructed from collected images divided into single-object and multi-object categories. Specifically, single-object cases broadly cover four cat- egories-nature animals, indoor, and outdoor scenes; multi-object cases can also be categorized into indoor and outdoor scenes. Figures 10 and 11 provide representative samples. For each image, source and target prompts follow the four-sentence template, which is illustrated in Table 5, 6, and
Figure 10: Single-Object Cases in ReShapeBench
Figure 11: Multi-Object Cases in ReShapeBench
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B.3 EVALUATION METRICS
We use four metrics grouped under three aspects: image quality, background preservation, and text- image alignment.
Aesthetic Score (AS) measures the perceptual quality of the generated image by indicating how well the visual content conforms to natural image statisties. We compute AS with the LAION aes- thetic predictor (Schuhmann et al., 2022), which applies a linear estimator on top of CLIP embed- dings'. AS helps detect unnatural boundaries blending artifacts that may occur during large-scale shape transitions and indicates how well the new shape integrates into the scene.
For background preservation, we adopt two widely used metrics that capture different aspects of visual similarity. Peak Signal-to-Noise Ratio (PSNR) measures low-level pixel fidelity, while Learned Perceptual Image Patch Similarity (LPIPS) evaluates perceptual similarity based on deep feature representations. Since we do not assume access to ground-truth masks and the edited shape can vary across models, we localize the edited region by applying a fixed-size box centered on the subject to occlude the foreground and compute similarity over the remaining background area. This heuristic enables fair evaluation how well the unedited content is preserved.
Finally, to assess text-image alignment, compute CLIP similarity between the generated image and the target prompt as an embedding-basee measure semantic consistency.
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C ADDITIONAL IMPLEMENTATION DETAILS
C.I PSEUDOCODE FOR FOLLOW- YOUR-SHAPE
Algorithm Regiin-Connrolled Editing
Input: Inference steps T. Predicted velocities (vix((x))) ), Uige (2g)(2)))) MT= Source inversion features (Ka,, Vith Jt=0 target prompt Cigs schedule phase durations {kitrons, kisil)
1: N + 0
b Editing window set
2: for t = T down do
3:
if T kirront then
4:
Ms f-
5:
else if T kiront 2 t >> kiait then Uugalzi (8)
6:
8 +
t) - Usic (x) t, Case)
7:
8
5,t) -min, 67.)
TDM Computation
max; 61 -miny 62
8:
Ms + 1
9:
N t [
else
(xp(6 ((0)
11:
800)_
Σvet Eneer exp(6
12:
Mg go x
TDM Aggregation
T + arg max PIMs S P|P > r1 (E[Ms Ms S 7/1 E[Ms Ms > +')) 2
13:
Mg 1|Ms >
15: end if
K + Ms K + (1-Ms) @ Kime
V' Ms @ V (1 Ms) ④ VilNv
18: end for
return K* V
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C.2 HYPERPARAMETERS
All hyperparameters used in our experiments can be seen in Table 3. The table is divided into two groups: General and Model Specific hyperparameters. Since our method is training-free, the only general hyperparameters on FLUX are the inference step and the guidance scale. Note that because we adopt RF solver, and the solver skips the final timestep. the number of function evaluations (NFE) is (15 - 1) x 2 = 28. We have already performed the ablation studies about Kfront and ControlNet parameters (Refer to Section 5.3). kigi controls the number of late timesteps where source features are injected; setting it to 2 or 3 ensures that the model has sufficient time to perform editing while still converging to the target distribution. The softmax scale regulates the sharpness of the aggregated TDMs, with higher value producing crisper mask, while Gaussian smoothing removes spurious noise to ensure the continuity of the mask. The injecting DiT block indicates the starting index of feature injection, and we found that injecting from block 19 onward achieves the best trade-off between editing quality and memory efficiency. This hyperparameter is relatively flexible and can be slightly adjusted depending the particular editing case. To ensure reproducibility, also provide the exact hyperparameters used for the specific examples shown in the paper.
Table Hyperparameters
Hyperparameter
Value
General Inference Step
15
Guidance
Model Specific
Frrm
2
knai
3
Softmax scale (temperature)
Gaussian smoothing o
0.7
Injecting DiT block (start idx)
19
ControlNet Timing
[0.1, 0.3]
ControlNet Strength
(2.5, 3.5)
Table 4: Hyperparameters used for the results displayed in the paper.
Task
kuil
CN type
CN timing
CN strength
parrot hat, coconut, guitar, LOVE, rabbit boy - cat, Teddy, backpack. soccer. lizard bird dragonflies, caps, robots, pokers
2
None
NA
NA
1
Depth & Canny
10.1, 0.3)
[2.5, 3.5]
2
3.
None
NA
NA
swan boat, flamingo, crocudile, turtle leaf latte + lion, horse dragon, cockrail ball
3
3.
Depth
[0.1. 0.3)
0.6
2
None
NA
NA
C.3 RUNNING TIME AND MEMORY USAGE
Running Time Analysis. The method requires an additional diffusion pass each step to com- pute the second-order prediction, resulting in a total of 28 NFEs. As introduced in Section 5.1, we conduct our experiment on an NVIDIA A100 (40 GB). and the average rumning time for one image (averaged across multiple trials) approximately 65.3 seconds. The computational cost is comparable to existing methods such as FlowEdit and RF-Solver-Edit. Since our work focuses on controllability and structural fidelity rather than acceleration, no additional optimization loops are introduced. Speed optimization is orthogonal to our contributions.
Memory Usage. During inference, the method stores a set of KV features and TDM maps on CPU memory, which amounts to approximately 12 GB in total. This cost is incurred only once during inversion and does not grow with the number of denoising steps. The GPU memory us- age remains stable around 25 GB throughout editing. comparable to existing flow-based editing pipelines. Since the method does not introduce any additional training procedures, this overhead is not a limiting factor in practice.
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C.4 POST-HOC ANALYSIS STRUCTURAL LOCALIZATION SIGNALS
To better understand whether the proposed Trajectory Divergence Map (TDM) provides mean- ingful structural signal, conduct a post-hoc analysis from complementary perspectives: (1)
mask-level comparison and (2) comparison against cross-attention maps.
Mask-level comparison. Our first goal is verify that TDM indeed identifies the correct struc- tural region to be edited. Using the crocodile case from Figure 9, we construct a pseudo ground-truth region by taking the union of the source mask and the target mask (generated by SAM (Kirillov et al., 2023)), and then downsampling it to the latent resolution. This union mask reflects the intu- itive region of change: areas covered by either the original foreground the edited foreground are exactly those that should be modified, while the background should remain unchanged. We com- pare this downsampled pseudo mask with our mask Mş. As shown the top row of Fig 12, the two maps exhibit similarity in editable areas, providing intuitive signal that TDM captures the correct semantic region for editing without relying on external supervision. This supports our claim that TDM provides an effective and interpretable estimate of "where" the model intends to apply shape transformation.
TDM vs. cross-attention. We further compare TDM with cross-attention maps FLUX DiT. We visualize representative timesteps from the most responsive attention block. Because cross-attention activation tightly tied to prompt tokens, localization quality depends heavily on which word has the strongest response. In practice, identifying a single "correct" token is not feasible feasible-responsee responses vary significantly across tokens, heads, and layers-Se so we use a ooftmax-normalized variant over all text tokens. Even under this stabilized setting, cross-attention remains spatially noisy and useless with the true editing region. These attention maps are extracted when using TDM-guided region control. Maps without guided control substantially noisier and therefore omitted for clarity. In contrast, TDM offers a much cleaner and more direct indication of structural change, and is obtained in far simpler manner by relying solely trajectory differences.
do
Mask-Level
Comparison:
TDM:
Gross Attn:
Figure 12: Post-hoc analysis of structural localization signals.
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D LIMITATIONS AND FUTURE WORK
While method demonstrates strong performance in shape-aware image editing, it also comes with certain limitations that suggest directions for future work.
D.1 FAILURE CASES
Our method can be sensitive to prompt ambiguity and imprecise editing instructions. Since the edit- ing behavior is driven entirely by prompt-guided inversion and denoising trajectories. the quality of the editing outcome depends on how clearly the intended modification is specified in the text prompt. When editing instructions specificity, the model may struggle to determine where and how strongly to apply the modification. This often leads weak, diffuse, inconsistent edits, particularly change not explicitly specified by the prompt. For example, prompts that describe abstract trans- formations or rely on implicit assumptions about the editing target may result in edits that do not match user expectations (see Figure 13). Clear and well-defined editing descriptions that explicitly identify the object to be modified and the desired transformation are therefore important for reliable performance.
vague, lack clear semantic targets, have low discriminative
where the intended
Source Prompt: A small stack of smooth black stones stands on the right side of calm reflective surface, a single round stone placed besice it the The sky glows worm pastel colors at sunset, and the water reflects the tranquil scene.
Torget Prompt: A small stock of smooth [white] stones stonds on the right side of calm reflective surface, with a single round stone placed beside it the left. The sky glows with warm pastel colors sunset, and the water reflects the tranquil scene.
Figure 13: Failure Case
D.2 EXTENDING TO VIDEO EDITING
Video generation (Ma al., 2024a; 2026; 2025f;e;;; 2024b; 2022; Chen al., 2025d; Wu al., 2024; Li et al., 2025; Gao et al., 2025; Song, 2022; Song et al., 2023; Qiu et al., 2024; 2025c; Zhang et al., 2025f; 2024a; Song et al., 2025; Qiu et al., 2025b;d;a; Song & Zhang, 2022; Zhang et al., 2025e;e;b;aıd; Fan et al., 2025; Cai et al., 2025) an important research direction for practical ap- plication. We also explore extending Wan 2.1 (Wan et al., 2025), an open-source video generation model that uses Rectified Flow. While our method can principle be applied duces major challenge, where the TDM becomes much less stable and effective when extended across time, as shown in Figure 14. In particular, the spatial editing regions indicated by TDM often fluctuate across frames, leading to inconsistent incomplete transformations in the resulting video. Since well-defined and temporally consistent TDM crucial for successful editing, future work may consider strategies such as temporally-aware TDM construction, explicit disentanglement of spatial and temporal components in the denoising trajectory.
shape-aware editing framework the video domain using
frames, we find that the temporal dimension intro-
Figure 14: TDM of Wan2.1 video editing at a single timestep across different frames.
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D.3 EXTENDING TO AGENT-BASE EDITING
An important future direction to extend our framework toward an agent-based video editing paradigm (Lin al., 2025a; Wang al., 2025c;b;a; Lin al., 2025b;c; Liu al., 2025b; Zhao et al., 2024; 2026; 2025; Chen et al., 2025b;a). Instead of treating editing as a single-pass gen- eration problem, we envision a multi-agent system in which specialised agents are responsible for temporal consistency verification, semantic alignment, motion-aware refinement, and user-intent in- terpretation. Through iterative reasoning and tool invocation (e.g.. tracking, depth estimation, or diffusion-basse based refinement modules), such agents could decompose complex editing instructions into structured sub-tasks and dynamically adjust intermediate results. This formulation would en- able adaptive long-horizon editing, reduce error accumulation across frames, and improve controlla- bility under ambiguous prompts. Moreover, integrating memory mechanisms for cross cross-frame state tracking may further enhance temporal coherence and enable interactive, feedback-driven video ma- nipulation.
E MORE EDITING RESULTS
We present additional shape-awart editing results in Figure 15 and Figure 16. We also present general task editing results in Figure 17.
F THE USAGE LARGE LANGUAGE MODELS
In this paper, the usage of the LLM mainly falls into the following aspects:
Grammar checking and format optimization: In the paragraphs of the paper, LLMs are used for grammar eтrоr checking and format checking of charts and figures.
Language polishing: The text description part of the paper uses LLMs to polish and opti- mize the language expression.
Prompt Generation:
We use Dw5e--2.5-V VL to generate paired source and edit prompts for images in constructed benchmark."
All authors are responsible for the content generated by the LLMs.
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Love
leaf-shaped
love-shoped
kiated
heart
golden star
piggy
mountain bike
rusty bike
wooden box
boy riding horse
cat
balloon
lion
turtle
ancient book
soil chunks
eggs
watermelon
tiger
divine statue
cat
lion
alien
coffee
crystal ball
pen
heart-shaper
round-shaped
brain
wooden boat
bottle
cat
retrieven
hedgehog
Figure 15: Additional Editing Results
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Air Jordon
cat
ceramic
woman
plant
motorcycle
corgi
rabbit
lion cub
gorilla
man
woman
haunted house
monster
eagle
CGT
pig
duck
bird
leopard
squirrel
bucket
chair
girl with dog
monkey
broomstick
laptop
book
cat
Figure 16: Additional Editing Results
Chonge Material
kimono woman
golden sculpture
white horse
silver metol
shirt
Sweater
Change
Color
bird
green
red hat
hat
cobinet
wooden cobinet
Add Object
black turban
elephant
riding
Figure 17: Additional Editing Results
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Table 5: Example 1: Image-prompt pairs in ReShapeBench
Image
Prompls
Source Prompt: A single Air Jordan high-top University Blue eleganily stop weath- ered free stump, its bold panels and crisp lines catching the golden afternoon light. The rich blue leather, white toe hox, black laces, and dark grey Swoosh stand out sharply against rough beneath, every stitch and texture rendered Rehind it, soft blur of ivy leaves and tangled branches melts into warm bokeh, hinting a quiet forest edge bathed in dippled sunlight.
photorealistic clarity.
The entire scene feels effortlessly stylish where streetwear meets nature, screne yet striking, captured with cinematic depth and orgunic warmch.
Edit Prompt I: A sleck tabby cat rests elegantly stop weathered tree stump, its graceful curves soft for catching The rich orange brown cost, white chest patch, black black-tipped ears, and w whiskers stand out sharply against the rough bark beneath. every strund rendered photorcalistic clarity.
golden afkemoon light.
Behind soft blur of ivy kaves and tangled branches melts into warm bokch, linting a quiet forest edge bathed in dappled sanlight.
The scene fools gently alive- where wild nature cradles quict companionshipp serene and soutful, captured with cinematic depih and organic warmit.
A delicate porcelain tencup with matching sancer rests gracefully atop a weath- cred free stump, its fine floral patterns and polden rim catching the golden afteroon light.
Edit Prompt 2:
The white cemmic surface, intricate pink blossoms, green leaves, gilded edges stand out sharply against the rough bark beneath, every detail rendered with photonealistic clarity.
Hehind soft blur ivy leaves tangled brunches melts into bokeh, hinting quiet gurden edge bathed in dappled sunlight.
The entire feels timelessly elegant where refined craftsmanstip meets russic nature, serene yet sophisticated, captured with cinematic depch and oeganic warmeh.
Table 6: Example 2: Image-prompt pairs in ReShapeBench
Image
Prompts
Source Prumpt: A steaming cup of latte with delicate leaf art sits beside two slices of avocudo toast topped with runny yolks and fresh herbs, bathed morning light from nearby window.
The creamy foom, golden crust, vibrant green leaves, and glistening egg yolk stand shaply against the rustic ccramic plate, every crumb and dropkct readered with photorallstic clarity. Behind it, gentle blur of lush green folinge outside the glass pane melts into warm bokeh,, hinting al quict garden waking under diffused daylight.
Thee entire seene feels genily inviting - where simple pleasures meet slow mornings, serene yet richly textured, cuptured with cinematic depch and organic wammth.
Edit Prompt 1: A delieate sprig of white wildflowers rests gently beside two slices of avocado toast topped with nanny yolks and fresh herbs, all bathed window.
morning light from nearby
Thee fragile petals, dawy leaves, golden crust, and glistening, egr yolk stand sharply against the mustic cermicc plate, every texture rendered with photorealistic clarity. Behind gentle blur lush green folinge outside the glass pane melts into bokeh. hinting at quier gurden waking under diffused daylight.
The entire scene feels tenderly alive where nature's grace meets simple neurishment, serene yet richly textured, captured with cinematic depth and organic warmth.
ripe yellow barana rasss casually beside slices of avocado toast topped with runny yolkss and fresh herbs, all bathed in soft morning light from nearyy window. golden bac, and glissoning egg yelk stand out sharply against the rustic ceramic plate, every texture rendered with photorealistic clarity.
Edit Prompt 2:
The smooth peel. gentle
Behind it, a gendl blur of lush green foliage outside the glass pane melts into warm bokch. hinting at quict garden waking under diffused daylight.
The entire scene feels playfilly alive where everyday fruit meets wholesome nourishment, serene yet subdy whimsical, captured with cinematic depili and organic warnih..
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Table 7: Example 3: Image-prompt pairs in ReShapeBench
Image
Prompts
Source Prompt: A young boy crouches low on street, one hand resting gently on worm soccer gaze steady and thoughtful under the glow afternoon light. His weal striped sweater, red shorts with white suipes, seuffed sneakers, and the cracked leather surface ball stand sharply against the rough asphalt stitch rendered with photorealistic clarity.
every thread, crease, and
Behind him, gentle blur of weathered brick walls and distant buildings melts into warm bokeh, hinting humble neighbordood bathed golden-hour The entire scene foels quictly powerful - where childhood dicams meet everyday resilicnce. yet deoply
captured with
depth organic warmth.
hand resting pently leather backpack beside his koce, gaze steady and thoughtful under the soft glow late aftemoon light.
Edit Prompt 1: young boy cronches low quiet
frayed straps faded stitching of the buckpack all stand out sharply against the rough asphalt beneath, every rendered with photorealistic clarity.
striped sweater, shorts white stripes,
sneakers,
Behind him, a gentle blur of weathered brick walls and distant buildings melts into warm bakeh. hinting humble neighborhood bathed in golden- hour have.
feels quietly nostalgic deeply human, captured with cisematic depth and organic warmth.
days quiet contemplation,
Edit Prompt 2: A young boy crouches low on quict strect, one hand resting genily on the back of sleepy curied beside his sneaker, his guze steudy and thoughtful under the soft glow late afternoon light.
His teal striped sweater, red shoets with white stripes, scuffed socakers, and the soft fur, floppy cars, relaxed posture
dog
sharply against rough asphalt beneuth, every
texture rendered with photercalistic clarity.
Behind him, gentle hlur of weathered brick walls and distant buildings melts hinting at humble neighhorhood bathed in golden. hour haze.
hokeh,
The entire scene feels lenderty alive where childhood quictness meets loyal companionship. serene yet deeply human, captured cinematic depth organic warmth.
31


Reviews:
[{"review_id": "naC6SQ4mt0", "reviewer": "Reviewer_b8cf", "summary": "This paper focuses on improving precision of large-scale shape editing in images. In contrast to binary segmentation masks or cross-attention masks, which tend to be noisy and unreliable for shape transformation, TDM is based on the semantic difference between source and target denoising trajectories.\n\nThe core contributions are:\n(1) EditAnyShape -- a novel training-free and mask-free approach for controllable object shape editing in images.\n(2) A selective KV injection mechanism based on a Trajectory Divergence Map (TDM). This TDM serves as a guide for more precise and localized KV injection, preventing unintended edits.\n(3) A Scheduled KV Injection approach to only introduce TDM-guided editing in later stages of the denoising process, in order to prevent instability in the early, high-noise denoising stages.\n(4) A new benchmark, ReShapeBench, consisting of 120 (image, prompt) pairs for shape-aware editing.", "questions": "- In Figure 2 (left), what is \"Other shape editing\"?\n- Is there some way to visualize the TDM and compare it to the ground truth difference in foreground masks of the source and target images?\n- Perhaps Section 4.2 about benchmark construction would better belong in the Methods section or its own section, rather than in Experiments?\n- Section 4.2: What is the purpose of the third \"general evaluation set\"? Is it that subsets 1 and 2 are for training and subset 3 is for testing?\n- Since this approach is training-free, it would be great to see it applied to other open-source flow-matching models, like", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- The general insight of the paper to use magnitude of trajectory difference for localizing edits is both clever and intuitive.\n- The methods were generally well-motivated and clearly explained.\n- The paper is overall well-written and polished.", "weaknesses": "- Figure 2 is quite confusing. For example, is that row in the top right a legend for left and right sides of the figure? If so, it could be labelled more clearly. Also, for the bottom figure, the outline colors of the frames (particularly blue and orange) are very hard to notice.\n- The quantitative evaluation only includes results on the paper's custom evaluation dataset, but does not report results of a third-party editing dataset, such as PIE-Bench. Although PIE-Bench does not isolate shape changes, it would be more convincing to see if this method can perform on-par with other editing methods on an unbiased outside dataset.\n- In some qualitative comparisons of Figure 6, it looks like other methods seem similar to the EditAnyShape framework. For example, in the third-to-last row, KV-Edit looks like it is making the figure 8 ball and preserving the background well. What is the inherent benefit over kv-edit (also training-free)?\n- The ablation study doesn't actually compare to the base Flux.1-dev model, without adding EditAnyShape framework. It would be informative to know how well does the base model do for editing by default?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Q7XDmO2iAU", "reviewer": "Reviewer_KcbZ", "summary": "The paper introduces EditAnyShape, a training-free, mask-free framework for shape-aware image editing built on the FLUX.1 rectified-flow model. It targets large geometric and structural changes of foreground objects driven by text prompts, while maintaining high fidelity in non-edited regions. The core idea is a Trajectory Divergence Map (TDM) that measures token-wise velocity differences between inversion and editing trajectories to localize where semantics should change, which then guides staged KV injection and ControlNet conditioning. The authors also propose ReShapeBench, a curated benchmark of 120 images and 290 shape-editing cases, and show that their method outperforms diffusion-based and flow-based baselines on aesthetic quality, text alignment, and background preservation.", "questions": "- Could you report background-only and foreground-only PSNR/LPIPS (e.g. using TDM masks or manual masks on a subset) to more directly validate background preservation?\n\n- How does TDM compare to a DiffEdit-style prediction-difference mask or to cross-attention-based masks when combined with your staged KV injection and ControlNet schedule?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- Trajectory-based region control. TDM offers a conceptually grounded way to localize editable regions by exploiting velocity differences in rectified-flow trajectories, avoiding reliance on noisy attention maps or external segmentation.\n\n- Training-free and mask-free pipeline. The method operates purely with a pre-trained FLUX model and does not require hand-drawn or model-generated masks, which reduces annotation overhead and simplifies deployment.\n\n\n- Consistent empirical gains. Across ReShapeBench, the approach improves aesthetic scores, maintains better background similarity (PSNR/LPIPS), and achieves stronger text-image alignment than diffusion- and flow-based baselines.\n\n\n- Targeted ablations. Parameter studies on early stabilization length and ControlNet timing clarify how different components contribute and highlight reasonable default settings.\n\n\n- Task-focused benchmark. ReShapeBench explicitly emphasizes shape and structural transitions with clear criteria and curated prompts, helping the community study this specific editing regime.", "weaknesses": "- Global PSNR and LPIPS cannot disentangle background preservation from foreground edits, so the core claim of “preserving non-edited regions” is only indirectly tested. Some region-restricted metrics would provide stronger evidence.\n\n- The paper does not empirically compare TDM to simpler region-selection strategies (e.g. DiffEdit-style prediction differences, cross-attention masks) when used within the same staged KV injection scheme, leaving the unique benefit of TDM somewhat under-quantified.\n\n- Runtime and memory costs introduced by TDM accumulation and extra passes are not summarized in the main text.\n\n- The paper primarily shows successful edits and does not systematically discuss where TDM fails (e.g. thin structures, cluttered scenes, overlapping objects), which would be valuable for practitioners.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eQhf0neNuz", "reviewer": "Reviewer_bqGG", "summary": "This paper proposes **EditAnyShape**, a training-free and mask-free method for shape-aware image editing using flow-based generative models. The method aims to enable large-scale shape manipulation while maintaining background fidelity. The main technical contribution is the Trajectory Divergence Map (TDM), a token-wise spatial map computed from the divergence between the velocity fields of a source inversion trajectory and its edited counterpart. TDM highlights regions of greatest semantic deviation, effectively predicting “where to edit” without an external mask or cross-attention supervision. Editing is then achieved through a Scheduled KV Injection mechanism in three stages: 1) Early-stage unconditional KV reuse for stability; 2) Middle-stage TDM aggregation; and 3) Late-stage localized KV injection guided by the final TDM (optionally assisted by a ControlNet).\n\nIn the experimental results, the authors introduce ReShapeBench, a curated dataset of 120 images with 290 prompt pairs emphasizing geometric and structural changes rather than texture edits.", "questions": "The authors are suggested to respond to those raised in **Weaknesses.**", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper identifies a significant and challenging problem. Large-scale shape editing is a major failure case for most SOTA methods.\n2. Using divergence between source and target flow trajectories to infer editable regions is original and well-motivated. It moves beyond cross-attention saliency or explicit user masks toward a model-intrinsic notion of semantic locality.\n3. The proposed approach can be applied to existing pre-trained flow models without finetuning or additional training data. It integrates smoothly into existing editors, e.g., FLUX and KV-Edit.\n4. The mathematical definition of TDM in Equations (1-- 4) is consistent, and the scheduled KV injection is clearly described and empirically ablated.\n5. The provided visual examples demonstrate strong control over shape deformation and good background preservation, especially in scenes where previous mask-free editors distort context.\n6. The contributed benchmark, ReShapeBench, is expected to fill an evaluation gap by focusing explicitly on shape-aware editing. The dataset and evaluation protocols are carefully designed and could be useful to the community.", "weaknesses": "1. The proposed method employs ControlNet guidance (depth/canny maps) during the final editing stage to preserve structure and edges. However, none of the baselines (FlowEdit, RF-Solver, KV-Edit, MasaCtrl, DiT4Edit, etc.) use ControlNet or equivalent structural conditioning. As ControlNet introduces a strong external geometric prior, the resulting improvements in PSNR, LPIPS, and boundary fidelity cannot be attributed solely to the proposed TDM mechanism. As a result, the unfair bassline comparison could invalidate the main quantitative claims in Table 1.\n2. The technical novelty is incremental. The Trajectory Divergence Map is a new signal, but the rest of the framework (KV injection, flow editing, scheduled feature blending) extends existing works such as KV-Edit and Stable-Flow. Hence, the novelty lies primarily in the source of the region-control signal (velocity divergence), not in the editing pipeline itself.\n3. The constructed ReShapeBench dataset, while valuable, is relatively small (120 images). Its representativeness for general photo editing, especially non-synthetic images, remains uncertain.\n4. The presentation lacks runtime and memory analysis. In particular, TDM computation requires per-timestep velocity differences and KV caching, likely increasing GPU memory. The paper also does not quantify overhead relative to baselines.\n5. As the proposed method is a training-free, language-driven system, it would be helpful to show cases where TDM misidentifies regions or when prompt wording yields ambiguous edits.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lQo9PFD1eT", "reviewer": "Reviewer_XzBW", "summary": "This paper introduces EditAnyShape, a training- and mask-free image editing framework designed for precise, shape-aware object editing while strictly preserving background content. Existing editing methods struggle with large-scale shape modifications—they either fail to achieve intended shape changes or degrade undesired regions. The authors propose a novel Trajectory Divergence Map (TDM), which localizes editable regions by analyzing token-wise velocity differences between model inversion and prompt-driven editing paths in latent space. This map guides a scheduled Key-Value (KV) injection mechanism, stabilizing initial trajectories, enabling region-selective edits, and maintaining non-target regions. A new benchmark, ReShapeBench, is introduced, containing 120 curated images and paired prompts to evaluate shape-aware editing models. Experiments show that EditAnyShape achieves state-of-the-art performance for both editability and visual fidelity, especially in challenging large-scale shape transformation tasks.", "questions": "- As mentioned in the Weaknesses, TDM is computed as a latent-level difference between the inversion and editing trajectories. However, it is not intuitively clear how such a latent-space divergence can yield a spatially clear and human-interpretable distinction of editable regions. Could the authors provide further analysis or visualization to support this claim? For instance, how does TDM behave compared to traditional cross-attention maps in terms of localization clarity or noise robustness?\n- Regarding the Key-Value (KV) injection process: in cases involving significant structural layout changes (e.g., when a large object is transformed into a smaller one or when the object’s spatial footprint drastically shifts), how does the model prevent source layout information from unintentionally being injected into non-target areas? An ablation or visual example illustrating this effect would help clarify the robustness of the proposed injection mechanism.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper addresses the challenge of large-scale shape transformations in image editing, which is a well-defined problem\n- EditAnyShape is a training and mask free that automatically localizes editable regions (using TDM), also new benchmark ReShapeBench is introduced improving the evaluation of shape-aware editing\n- This framework achieves state-of-the-art quantitative performance across multiple metrics", "weaknesses": "While EditAnyShape presents a well-structured approach to large-scale shape transformation, several aspects remain underexplored or insufficiently analyzed.\n\n- **Lack of analysis on TDM interpretability and reliability.**\n    \n    The paper claims that the Trajectory Divergence Map (TDM) can precisely localize editable regions, unlike the noisy cross-attention maps. However, it remains unclear how effectively TDM identifies the user-intended foreground. Although the authors argue that cross-attention–based attention injection is noisy and inconsistent, they do not provide sufficient experimental evidence demonstrating that TDM is indeed cleaner or more reliable.\n    \n    It would strengthen the paper to include visual or quantitative comparisons between TDM and cross-attention maps (e.g., attention heatmaps, spatial correlation, or IoU-based analysis). This would clarify whether TDM truly captures semantic differences rather than inheriting similar noise patterns. Conceptually, since TDM is derived from latent velocity differences between source and edit trajectories, it is not immediately intuitive why it would be less noisy or more foreground-focused than traditional cross-attention maps. More empirical validation is needed to justify this claim.\n    \n- **Potential limitations of KV injection and region blending.**\n    \n    In Section 3.2.2, the framework performs Key-Value (KV) injection across stages 1–3, blending features from inversion and editing trajectories. However, this mechanism may face challenges in cases involving significant structural layout changes or large scale variations (e.g., when a large object is transformed into a much smaller one) Because KV injection reuses structural information from the source trajectory, there is a risk that unintended layout features from the original image could be reintroduced into the edited regions, potentially interfering with the desired transformation.\n    \n    A detailed ablation or visualization showing how KV injection affects spatial consistency and layout preservation in such extreme transformation cases would improve the paper’s rigor. It would also be helpful to discuss whether TDM-based modulation sufficiently suppresses unintended KV influence in non-foreground areas.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)