Geometric Image Editing via Effects-Sensitive In-Context Inpainting with Diffusion Transformers
Authors: Shuo Zhang
Wenzhuo Wu
Huayu Zhang
Jiarong Cheng
Xianghao Zang
Chao Ban
Hao Sun
Zhongjiang He
Tianwei Cao
Kongming Liang
Zhanyu Ma
Venue: ICLR 2026 Poster (2026)
Keywords: geometric image editing
inpainting
diffusion model
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recent advances in diffusion models have significantly improved image editing. However, challenges persist in handling geometric transformations, such as translation, rotation, and scaling, particularly in complex scenes. Existing approaches suffer from two main limitations: (1) difficulty in achieving accurate geometric editing of object translation, rotation, and scaling; (2) inadequate modeling of intricate lighting and shadow effects, leading to unrealistic results. To address these issues, we propose GeoEdit, a framework that leverages in-context generation through a diffusion transformer module, which integrates geometric transformations for precise object edits. Moreover, we introduce Effects-Sensitive Attention, which enhances the modeling of intricate lighting and shadow effects for improved realism. To further support training, we construct RS-Objects, a large-scale geometric editing dataset containing over 120,000 high-quality image pairs, enabling the model to learn precise geometric editing while generating realistic lighting and shadows. Extensive experiments on public benchmarks demonstrate that GeoEdit consistently outperforms state-of-the-art methods in terms of visual quality, geometric accuracy, and realism.

Published as a conference paper at ICLR 2026
GEOMETRIC IMAGE EDITING VIA EFFECTS-SENSITIE IN-CONTEXT INPAINTING WITH DIFFUSION TRANS- FORMERS
Shuo Zhang* Wenzhuo Wu Huayu Zhang* Jiarong Cheng Xianghao Zang²
Chao Ban
Hao Sun? Zhongjiang He²t Tianwei Cao Kongming Liang" Zhanyu Ma
School of Artificial Intelligence, Beijing University of Posts and Telecommunications Institute of Artificial Intelligence (TeleAI), China Telecom 'Beijing Institute of Technology fzhangshuo123, wuwenzhuo, cactianwei, liangkongming, mazzanyu]@bbpt. edu cn (zhanghy56, zangxh, banceechinateeecee (1 (oyran2]eouulooli com
(sun. (sun.010, hezhongj.1j@1 1611633.1 com
ABSTRACT
Recent advances in diffusion models have significantly improved image editing. However, challenges persist in handling geometric transformations, such translation, rotation, and scaling, particularly in complex scenes. Existing approaches suffer from main limitations: (1) difficulty in achieving accurate geometric editing of object translation, rotation, and scaling; (2) inadequate modeling of intricate lighting and shadow effects, leading unrealistic results. To address these issues, we propose GeoEdit, framework that leverages in-context generation through diffusion transformer module, which integrates geometric transformations for precise object edits. Moreover, we introduce Effects-Sensitive Attention, which enhances the modeling of intricate lighting and shadow effects for improved realism. To further support training, we construct RS- RS-Objects, large-scale geometric editing dataset containing over 120,000 high-quality image pairs, enabling the model to learn precise geometric editing while generating realistic lighting and shadows. Extensive experiments on public benchmarks demonstrate that GeoEdit consistently outperforms state-of-the-art methods in terms of visual quality, geometric accuracy, and realism.
INTRODUCTION
Image editing has achieved remarkable progress with recent advancements in generative models, enabling wide range of practical applications (Huang et al. 2025b). Nevertheless, it remains challenging task handle geometric transformations, where an object within an image is trans- lated, rotated, or scaled while preserving scene coherence. The challenge becomes more significant when dealing with large transformations (e.g. long-distance translations, large-angle rotations, and significant scaling) or complex scenes.
This task, commonly termed geometric image editing, aims to perform geometric transformations on objects within image while maintaining object-background consistency during transformation. In this editing task, two key challenges remain: (1) accurate geometric transformations, including translation, rotation, and scaling: (2) natural blending with proper visual effects (e.g., shadows or reflections). Early methods typically relied on copying and pasting objects into target locations, followed by image harmonization (Wang al. 2024). While simple, such strategies struggle with large transformations and fail to produce realistic lighting and shadow effects. Recent advances in diffusion models have enabled geometry-aware editing by inverting images into noise space and applying afline Transformations before decoding (Pandey et al., 2024] Sajjanii et al.) [2025] (Zhu al. 2025). Although these approaches support a broader range of transformations, they still lack
* Eqqual contribution.
Project lead.
Corresponding author.

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Published as a conference paper at ICLR 2026
Translation
Rotation
Scaling
Translation + Rotation + Scaling
Figure 1: Our method accurately performs geometric edits including translation, rotation, scaling, and their combinations (e.g., translation combined with rotation and scaling), while achieving reli- able generation of lighting and shadow effects to ensure realistic editing results.
physically consistent illumination and shadows. A complementary line of work leverages large-scale video datasets to learn environmental lighting priors (Alzayer al., 2025 Yu al., 2025 Cheng iet al., (025a). However, these methods remain limited in achieving precise and complex object geometry transformations, Taken together, existing approaches have yet to simultancously achieve high-fidelity object transformations and photorealistic visual effects.
To address these challenges, we present GeoEdit, an srfects-sensitive in-context inpainting frame- work built upon diffusion transformers (Peebles & Xie 2023). More specifically. to address pre- cise object transformations, we introduce Geometric Transformation module that employs 3D reconstruction to lift objects into an elevated dimensional space, where parametric transformations (translation/roaaiinnneeaannn are executed with precise control. address the realistic generation of lighting and shadow effects, we design Effects- Senest--ennsiivee Attention (ESA) that refines the prior distribution the attention map, guiding the post-training model to generate more plausible visual effects. Meanwhile, the superiority of ESA is also supported by theoretical analysis (see Thm
Unfortunately, existing datasets fail to simultaneously provide precise geometric transformations and high-quality lighting/shadow effects (Peebles & Xie [023] Yu et al. 2025 AAlzayer al.) [2025] Cheng et al. 2025b), leading them to be inadequate for training our model. To fill this gap, we con- struct RS-Objects, which is large-scale dataset containing 120,000 image pairs (20,000 rendered and 100,000 synthetic) spanning 30 objects across 24 scenes. This ensures adequate training of our proposed model.
In light of these designs, GeoEdit enables precise geometric transformation and faithful visual ef- fects (see Figure T). To summarize, our main contributions are threefold:
1. We propose GeoEdit, an in-context inpainting framework that integrates a Geometric Transformation module for precise object editing and Effects-Sensitive Attention for realistic generation, with the effectiveness of ESA also theoretically supported.
2. We construct RS-Objects, a large-scale geometric editing training dataset containing over 1 120,000 samples, covering 30 different object categories and 24 complex scenes.
3. Through extensive experiments, we demonstrate that GeoEdit achieves superior results and outperforms existing methods.
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2 RELATED WORK
Diffusion Models. Diffusion models synthesize images by iteratively denoising noisy samples with a learned noise estimator (Ho et al. 2020; Song et al.] 2020b). Significant improvements have been achieved in many aspects such efficient sampling (Song al. 2020a), latent space model- ing (Rombach et al., 2022 Nichol et al. 2021), and guidance mechanisms (Ho & Salimans, [002]
Dhariwal & Nichol 2021). Notably, recent development of Diffusion Transformers (Peebles & Xie
2023) has demonstrated superior generation capabilities and has been adopted by many advanced image generation models. In this paper, explore utilizing diffusion transformers tackle geo-
metric editing.
Geometric Image Editing. Recent geometric image editing methods can be categorized by their need for per-instance training. Training-free approaches impose geometric constraints during infer- ence on pre-trained generative models. These approaches typically either (i) optimize latent features to match user control points (Pan et al., 2023), or (ii) manipulate diffusion features and attention maps to reflect geometric transformations (Pandey et al. 2024, Sajnani et al. 2025 Zhu et al. 2025)
Realism is often enhanced via post-processing modules for harmonization or relighting (1 sai et al.)
2017 Cong et al., 2020). with recent work integrating diffusion-based harmonization and counter- factual supervision for physical plausibility (Song et al. 2023: Winter et al.) 2024 Kim al. 2025).
However, these methods often require auxiliary inputs and can produce artifacts under significant pose changes. Training-baa -based editors learn task-specific geometric priors via (i) rest-time fine- fine-tuning
with lightweight adaptation (Shi et al. 2024, Zhang et al. (2055) [hh et al. [202)) or (ii) supervised
learning on video/3D datasets distill physical priors (Alzayer al. 2025
Yu et al. 2025 Cheng
et al. 2025b; Yenphraphai et al. 2024 Wu et al. 2024] Michel et al.
2023 In contrast, GeoEdit
does not rely on per-instance fine-tuning; performs in-context DiT impainting guided by texture information as well as geometric guidance.
In-Context Learning for Image Editing. Visual in-context learning treats references, exemplar pairs, and controls as unified visual prompt, processed in a single forward pass. In image editing, this paradigm powers
approaches that preserve appearance via cross-attention or lightweight adapters (Yang et al. [023] Ye et al.) (2023). and panel-basee -baed layouts that facilitate few- shot adaptation (Wang et al. 2023b Chen et al. [2023] Zhang al., 2025a; Chen et al., (025)) These methods have been extended to mask mask-conditioned object insertion on DiT architectures (Song et al.) 2025) and broader general-purpose purpose frameworks (Bar et al. [2022] Wang et al. (202a)). GeoEdit also adopts this framework, while introducing several task-specific modifications to better address the challenges of geometric editing.
GEOEDIT
Our goal is to achieve precise geometric editing while generating realistic lighting and shadow ef- fects. To this end, we propose GeoEdit, a diffusion based framework for geometric image editing (see Figure 2 Given an input image and a source mask our Geometric Transformation module (Section 3.1) applies translation, rotation, and scaling to produce a target mask and an appearance reference (transformed object) for in-context guidance. These inputs, together with the original im- age, are processed by the Diffusion Transformer module, where paired masks explicitly constrain content generation and the Effects-Sensitive Attention (ESA) (Section (.2) mechanism adaptively captures lighting and shadow effects to produce realistic editing results. The resulting representa- tions are then decoded to produce the final edited image.
3.1 GROMETRIC TRANSFORMATION
To better achieve precise geometric editing of objects, model Geometric Transformation (shown in the bottom-ledt panel of Figure 2) as translation, rotation and scaling. We precompute object images and masks under these transformations to provide geometric and texture cues for in-context reasoning and downstream editing. Each operation preserves object appearance while enabling pre- cise geometric control.
Translation. We copy the source mask to the target location to reposition the object without altering its shape or texture, providing stable spatial reference for subsequent transformations.
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Diffusion Trausformer Module
Learnable
Madked
Image Eicoddr
DIT Blokss
Siurce Mirk
Source Mask
Target Misk
Effecto-raaiee Seasttiv Aftention
Single Blocks
Geometric Transformation
VAE
Decoder
VAE Encoder
DVT Blocks
Orgainal Imsage
Geometric Transformation
Effeccs--Snsitive
Quecy
Rotation
Booling
ESA
Standard Attenice
ESA Anceiios
The framework of proposed GeoEdit, built upon in-context inpainting paradigm, sists of a Diffusion Transformer Module that integrates two key components: (1) Geometric Trans- formation for object editing (translation, rotation, and scaling), and (2) Effeccs-SSnsitive Attention for modeling intricate lighting and shadow effects.
Figure 2:
Rotation. We reconstruct each object textured 3D mesh using Hunyuan3D-2.1 (Hunyuan3D et al. 2025) and rotate it to arbitrary angles. The mesh is orthographically projected onto white canvas. To avoid clipping, we first render and use a depth buffer for occlusion. The projected region is cropped to the object's bounding box and rescaled with safety factor of 0.7 fit the target resolution while preserving aspect ratio. The content is then centered. A corresponding mask is generated by rendering the mesh in white on black background using the same procedure. Finally, the transformed object and corresponding mask are translated to the target location.
canvas three times larger than the target resolution
Scaling, We simulate depth variation by uniformly scaling the object image and mask according to the intended camera-axt displacement, providing simple depth cues and completing the geometry- aware input preparation with translation and rotation.
3.2 EFFECTS -SERSTIVVE ATTENTION
In geometric editing, the modeling of intricate lighting and shadow effects is often insufficient. To address this, we explore different modulation strategies for guiding attention and propose Effects- Sensitive Attention (ESA), a soft guidance mechanism that biases attention according to regional objectives while preserving cross-region interactions.
We first consider the standard attention for object insertion in geometric editing, where the query of token i denoted as di ( and the key of token as k; (T),, Here, (()) represents the set of tokens corresponding to all regions of the image, while ((K) represents the set of tokens encoding object-specific features. The interaction between qi T and k5 T(K) can then be expressed as the scaled dot-produce similarity:
k.k Vd
A, = =( Siz
where Sty denotes the similarity between query i and key j. denotes the dimensionality of the query and key vectors, g(S,g) = exp(S,,)) Σ, exp(S,,) denotes the softmax function. This for- mulation distributes attention broadly across the entire scene, which helps maintain global context.

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However, this broad distribution often means that the editing region lacks sufficient focus. As shown in Figure standard attention struggles to accurately integrate objects within the editing regions.
One straightforward way to modulate attention is to only focus certain queries keys (Sun et al., 2025). We define T( edit as the set of tokens corresponding to insertion regions, while ( aux denotes the tokens in the other regions, such that T()) edit U ( aux. Accordingly, the Hard Modulation can be written as:
subset of
+00, 9:kb /Vd,
E T()))
hard A = g (Stt)rr)
shard kj
1
(2)
q: E T (Q)
Taus
However, since the auxiliary regions include lighting and shadow effects, suppressing attention from these regions to the object may result in missing these effects. As shown Figure Hard Modula- tion can insert objects, but it fails to properly render the associated shadows and lighting effects.
To enhance attention within the insertion region by focusing on object tokens, while preserving interactions with auxiliary regions to capture lighting and shadow effects. We propose Effects- Sensitive Attention (ESA). The equation of ESA can be written as follows:
[kk] /V
9i E
( edit
A (SSAA))
SESA S0
Lik]/Va, 9: E ToQ
Here, 8 (S,)) denotes a scaled standard deviation of the raw attention logits, where the scaling factor > 0 represents the strength of control over attention. As shown in Figure 3 ESA can insert objects while maintaining high-quality shadows and lighting.
While the previous discussion focused on ob- ject insertion, geometric editing involves both object insertion and background restoration. Similar to Equation 3 we enhance the atten- tion of tokens in the background restoration gions to the background features. The hyper- parameter is set differently for object inser- tion and background restoration, with specific experimental details provided in the Appendix (see Appendix G))
Sturdard
Hard
Ours
In addition aforementioned analysis, Theo- rem 3.1 further reveal that ESA can align the prior distribution of the attention map with an ideal attention structure A". Since that A si- multaneously attends to object and visual effect regions (see Appendix I). the alignment with A" may boost visual effect generation for post- training models. The full proof process refers to Appendix
Figure 3: Comparison of attention strategies: standard; Hard Modulation; Ours.
Theorem 3.1 Let A be an ideal attention map, and p be its threshold for discriminating criticalhon-crrtiiall regions. Here A has several necessary conditions defined Appendix A
Based on this, if we have 2 1/(T((2) then the following statements hold for each key token kj E (K))
6(1T(19) p 1) 0, thus 1. The subtraction DKL DKI(At) A.z) DKL (Aty AESA)) have (A's || ATSA)) S DKL (A', A.))
2. The divergence DKL (At, Abardd) bound DKL (A:, || ABSA) < [T) log (1/ min(A:;)), thus we have DKL || ABSA) < DKL. (At, || Abard).
too and DEL (At, (1 has a finite upper
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where DKL denotes KL divergence; Ar denotes the attention distribution w.r.t A over each query token qi T(9); Alyan A and A, are defined by following the same paradigm with A:
In this way, ESA maintains attention flow across the entire scene while softly biasing attention to- ward relevant tokens. As shown Figure this approach increases the attention editing regions, while still preserving interactions with auxiliary regions.
4 DATASET CONSTRUCTION PIPELINE
Preprocessing
Ofjicts
w
Synthetic Data Generation
LORA
Blenderkit
(Mles Mides
Pe anvonnatio
Ronder Teoplete
Lightie
Geometric Transform
Move
Anglevertion F2
Citjecs
slgeed groand plane
Move
Good Case
Scale
M,
1.
M,
4
Hatyssa
Bad Case
Plysis Defied
Fulled Ellmination
Move-Rotation
Rotation
Anwicar
Momuich
M,
Mi.
Rendering stage
Synthesis stage
Figure The rendering-synthesi pipeline for building our RS-Objects dataset.
Collecting large-scale images of objects with precisely controlled transformations while preserving realistic lighting and shadow effects is inherently challenging. To overcome this, we introduce RS-Objects, dataset constructed through two-stage cendering-synthesis strategy. This approach provides fully controllable training samples exhibiting realistic visual effects, while helping bridge the domain gap between synthetic and real images. The overall pipeline is shown in Figure 4
Rendering Stage. In step (1) of the process (shown in Figure 4.. we use Blender (Community. 2018) render 24 diverse object-rich scenes with 30 distinct objects. Multiple camera rings employed under parameterized translation, rotation, and scaling, yielding an extensive Rendered Dataset of 20,000 image pairs. This dataset captures detailed object geometry and appearance (see visualization in Appendix B). which is used to train the initial LoRA (Hu et al. 2022).
Synthesis Stage This stage comprises three main steps (shown in the right part of Figure 4
Mash-based sample generation, (3) Large-scale image synthesis using the trained LoRA, and (4) Human-in-the-loog quality filtering. Specifically, step (2) leverages meshes from Anylnsertion VI (Song et al. 2025) and Hunyuan3D 2.1 (Hunyuan3D et al. 2025) to generate preprocessed images and target masks, enriching the dataset with diverse geometry and textures. Step (3) employs the LoRA model trained the Rendered dataset for batch generation of geometry- and texture- aware object images, producing over 800,000 synthesized samples for robust downstream training- Step (4) involved 20-person annotation team conducting three-week quality assessment to discard samples with issues such as spatial coherence, feature consistency, illumination consistency, and controlled generation. After this rigorous filtering (see Appendix C). retained over 100,000 high-quality image-mast mask pairs constituting the final AIGC Dataset for training.
The final RS-Objects dataset is constituted by the Rendered Dataset and the AIGC Dataset, contain- ing a total over 120,000 high-quality rendered and synthesized image-mask pairs that exhibit precise geometric transformations and realistic lighting, shadow effects.

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Table 1: Quantitative results 2DD-edits and 3D-edits tasks. We report seven metrics for image quality, consistency, and editing effectiveness. Best results are in bold, second best are underlined.
Methods
Editing Tasks
FID.
DINOv2,
KD
SUBC1
BC1
WEI
MD.
Rrai Lu cl.
2024
41.88
257.43
0.052
0.796
0.972 0.120
32.75
MotionGuidance Geng & Owens
2024
146.41
1307,90
0.078
0.452
0.714 0.260
145.46
DragDiffusion Shi et al 2024
37.68
242.52
0.051
0.776
0.969
0.177
34.78
Diftusion Handles Pandey ct al.
2024
69.34
588.58
0.054
0.725
0.857 0.180
40.94
GeoDiffuser Sajnami et al. 2025
2D-edits
38.22
198.58
0,052
0.761
0,937 0.166
34.94
Ddsi Jia et al. 2024
32.55
142.45
0.052
0.874
0.962 0.098
10.15
Magic Fixup Alzayer el al. 2025
27.32
114.08
0.051
0.889
0.966
0.075
10.39
FreeFine Zhu et al. 2025
27.48
109.23
0.052
0,906
0.971
0.056
9.42
GeoEdit (Ours)
25.07
90.66
0.051
0,910
0.977
0.054
9.23
Diffusion Handles Pandey et al. 2024
126.24
1028.60
0.056
0.737
0.885
0.189
18.56
GeoDifuuser Sajnani et al. 2025
77.34
475.62
0.055
0,802
0.946
0.179
43.51
DDeddits
FreeFinee Zhu 2225
65,94
366.39
0.055
0.832
0.967
0.052
21.27
GeoEdit (Ours)
64.30
350.69
0.054
0.840
0.977
0.051
18.08
EXPERIMENTS
5.1 EXPERIMENTAL DETAILS
Implementation Details. GeoEdit builds upon FLUX.1 Fill (Black Forest Labs ing model based on the DiT architecture. We replace the original T5 text encoder (Raffel et al. (020) with a SigLIP image encoder (Zhai et al., 2023 and fine-tune it using LoRA (Hu et al.) 2022). All
2024), ), an inpaint-
training and inference procedures follow standard practices with high-resolution images. Detailed training configurations and hyperparameters are provided in Appendix
Comparison Methods. We benchmark our framework against representative image-editing meth- ods under different geometric transformations. For 2D-edits, we compare with RegionDrag (Lu et al. (022)). MotionGuidance (Geng & Owens] (2045) DragDiffusion (Shi et al.] (2224). Diffusion- Handles (Pandey et al. 2024), GeoDiffuser (Sajnani et al. 2025). DesignEdit (Jia et al. (024), Mag- icFixUp (Alzayer et al.) 2025), and FreeFine (Zhu et al. 2025 For 3D-edits, we evaluate against methods explicitly modeling 3D transformations DiffusionHandllss, GeoDiffuser, and FreeFine. This division reflects the different challenges of 2D versus 3D editing and enables targeted evalua- tion.
Datasets and Evaluation Metrics. We evaluate GeoBench (Zhu et al. 2025), , which inte- grates PIE-Bench (Ju et al., 2024) and Subjects200K (Tan et al. 2024). yielding 81 source im- ages and 5,988 editing instructions spanning 2D (translation, scaling) and 3D (rotation) tasks. Fol- lowing FreeFine (Zhu et al. 2025), we adopt seven metrics: (1) Image Quality: Fréchet Incep- tion Distance (FID) (Heusel et al. (017) computed separately per task type, plus Kernel Distance (KD) (Binkowski et al. 2018) and DINOv2 feature distance (Stein et al. (023). (2) Consistency: Subject Consistency (SUBC) and Background Consistency (BC) measure similarity of foreground and background embeddings. using source mask and target mask. (3) Editing Effectiveness: Warp Error (WE) and Mean Distance (MD) quantify how closely generated objects match target config- urations within masked regions. Together these metrics provide complementary views of realism, consistency, and adherence to editing instructions.
5.2 COMPARISON WITH EXISTING METHODS
Quantitative Results. Table 1 reports quantitative results for 2D-edits and 3D-edits. For 2D-edits, GeoEdit leads on all seven metrics, including the lowest FID (25.07). DINOv2 distance (90.66), and Kernel Distance (0.051). and the highest Subject (0.910) and Background Consistency (0.977). It also achieves the lowest Warp Error (0.054) and Mean Distance (9.23), indicating strong realism and precise edits. For 3D-edits, GeoEdit again surpasses prior methods with the lowest FID (64.30), DI- NOv2 distance (350.69), and Warp Error (0.051). while outperforming FreeFine (Zhu et al. 2025),
GeoDiffuser (Sajnani et al. 2025). and DiffusionHandles (Pandey al. 2024 consistency met-
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Source
Promot
DragDimtusin Diffusion Handles GecDifluser
DesignEdit
Megic Floup
FreeFine
Qurs
Figure 5: Qualitative comparison with different editing approaches on the 2D-edits of GeoBench.
Source
Prompt
Diffusion Handles
GeoDiffuser
FreeFine
Ours
18
Figure 6: Qualitative comparison with different editing approaches on the 3D-edits of GeoBench.
rics. These results show that GeoEdit generalizes well across 2D and 3D transformations, producing high-quality, geometrically accurate edits.
Qualitative Results. In 2D-edits (Figure 5 such as resizing the goldfish, it preserves object struc- ture and integrates coherently with the scene, avoiding artifacts seen in other methods. When remov- ing the seal from the waterfront scene, our approach realistically reconstructs occluded regions and adjusts illumination to produce physically consistent shadows. For the more challenging 3D-edits (Figure 6). it is the only method producing perceptually convincing results, correctly reconstructing geometry and shading (e.g., the rotated wheel) where others fail. These results demonstrate precise, context-aware editing and improved realism for both 2D and 3D transformations.
5.3
USER STUDY
To provide a comprehensive subjective evaluation, conducted user study assessing the percep- tual quality of GeoEdit across 2D-edits and 3D-edits tasks. Participants compared our method with competing approaches along three human-judged dimensions: Quality, Consistency, and Effective-
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Prompt
Stardard
Dut
Male
20 edits
22.1%
13.25
30
45 0%
1%
13 0%
Edkiigg tpes
0%
23,8%
62 135
27 195
9.3%
etfectienesss
80.1%
11.39
(2) User preferances in each dimerskon
Figure 7: Ablation results different attention modulation strategies.
Figure 8: User study results: GeoEdit outper- forms prior methods on all criteria.
ness. As shown in Figure GeoEdit consistently achieved the highest user seer-preeerence rates across all tasks and dimensions, substantially surpassing baseline methods. Further details-iiccuudngg notator recruitment, sample construction, online survey interface, randomization to mitigate bias, and full per-dimension statisties provided in Appendix E
5.4
ABLATION STUDIES
Table 2: Ablation results of attention modulation and datasets. The best results are in bold.
Setting
Metries
Editing Tasks
Type
Variant
FTD
DINOv2,.
KDJ
SUBC1
BCT
WF.
MDJ
Standard
29.11
115.04
0.052
0.891
0.969
0.097
15.75
Hard Modulation
2D-edits
27.09
107.83
0.051
0.899
0.964
0.063
11.11
Ours
25.28
94.79
0.051
0.908
0.977
0.057
9.32
Attention
Standard
68.02
372.68
0.055
0.823
0.962
0.082
22.50
Hard Modulation
3Deedits
67.32
367.27
0.055
0.832
0.969
0.061
19.62
Ours
64.30
350.69
0.054
0.840
0.977
0.051
18.08
Rendcred
26.
110.82
0.052
0.889
0.969
0.076
10.55
AIGC
2D-edits
25.82
106.03
0.051
0.898
0.972
0.066
9.96
Both
25,28
94,79
0.051
0.908
0.977
0.057
9.32
Datasct
Rendered
66,92
385.10
0.055
0.822
0.961
0.091
19.04
AIGC
3D-edits
65,94
364.59
0.055
0.825
0.967
0.080
18.67
Both
64.30
350.69
0.054
0.840
0.977
0.051
18.08
We perform three ablation studies to quantify the contributions of data composition and attention modulation in GeoEdit.
Data composition. Table shows that training on rendered data alone yields the weakest perfor- mance, while adding AIGC data improves all metrics. Combining rendered and AIGC data achieves the best scores (FID 25.28, DINOv2 94.79), indicating that diverse data strengthens geometric pri- ors.
Attention modulation. Table compares modulation strategies. Hard Modulation outperforms the baseline but sacrifices contextual cues, whereas our soft ESA attains the lowest FID (25.28) and Warp Error (0.057), with consistent gains on 3D-edits. According Figure 7 ESA also enhances the model's s ability to generate realistic lighting and shadow effects, further improving perceptual realism. Additional qualitative ablations are provided in Appendix
Hyperparameter in ESA. The hyperparameter & is set separately for object insertion and back- ground restoration (Appendix G). Based on the results in Table which offer the best balance between edit fidelity and contextual consistency. Additional experi- ments under varied illumination (A (Appendixll Hp further validate these choices.
aj = 0.1 and ag 1,

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6 CONCLUSION
In this work, we introduced GeoEdit, a diffusion-based framework for geometric image editing that integrates Geometric Transformation module, an in-context inpainting paradigm, and Effects- Sensitive Attention to jointly achieve precise object manipulation and realistic modeling of lighting and shadows. We also constructed large-scale dataset over 120,000 image pairs tailored for effective training, enabling GeoEdit to learn precise geometric transformations and realistic scene effects. As result, extensive experiments and user study demonstrate that GeoEdit consistently outperforms prior methods in geometric accuracy, visual fidelity, and realism.
ACKNOWLEDGMENTS
This work was supported by the National Nature Science Foundation of China (Grant 62476029, 62225601, U23B2052), funded by the Fundamental Research Funds for the Beijing University of Posts and Telecommunications under Grant 2025TSQY08, and sponsored by Beijing Nova Program.
ETHICS STATEMENT
Our research involves the creation and use of synthetic datasets for geometric image editing and associated human evaluations. All image data used in this work are either synthetically generated or obtained from publicly available sources with appropriate licenses. No personally identifiable information (PII) or sensitive content is included.
For the user study, participants were recruited from a professional annotation team. Participation was entirely voluntary, and all data were anonymized protect privacy. The study protocol adhered to standard ethical guidelines, and no participants were exposed to harmful or distressing content.
Additionally, in manuscript preparation, any assistance provided by large language models (LLMs) was carefully reviewed and verified by the authors to ensure accuracy, avoid bias, and maintain scientific integrity.
Overall, we have taken care to ensure that our dataset, experimental protocols, and writing practices adhere to ethical standards appropriate for computer vision research.
REPRODUCIBILITY STATEMENT
We have made extensive efforts to ensure the reproducibility of our work. The implementation details of proposed method, including training configurations, model architectures, and hyper- parameters, are provided in the main paper and further elaborated in the appendix. The source code package is included in the supplementary materials. For the datasets used in this work, we describe the preprocessing pipelines and filtering criteria in the main paper and appendix. For theoretical derivations, proofs, and assumptions are clearly documented in the appendix. Together, these re- sources are intended to enable researchers to fully reproduce our results.
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A ATTENTION DISTRIBUTIONS AND DIVERGENCE ANALYSIS
This appendix complements Section by formally analyzing how the standard attention, Hard Modulation, and ESA compares to an idealized "perfect" attention for our editing task. All notation follows Section 3.2
A.1 NECESSARY CONDITIONS FOR IDEAL ATTENTION
For geometric editing tasks, we argue that an ideal attention A should meet the following necessary conditions: (i) the attention weights are expected to be elevated in both object region and their effected region (shadows reflections); (ii) Besides these regions, other non-salient parts require suppressed attention responses. For enhanced analytical precision, we decompose the auxiliary region into the effect region and the other region, i.e., T(()) Teflett U To() necessary conditions for A can be formulated as:
other. Based on this, the
A
A 2 P:
VET
GET
(T)))
effect
: A 2 Pi
(4)
Σ A 21-6.
9 (T
(T(
where p E (0,1) stands for the threshold for differentiating the critical region (i.e. T!l U T(02))
from other non-critical region (i.e. T(90) is a constant such that s < p. indicating the non-critical region exhibits sufficiently small attention values.
A.2 PROOF: STATEMENT (1) IN THEOREM
To prove DKL (A; 11 A.)) > (AL 11 |AAA)) > 0. To this end, we first come the following equation: DKL (A', Al.)) - DKI (A || 55AA)
(AI AESA), it suffices to prove DKL (KL 1| A.))
1
Aij log A Auj
A
A
Σ
Aty Asj
+
Σ
Σ
A log EESA
+
Σ
Aty log
A
A AESA
( (2)
(E)
(Q)
A log A
At
A
A
Σ
Σ
Aij log
Σ
A log
Σ
A log
Aij
AESA
Ai
AESA
(
(0)
(.TT(2))
g.E (Q)
Auuz
(5)
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Based on this, can apply algebraic operations edd and get that:
Aij Asj
A log
Dedit
Σ
Aig
log
Σ
Aij
A
AESA
9.ET. edit
A
A
Σ
A
log
log
Arj
1ESA
4, (Q)
A
A
Σ
Aij
log
log
Asj
AESA
9. (ETT.
Σ
Aij
(log A 15
log Asj)
qi ET (Q)
Meanwhile, can get the following equation by using the same operation as Eq.
Aaux:
A Aaur
Σ
Aig (log ESA log Ai²)
4 (Q)
In this section, Σ means sum over index by default. Then Aedit can be expressed as:
Aedit
Σ
Aig (log A
ESA log Aij)
(0)
exp(S
log exp(Sii)
Σ
Aij log Σ exp( (Sp((S55))
Σ exp((si))
(8)
Σ
SESA Sig + log Σ exp(Sig) - log Σ exp(SE5A)
q. (TT
exp(SE..A)
Eexp(S())
A
log
+ log
exp(S,;)
Σ Eexp(S,f55))
Then we analyze the term Amwa Note that we have SESA Sig when di Toll that:
Thus, can get
Σ
Ai (log A - -log Aij)
4 T
exp(S
log exp(Sij)
Σ
A
log
Σ (.ES))
Σ exp(Sit)
4, (
Σ
A
CESA ij
Siy + log Σ exp(Sig) log Σ exp exp(Stss))
4: TETT TTu
Σ
A Sij Siz + log Σ exp(Sig) - -log Σ exp(SE5A)
4; ( (Q)
Eexp(Sy)
Σ
A log
Σ Eexp(SESA)
4, AUx
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Based on this, can substitute Eq
8 and (q. into Eq.
yielding that:
DKL (Arg || A..)
DKL (4'g
Σ exp(Stt)
]
Σ exi((Sis)
Σ
Aig
log
log exp(Sigg)
Σ 7.E
Aig Atylog log
Σ (ex((S155
Σ Eexp(s!t) (5.55A)
ETdis
exp(S
Eexp(Sig) Σ Eexp(SEsA)
Σ Exp((Si)
Σ
Aig log
Σ ( odit
Aiy log
Σ
Aig log
EExp(SfSAA
exp(Sit)
4: (T)
edis
exp(St)sA
Σ exp((Si)
Σ
Aig log
Aig log
exp(Sit)
Σ exp(S!/5A))
ET
GTTT(O)
edit
exp(St) exp(Si))
Σ Exp((Sis)
Σ
Aig log
+ log
Σ eepp(st55
9i STET. (Q)
exp(St5SA) exp(Sig)
Σ Eexp(Sis)
plog
log
Σ exp(SESA)
9. ET
(10)
Furthermore, we can derive that:
exp(S
exp(Sii + +.)
(exp(Ssg) exp(o)
log
log
loog
6.
(11)
(S.))
exp(S:j)
exp(Sit)
Therefore, the first term in the last line of Eq.(TO) can be reformulated as:
exp(SESA)
Σ plog
exp(Sit)
1j
Σ p.
QQ)
pp.
(12)
T
Now we come to the second term in the last line of Eq. (I0)
Exxp(S,)
log
Σ exp(S!5SA)
Σ exp((Sis)
llog
(13)
exp(Sij + 5)
Σ exp(Sii))
log
exp(Sis) exp(o) log exp(o)
= -.
By substituting Eq.
12 and Eq. 13) into Eq. IOp, we could know that:
(Ai || A.j) - DKL (A:; ||
A
ESA) 2 [T(0) p 8
((1-(1) edit lpllppp-
-p
(14)
In this way, when 2
1/1T(22 I. we have As 2 0, indicating that DKL. A.j)
2
DRL. (Aty || AESA). Now the proof is concluded.
A.3 PROOF: STATEMENT (2) IN THEOREM 1
Based on Eq.(14. if we want to prove DRL(A, || Algurd) > DKL (At, || || we only need to prove DKL (A: Ahaar) > DKL (KI(A,, || A,). To prove DKL (A', || A > DKL (At, AA))) it suffices establish the existence of finite upper bound for DKL. (At, || ABSA). To this end,
first analyze the structure of the standard attention distribution
Siz = q:k]
Au = 9($),
Vd
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Since all queries 9; and keys ky are finite-valued vectors, the logits Siz = %: Consequently, Aij 0 for all i, j, ensuring that the support of A, covers the entire query token set T(Q),
vi are finite for all i,
Now, consider the ideal distribution A; which satisfies Eq, with Paux > 0. Since Aij > 0 for all i, the ratio / Aig is finite for every i, and the KL. divergence:
10
DRL. (AT)1A,)) = Σ
A log
(15)
Aij
a finite weighted sum of finite terms. More precisely, we can bound it as follows:
Let & = mfng, Ai > 0 (which exists since there are finitely many tokens and Ay > 0). Then for any with At > have:
Aij. Aij
log
S log Aij
S log B
Similarly, since Aty S we have:
A
1
Aij log Asj
< log
3
Therefore,
DKL. (Ar 11 A.g) S ( log < +00.
(16)
In contrast, as shown Section
A.4.
when Paux > O, we have:
DKL (KL(A', || A +00.
Thus, we conclude that:
DKL (At, || Ahard) too DKL (A', |I A.)),
(17)
By integrating Eq. (4) and Eq. the proof can be concluded.
A.4 KI. DIVERGENCE BETWEEN THE HARD MODULATIOO AND IDEAL DISTRIBUTIONS
Recall the Hard Modulation logits and the resulting attention Eq
2
(Q)
+00,
edit
A hhard
((Shaad))
Sig
Shard
yike
T (Q)
Vd
Applying the softmax over i, the limit of Abard is
[1/TT odlit
di E (Q))
Abard
(18)
0,
a. E T
The KL divergence between the ideal and Hard Modulation distributions for each j
A
DKL( || Ahard) =
Σ
log
(19)
Abard
Because Ahard for qi E
((()) any nonzero ideal mass At, > O in the auxiliary region yields
A
A log
+o0.
(20)
O
Thus, whenever the ideal distribution places even small positive mass Paux > 0 on auxiliary tokens (as allowed by Eq. 4). have
DRi(A A Ahard) +00.
(21)
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Interpretation. Hard Modulation collapses all probability mass onto the edit region and assigns zero probability to auxiliary tokens. Since the ideal distribution generally preserves at least a small positive mass the auxiliary region, the two distributions have disjoint support, and their KL divergence diverges. This highlights the instability of overly hard attention gating compared to softer, ideal allocation.
B VISUAL COMPARISON: RENDERED DATASET VS. OTHER RENDERING DATASETS
OBJECT
ObjectMover
Ours
Figure 9: Visual comparison between our Render Dataset and existing rendering datasets, highlight- ing its broader scene diversity, richer geometric variations, and higher visual fidelity.
We compare our Rendered Dataset with datasets rendered by ObjectMover (Yu et al., 2025) and 3DIT (Michel et al. 2023). As shown in Figure g our dataset provides more realistic scenes and objects than the 3DiT dataset, and also captures more complex lighting and shadow phenomena. Compared to the ObjectMover dataset, Rendered Dataset includes not only more diverse and realistic environments but also more challenging object-scene interactions (e.g.. the lower-left example of Figure 9 moving sculpture is partially occluded by wire fence). Our Rendered Dataset also captures detailed object geometry and appearance, making it one of the most compre- hensive and high-quality datasets for object movement currently available.
C DATA FILTERING CRITERIA
To ensure high-quality and physically plausible samples, conducted large-scale human-in -aman--n-thee- in-the- loop filtering process over three weeks with a 20-person annotation team. Each image-mask pair was manually inspected according to the following criteria:
Spatial coherence. Verify the spatial coherence of the moved object's placement. The object must be integrated naturally within its environment, demonstrating believable relationship with sur- rounding objects in terms of scale, contact, and alignment. This requires ensuring it rests securely on supporting surfaces without floating, maintaining appropriate clearance avoid interpenetration,
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and following the scene's functional logic to prevent unrealistic positioning. The overall goal is to achieve a seamless and contextually appropriate fit.
Feature consistency. It is imperative to ensure that the manipulation of object preserves the absolute integrity of its intrinsic visual properties. This means the object's fundamental character- includdig its geometric form (shape, scale), surface qualities (texture, material), and color values must remain invariant relative to its original state. Any dynamic changes, such as perspective adjustments, should be applied consistently across the entire scene to maintain this fidelity.
Illumination consistency. Ensure the moved object's lighting and shadows are physically correct. Verify that shadow direction, softness, and length match the scene's lights, and that it correctly occludes light and casts contact shadows to avoid floating look.
Controlled generation. Confirm that the edited image strictly adheres the intended transforma- tion instructions without introducing unexpected artifacts or deviations in content.
Only samples satisfying all four criteria were retained, resulting in over 100,000 high-quality im- mask pairs in the final AIGC Dataset.
D IMPLEMENTATION DETAILS
2024), an inpainting model based on the DiT architecture. We replaced the original T5 text encoder (Raffel et al. (2200) with SigLIP image encoder (Zhai et al. 2023) for textual inputs, fine-tuned via LoRA (rank 256) (Hu et al.) 2022). All images were processed at resolution of x024x 1024 with batch size of 4. Training used the Prodigy optimizer (Mishchenko & Defazio 2023) with safeguard warmup, bias correction, and 0.01 weight decay, on 4 NVIDIA H100 GPUs (80GB each). The model was trained on the proposed dataset for about 5000 steps. Inference employed 28 denoising iterations, and the training objective followed the flow matching framework (Lipman et al. 2022).
Our GeoEdit builds upon FLUX.1 Fill [dev] (Black Forest Labs,
E USER STUDY DETAILS
To comprehensively evaluate perceptual quality, conducted formal user study. The study was structured around three key dimensions: (1) Quality, assessing visual realism and the absence of artifacts; (2) Consistency, evaluating the preservation of the original subject and background in- tegrity; and (3) Effecttiveness, measuring how accurately the result realizes the intended geometric transformation.
The study was hosted as an online survey to ensure reproducibility. We recruited 33 professional annotators evaluate 40 distinct instances randomly sampled from GeoBench (Zhu al.) (025) (10 instances each for Move, Rotate, Resize, and 3D-edits). In each trial, participants viewed the source image, a visualization of the editing prompt, and the anonymized outputs from all competing methods. To mitigate positional bias, both the sample presentation and the method outputs were fully randomized, For each instance, annotators selected the top-1 result that best satisfied each of the three perceptual criteria, yielding total of 3,960 valid votes.
The baseline methods varied by task. For 2D-edits, we compared against DesignEdit (Jia et al., 2024), Diffusion Handles (Pandey et al., 2024), DragDiffusion (Shi et al. 2024). FreeFine (Zhu al. 2025). GeoDiffuser (Sajnani et al. 2025), Magic Fixup (Alzayer et al. 2025), and Mo- tionGuidance (Geng & Owens 2024). For 3D-edits, the comparison set included Diffusion Handles, FreeFine, and GeoDiffuser.
As detailed in Fig. 8 and Table GeoEdit consistently achieved the highest user preference rates across both 2D and 3D edits. Furthermore, when aggregated by perceptual dimension, our method secured a decisive lead in Quality, Consistency, and Effectiveness. This strong subjective prefer- ence, which aligns with our quantitative findings, underscores the practical utility and robustness of GeoEdit in producing high-fidelity, artifact-free results that faithfully execute user commands.
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Table 3:
User research results statistics. (Votes aggregated across three perceptual dimensions: Image Quality, Consistency, and Editing Effectiveness.)
2D-Fdits
Method
3D-Edits
Total
Move
Resize
Rotate
2D Total
DesignEdit(ia ct al. 2024]
207
159
38
404
404
DragDiffusion (hi et al. 2024
8
29
25
62
62
Magic Fixup Alzayer al. 2025
213
169
274
656
655
MotionGuidance GGng & Owens
2024
0
0
1
Diffusion MandleesPaande el al.
2024
15
16
8
39
13
52
FreeFine (Zhu ct al. 2025
88
93
210
391
387
778
GeoDiffuscr(Sajnnn al. 2025
75
29
42
146
129
275
Ours
384
494
393
1271
461
1732
F ADDITIONAL VISUALIZATION OF ATTENTION MODULATION ABLATION RESULTS
In figure TQ we present additional visualizations of different attention-modultie variants. We
observe that ESA produces lighting and shadow effects that are more realistic and closer to the ground truth, demonstrating its superior performance.
Source
Prompt
Standard
Hard
Ours
GT
Figure 10: Qualitative visualizations of attention modulation ablation results.
G ABLATION STUDY ON THE HYPERPARAMETERS IN ESA
As discussed in Section geometric editing involves both object insertion and background restora- tion. Accordingly, we introduce two hyperparameters, a; and dg, which control the scaling of attention in the insertion and restoration regions. In the table 5, We explore the effects of different
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settings for these hyperparameters, and the results indicate that a1 0.1 and 0g overall performance.
yield the best
Table 4: Ablation Study on the Hyperparameters in ESA and the best results are in bold.
Hyperparameters
Metrics
Editing Tasks
01
FID
DINOv2.
KDJ
SUBC1
BC1
WHY
MDJ
1.0
0.1
27.09
107.75
0,051
0.900
0.978
0.063
11.33
1.0
0.5
28.24
111.74
0,051
0.900
0.977
0.061
10.57
1.0
27.05
105.01
0.051
0.898
0.976
0.071
11.03
0.5
0.1
27.13
107.97
0.051
0.898
0.977
0.068
13.26
0.5
0.5
2D-edits
28.68
112.00
0.051
0.896
0.977
0.071
10.40
0.5
1.0
29.11
114.77
0.051
0.894
0.975
0.075
10.95
0.1
0.1
26.07
99.54
0,051
0.897
0.978
0.074
13.79
0.1
0.5
28.08
112.52
0.051
0.897
0.977
0.072
10.70
0.1
1.0
25.28
94.79
0.051
0.908
0.977
0.057
9.32
1.0
0.1
71.35
387.55
0.055
0.827
0.980
0.086
20.07
1.0
0.5
73.35
399.68
0.055
0.823
0.978
0.066
21.22
1.0
1.0
69.56
381.68
0,055
0.831
0.978
0.089
18.98
0.5
69.75
385.01
0.055
0.829
0,977
0.078
19.82
0.5
31D-edits
67.90
355.50
0.055
0.833
0.979
0.099
18.14
0.5
1.0
72.56
394.12
0.055
0.828
0.973
0.090
20.47
0.1
0.1
67.96
360.67
0.055
0.833
0.980
0.092
19.01
0.1
0.5
69.57
367.44
0.055
0.832
0.979
0.097
19.99
0.1
64.30
350.69
0.054
0.840
0.977
0.051
18.08
H ABLATION STUDY OF ESA HYPERPARAMETERS UNDER DIFFERENT ILLUMINATION CONDITIONS
Table 5: Ablation Study on the Hyperparameters in ESA under High- High-illumination Setting. Best
results are bold.
Hyperparameters
Metrics
Editing Tasks
Q1
Q22
FID
DINOv2
KDJ
SUBC1
BC1
WEI
MD.
1.0
0.1
27.19
107.87
0.051
0.901
0.977
0.064
11.28
1.0
0.5
28.33
111.85
0.051
0.898
0.979
0.059
10.52
1.0
27.14
105.13
0.051
0.897
0.975
0.073
11.00
0.5
27.25
108.09
0.897
0.978
0.067
13.22
0.5
0.5
2D-edits
28.79
112.12
0.051
0.895
0.976
0.072
10.36
0.5
1.0
29.21
114.89
0.051
0.893
0.974
0.076
10.91
0.1
0.1
26.17
99.66
0.051
0.898
0.979
0.073
13.75
0.1
0.5
28.18
112.64
0.051
0.896
0.978
0.071
10.66
0.1
25.37
94.89
0.907
0.976
1.0
0.1
71.42
387.61
0.055
0.826
0.981
0.085
20.09
1.0
0.5
73.28
399.58
0.055
0.824
0.979
0.067
21.18
1.0
1.0
69.51
381.73
0.055
0.830
0.977
0.088
18.96
0.5
0.1
69.71
384.95
0.055
0.830
0.976
0.079
19.80
0.5
3D-edits
67.86
355.44
0.055
0.832
0.980
0.098
18.12
0.5
1.0
72,60
394.18
0.055
0.829
0.974
0.091
20.49
0.1
0.1
67,93
360.71
0.055
0.832
0.981
0.093
19.03
0.1
0.5
69.54
367.38
0.055
0.833
0.978
0.096
19.97
0.1
64.27
350.63
0.054
0.839
0.976
0.050
18.06
To evaluate whether ESA's improvement is sensitive to the choice of the scaling factor aı and a2 under different lighting conditions, we extended our ablation study by categorizing the evaluation data into three illumination regimes: high, medium, and low light. We then conducted separate
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Table 6: Ablation Study the Hyperparameters in ESA under Medium- Redium-illumination Setting. Best
results are in bold.
Hyperparameters
Metries
Editing Tasks
FID
DINOv2
KDJ
SUBC1
BCT
W..
MD
1.0
0.1
27.01
107.65
0.051
0.899
0.978
0.062
11.37
28.16
111.63
0.051
0.901
0.976
1.0
1.0
27.00
104.91
0.051
0.899
0.977
0.069
11.07
0.5
0.1
27.04
107.93
0.051
0.899
0.976
0.069
13.30
0.5
0.5
2D-edits
28.59
111.96
0.051
0.897
0.978
0.070
10.44
0.5
1.0
29.02
114.73
0.052
0.895
0.974
0.074
10.99
0.1
26.00
99.42
0.051
0.896
0,977
0.075
13.83
0.1
0.5
28.01
112.40
0.051
0.898
0.976
0.073
10.74
0.1
1.0
25.21
94.69
0.051
0.909
0.977
0.056
9.36
1.0
0.1
71.31
387.47
0.055
0.828
0.979
0.087
20.04
0.5
73.41
399.80
0.055
0.822
0.977
0.065
21.26
69.62
381.62
0.055
0.832
0,979
0.090
0.5
0.1
69.80
385.09
0.055
0.828
0.978
0.077
19.84
0.5
0.5
3D-edits
67.95
355.59
0.055
0.834
0.978
0.100
18.16
0.5
1.0
72.53
394.06
0.055
0.827
0.972
0.089
20.44
0.1
68.00
360.59
0.055
0.834
0.979
0.091
19.00
0.1
69.61
367.50
0.980
0.1
1.0
64.34
350.75
0.055
0.841
0.978
0.052
18.10
Table 7: Ablation Study on the Hyperparameters in ESA under Low-illumination Setting. Best results in bold.
Hyperparameters
Metrics
Editing Tasks
Q1
Q2
FID4
DINOv21
KDJ
SUBC1
BCT
WE..
MDJ
1.0
0.1
27.08
107.73
0.051
0.900
0.977
0.063
11.31
1.0
0.5
28.23
111.72
0.051
0.900
0.977
0.061
10.57
1.0
1,0
27.04
105.00
0.051
0.898
0.976
0.071
11.03
0.5
0.1
27.12
107.95
0.051
0.898
0.977
0.068
13.26
0.5
21D-edits
28.67
111.98
0.051
0.896
0.977
0.071
10.40
0.5
1.0
29.10
114.75
0.051
0.894
0.975
0.075
10.95
0.1
0.1
26.06
99.54
0,051
0.897
0.978
0.074
13.79
0.1
0.5
28.07
112.52
0.051
0.897
0.977
0.072
10.70
0.1
1.0
25.27
94.79
0.051
0.908
0.977
0.057
9.32
1.0
0.1
71.29
387.51
0.055
0.827
0.980
0.086
20.08
1.0
0.5
73.39
399.75
0.055
0.823
0.979
0.065
21.24
1.0
1.0
69.60
381.65
0.055
0.831
0.978
0.089
18.99
0.5
0.1
69.78
385.02
0.055
0.829
0.977
0.078
19.83
0.5
0.5
3D-edits
67.93
355.52
0.055
0.833
0.979
0.099
18.15
0.5
72.55
394.09
0.828
0.973
0.090
0.1
0.1
67.98
360.64
0.055
0.833
0.980
0.092
19.01
0.1
0.5
69,59
367.45
0,055
0.832
0.979
0.097
20.00
0.1
1.0
64.32
350.70
0.054
0.840
0.977
0.051
18.09
experiments for each group using the same set of & values. As shown in tables 5 the a1 and ag selected in main experiments consistently yields the best performance across all three lighting conditions. This indicates that ESA's gains are stable and not strongly dependent on illumination- specific tuning.
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Image
Source Mask
Target Mask
Anydoor
Insert Anthing
ObjectMover
Ours
GT
Figure 11: Qualitative comparison of different editing approaches on the ObjMove-A dataset.
EXPERIMENTAL EVALUATION ON THE OBJMOVE-A (YU ET AL.. 2025 BENCHMARK
We conduct a comprehensive evaluation on ObjMove-A, a benchmark dataset providing ground- truth images for object movement tasks. We employ suite of eight metrics quantify different aspects of performance. To evaluate the fidelity of the inserted object, we compute object-level metrics which include DINO-Score (Caron al. 2021). CL.r--corre (Radford et al.) 2021). and
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Table 8: Quantitative results object insertion and manipulation tasks. We report eight metrics for
image quality, consistency, and editing effecttivnness. Our method achieves the best performance.
Methods
PSNR
SSIM
Clip-Scoref
DINO-Score?
FIDI
Lpips.
ReMOVE
DreamSim
3DT Michel 2023
18.18
0.817
57.41
57.41
95.65
0.461
0.651
0.164
AnyDoor Chen 2024
23.42
0.897
85.90
85.90
41.11
0.212
0.860
0,059
DreamFusc Huang 2025a
18.55
0.815
82.04
82.04
76.04
0.326
0.480
0.106
Insert Anything Song 2025
22.30
0.842
86.21
86.21
44.79
0.297
0.848
0.055
Magic Fixup Alzayer 2025
22.83
0.842
80.51
80.51
45.09
0.330
0.844
0.055
ObjectMover Yu
24.06
0.856
82.18
82.18
36.84
0.327
0.874
0.045
Ours
26.18
0.891
92.90
92.90
23.65
0.180
0.874
0.023
DreamSim Fuu et al. 2023 on the cropped target region. Since the task also requires removing the object from its original location, we use ReMOVE (Chandrasekar et al., (222)). We further measure overall image similarity using PSNR and SSIM applied the full generated image. Additionally, we report FID (Heusel et al. 2017) and LPIPS (Zhang et al. 2018) to evaluate global realism and perceptual quality.
Among the baselines, AnyDoor (Chen et al. 2024), DreamFuse (Huang et al.] 2025a), and In- sert Anything (Song et al. 2025) are object insertion models. our pipeline, we first use Om- niEraser (Wei et al. 2025) remove the original object and obtain a clean background, after which the object is inserted into the target location. Quantitative results demonstrate that our approach achieves state-of-the-an art performance across the majority of these metrics, superiority that is fur- ther evidenced by the visual comparisons presented in Figure [1] where our model produces the most realistic and geometrically consistent edits among all existing methods.
J LIMITATIONS AND FUTURE WORK
GeoEdit may struggle in scenes with strong secondary physical effects. For example, when torcycle kicks up sand or dust, moving the object may not fully transfer these extended effects the new location. These dynamic, particle-based phenomena illumination-related effects (e.g., shading or soft shadows), which GeoEdit is specifically designed handle. future work, we plan to further reduce the model's 's reliance on external 3D priors, im- prove its ability to model complex physical interactions, and strengthen its handling of illumination and shadow generation.
fundamentally different from
K
THE USE OF LARGE LANGUAGE MODELS (LLMs)
During the preparation of this manuscript, we leveraged large language models (LLMs) to assist with several aspects of writing and document organization. First, LLMs were used to search for relevant literature based on our research focus and to generate corresponding BibTeX entries for cited papers. Second, they assisted in formatting and refining tables, ensuring consistent style, alignment, and readability across the manuscript. All outputs suggested or generated by LLMs carefully reviewed and edited by the authors to maintain correctness, clarity, and adherence to the intended scientific meaning. This workflow allowed us to improve efficiency in manuscript preparation while retaining full authorial control the content.
L SOURCE CODE AVAILABILITY
The source code package is provided in the supplementary materials to facilitate reproducibility and enable readers to experiment with our proposed methods.
24


Reviews:
[{"review_id": "Wv7w7RZxlI", "reviewer": "Reviewer_4ZEL", "summary": "This paper proposes GeoEdit, a diffusion-transformer–based framework for geometric image editing.\nTwo key components are introduced: (1)Geometric Transformation module – performs translation, rotation, and scaling in a 3D-aware way via object reconstruction. (2)Effects-Sensitive Attention (ESA) – a soft guidance mechanism that modulates attention logits to preserve lighting and shadow realism, theoretically supported by a KL-divergence analysis.\n\nThe authors also construct RS-Objects, a dataset of 120k rendered + synthetic image pairs designed for geometric transformations and visual-effects learning.Extensive experiments on GeoBench show consistent quantitative and qualitative improvements over strong baselines such as FreeFine, GeoDiffuser, and Diffusion Handles.", "questions": "The proposed ESA biases attention distribution statistically rather than modeling physical light transport. So, can GeoEdit guarantee physically correct shadow direction, reflection geometry, or color temperature consistency, especially when global illumination changes?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1 Clear motivation and problem definition – focuses on geometric (translation / rotation / scaling) image editing, which remains under-explored compared to semantic or text-guided edits.\n\n2 Comprehensive dataset pipeline – the RS-Objects dataset seems carefully designed (render + AIGC + human), addressing a genuine data gap.\n\n3 Strong experiments – covers both 2D and 3D edits, reports seven metrics, and includes ablations and a user study\n\n4 Readable writing and solid figures", "weaknesses": "1 Dataset authenticity & reproducibility. whether any real photographs with ground-truth geometric edits exist for validation. Public release status is unclear\n\n2 Many baselines are test-time or training-free. The proposed method is training-based with a large custom dataset, so the comparison is not entirely apples-to-apples. how much overhead does ESA add versus standard DiT inpainting?\n\n3 Limitations are not discussed, what is the thing that GeoEdit can not achieve? Discussing these aspects is crucial for guiding future research and fair benchmarking.", "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": "92kc7Hi6Z1", "reviewer": "Reviewer_YQsq", "summary": "The paper proposes GeoEdit, a diffusion-transformer–based framework for geometric image editing—tasks involving object translation, rotation, and scaling while maintaining realism (e.g., lighting and shadow coherence).  GeoEdit outperforms prior works such as DragDiffusion, GeoDiffuser, and FreeFine on FID, DINOv2 distance, and geometric consistency metrics across both 2D and 3D editing tasks. Ablations show that ESA and dataset composition are critical for performance.", "questions": "see weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper presents a clearly structured geometric editing pipeline with well-defined, reproducible steps. Each transformation—translation, rotation, and scaling—is handled through explicit procedures. The detailed description of these steps provides strong methodological clarity and makes the approach readily reproducible.\n\n2. The proposed RS-Objects dataset is thoughtfully designed to align with the objectives of geometric image editing. It employs a two-stage rendering-to-synthesis pipeline that produces roughly 120,000 image–mask pairs. The process combines high-quality Blender-rendered samples, mesh-based scene generation, large-scale LoRA-driven synthesis (pre-filtered from about 800,000 candidates), and a final human filtering stage to ensure coherence and illumination realism. This carefully engineered dataset directly supports the training objectives of GeoEdit and provides a valuable resource for future research on geometry-aware image editing.", "weaknesses": "1.\tTheoretical Contribution and Clarity of ESA\nTheorem 3.1 offers a limited theoretical contribution. Since the hard-modulated attention focuses exclusively on insertion tokens, its KL divergence from the ideal attention map is trivially infinite, and showing that the ESA variant achieves a smaller divergence is therefore not a particularly informative result. The theorem appears unnecessary in its current form. The central issue is not the inequality itself, but the rationale behind defining the similarity for edited tokens in ESA as $q_i k_j^\\top / \\sqrt{d} + \\delta$. The paper should clarify the intuition for introducing this additive bias $\\delta$, its relationship to the observed attention patterns, and the impact of the hyperparameter $\\alpha$ that scales it. Without such discussion, the theoretical section feels more decorative than explanatory.\n2.\tArchitectural Substitution and Fairness of Comparison\nThe implementation introduces an architectural change by replacing the T5 text encoder with SigLIP and fine-tuning the model via LoRA on a FLUX.1-Fill DiT backbone.\n(a) The description “SigLIP image encoder for textual inputs” is ambiguous and requires clarification. For instance, Figure 2 does not make it clear how SigLIP features and mask references are integrated into the model architecture or how textual prompts, if any, are represented.\n(b) Moreover, several baselines appear not to have been re-run on this modified backbone. Without controlling for differences in encoder strength and conditioning design, part of GeoEdit’s reported performance gain might arise from the stronger base model rather than from the proposed ESA or geometric modules. A discussion or ablation controlling for these factors would make the comparisons more convincing.", "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": "8tMfvsCnhr", "reviewer": "Reviewer_XX6q", "summary": "This paper introduces GeoEdit, a diffusion-transformer–based framework for geometric image editing that can accurately perform translation, rotation, and scaling of objects within complex scenes while preserving realistic lighting and shadow effects.\nThe key innovations are:\n\n* Geometric Transformation Module – utilizes 3D reconstruction (via Hunyuan3D) to apply parametric transformations with precise control.\n\n* Effects-Sensitive Attention (ESA) – a soft attention modulation designed to better model lighting and shadow consistency.\n\n* RS-Objects Dataset – a large-scale (120K samples) dataset combining rendered and synthetic data to support geometric editing training.\n\nGeoEdit is built upon the FLUX.1-Fill DiT backbone and shows superior results on both 2D and 3D edit benchmarks, achieving better FID, DINOv2, and consistency metrics than prior works such as FreeFine, GeoDiffuser, and DiffusionHandles.", "questions": "* The technical novelty requires further justification\n\n* How sensitive is ESA’s improvement to the choice of the scaling factor α in different lighting conditions?\n\n* Can GeoEdit function with other external 3D priors models (other than Hunyuan-3D), or without external 3D priors?\n\n* Can comparisons be made using the same Flux backbone?\n\n* What are the model’s failure modes—e.g., in cluttered or occluded scenes?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* Well-written and structured paper with clear motivation and consistent organization.\n\n* Strong experimental performance on both quantitative and qualitative metrics across multiple tasks.\n\n* Comprehensive dataset (RS-Objects) with rigorous construction and filtering criteria; could benefit the broader community.\n\n* Practical application value in realistic scene editing and geometric transformation control.", "weaknesses": "* The proposed ESA module appears conceptually similar to a soft attention bias, and its correlation with lighting effects is not convincingly demonstrated.\n\n* The method’s heavy reliance on external geometry models (e.g., Hunyuan-3D) for 3D reconstruction compromises its originality and self-containment. It also remains unclear how GeoEdit performs when alternative geometry backbones are employed.\n\n* The model is built upon the Flux backbone, while several baselines are not, raising concerns about the fairness of comparisons.\n\n* The potential overfitting to the RS-Objects synthetic dataset may further limit the model’s generalization to real-world scenarios.\n\n* In some qualitative examples (e.g., Fig. 7), objects like bottles and razors do not fully align with the target sketch positions—they shift slightly, suggesting weak spatial control.", "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)