Dragging with Geometry: From Pixels to Geometry-Guided Image Editing
Authors: Xinyu Pu
Hongsong Wang
Jie Gui
Pan Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Model; Drag-based Image Editing
Primary area: generative models

Abstract: Interactive point-based image editing serves as a controllable editor, enabling precise and flexible manipulation of image content. However, most drag-based methods operate primarily on the 2D pixel plane with limited use of 3D cues. As a result, they often produce imprecise and inconsistent edits, particularly in geometry-intensive scenarios such as rotations and perspective transformations. To address these limitations, we propose a novel geometry-guided drag-based image editing method—GeoDrag, which addresses three key challenges: 1) incorporating 3D geometric cues into pixel-level editing, 2) mitigating discontinuities caused by geometry-only guidance, and 3) resolving conflicts arising from multi-point dragging. Built upon a unified displacement field that jointly encodes 3D geometry and 2D spatial priors, GeoDrag enables coherent, high-fidelity, and structure-consistent editing in a single forward pass. In addition, a conflict-free partitioning strategy is introduced to isolate editing regions, effectively preventing interference and ensuring consistency. Extensive experiments across various editing scenarios validate the effectiveness of our method, showing superior precision, structural consistency, and reliable multi-point editability. Project page: https://xinyu-pu.github.io/projects/geodrag.

Published as a conference paper at ICLR 2026
DRAGGING WITH GEOMETRY: FROM PIXELS TO GEOMETRY-GUIDED IMAGE EDITING
Xinyu Pu!
Hongsong Wangh. Jie Gui Pan Zhou
'Southeast University ?Purple Mountain Laboratories 'Singapore Management University
{xinyupu, hongsongwang, gu giejieeeeeeee @eeee edu. en panzhou3@gmail com
ABSTRACT
Interactive point-based image editing serves as a controllable editor, enabling precise and flexible manipulation of image content. However, most drag-based methods operate primarily on the 2D pixel plane with limited use of 3D cues. As result, they often produce imprecise and inconsistent edits, particularly geometry-intensive scenarios such as rotations and perspective transformations. To address these limitations, we propose a novel geometry-guided drag-based image editing method-GeoDras
which addresses three key challenges: 1) in- corporating 3D geometric cues into pixel-level editing, 2) mitigating discontinu- ities caused by goometry-only guidance, and 3) resolving conflicts arising from multi-point dragging. Built upon a unified displacement field that jointly encodes 3D geometry and 21) spatial priors, GeoDrag enables coherent, high-fidelity, and structure-consistent editing a single forward pass. In addition, a conflict-free partitioning strategy is introduced isolate editing regions, effectively prevent- ing interference and ensuring consistency. Extensive experiments across various editing scenarios validate the effectiveness of our method, showing superior pre- cision, structural consistency, and reliable multi-point editability. Project page:
INTRODUCTION
Image editing (Cho et al., 2024; Mokady et al., 2022; Shi et al., 2024) has seen remarkable progress in recent years, largely driven by the emergence of powerful generative models (Hertz al., 2022: Kawar et al., 2023; Mokady et al., 2022; Yang et al., 2024b; Zhou et al., 2025). Among these, text-guided image editing (Ruiz et al., 2023; Subberman-Spiegeleen
et al., 2024; Zhou et al., 2025; Nguyen et al., 2024) has become a widely adopted paradigm, allowing users to modify images using natural language prompts. While expressive and flexible, this approach often falls short in providing fine- fine-grained spatial control- especially when precise, localized, geometry-sensitive edits are required (Pan et al., 2023; Shi et al., 2024; Liu et al., 2024; Ling et al., 2024; Hou al., 2024; Zhang et al., 2025; Chen et al., 2024).
To overcome these limitations, point-based image editing (Pan et al., 2023; Shi et al., 2024; Liu et al., 2024; Ling et al., 2024; Hou et al., 2024; Zhang et al., 2025; Chen et al., 2024) has emerged as a powerful alternative, gaining traction for its user-friendly, precise manipulation. By enabling point pairs, methods such DragGAN (Pan et al., 2023) and its
users to specify handle-to-targe successors (Shi et al., 2024; Liu et al., 2024; Ling et al., 2024; Hou et al., 2024; Zhang et al., 2025; Chen al., 2024; Shin et al., 2024) offer intuitive and precise image manipulation. They often adopt a two-step pipeline: (1) motion supervision, dragging the handle point toward the target, and (2) point tracking, monitoring the handle's updated position during editing.
Despite their success, existing point-based methods often rely on iterative gradient- based optimization, which is computationally intensive and impractical for real-time use. To im- prove efficiency, recent approaches like FastDrag (Zhao et al., 2024) and RegionDrag (Lu et al., 2024) propose one-step editing via latent relocation based on dense displacement fields constructed over user-specified regions. However, these methods operate purely in the 2D pixel plane, and ignore
Motivation.
"Corresponding authors. Code available http://giinabbcookiyy--mCoooooo

---

Published as a conference paper at ICLR 2026
Original Semantic
(a)
Plane-based
Geometry-based
Shifts
Distance
Depth
Skifts
One-step Editing
(b)
Only Geometry
Conflicting Drag
Shifts
Depth
Result
Ideal result
Input
Drag
2nd Drag
Conflict
Editing
Figure 1: (a) Displacement fields. Left: Plane- base- estimation (e.g., FastDrag (Zhao et al., 2024)) lacks geometric awareness and introduces structural inconsistencies under geometry-intensive edits like rotation. Right: Our geometry-aware GeoDrag aligns with 3D structure-nea. pixels move more, far pixels less. (b) Relying solely 3D geometry can produce discontinuous displacements near object boundaries. (c) Two nearby handle-target pairs with opposing directions (c.g.. leftward and rightward drags) may conflict, causing displacement cancellation and editing failure. The color legend (Shift/Distance/Depth) is provided in Appendix H.
underlying 3D scene geometry. This becomes critical limitation for complex transformation geometry-intensiv edits-like rotations or perspective whetee 2D-only reasoning leads to structural artifacts, unnatural deformations, and spatial misalignment. For example, plane-aware strategy decays displacement strength based solely pixel distance, and ignores 3D geometry, resulting in perceptual distortions of human's 's face as shown in Fig. ((a, left).
To improve the realism and semantic consistency of image editing, incorporating 3D geometric in- formation is essential, as it offers richer structural cues beyond the 2D pixel plane. However, this introduces three key challenges. 1) How geometry be integrated into pixel-level editing? While 3D cues (e.g., depth maps) are informative, they do not align directly with pixel-wise oper- ations. For example, drag defined in the image plane may become ambiguous in 3D due to per- spective and depth variation variation-hhihlightiin
the need for a mechanism to incorporate geometry into 2D editing pipelines. 2) geometry alone sufficient for high-quality editing? Although helpful for preserving global structure, geometry alone may cause issues. Displacement fields based solely on 3D geometry (e.g.. depth) often become discontinuous near object boundaries (see Sec. 3.1), disrupting the diffusion process and causing semantic artifacts as shown in Fig. 1(b). 3) How to reconeile guidance from multiple handle-target pairs? real scenarios, users often specify mul- tiple drag points. If their displacement fields overlap especially with opposing directions they can destructively interfere, even with distance-base based weighting as in FastDrag (Zhao et al., 2024). This leads displacement cancellation and failed edits illustrated by Fig. 1(c). These challenges call for unified framework that integrates 3D geometry and 2D cues while resolving conflicts for precise and coherent manipulation.
Contributions. To tackle these challenges, we propose GeoDrag-a drag-based image editing framework that is both geometry-aware and plane-aware, ensuring coherent, high-fidelity, and fast one-step image manipulation. Built upon the latent consistency model (LCM) (Luo et al., 2023), GeoDrag predicts dense displacement field directly the noisy latent space at a specific diffusion cnnestep-circumventiii
iterative optimization and enabling fast and efficient editing. GeoDrag Geometry- introduces three key innovations resolve the above three challenges, respectively. aware field modeling: To resolve the mismatch between 3D geometry and 2D editing, GeoDrag introduces novel influence function that modulates displacement strength based on 3D geometric relationships. By leveraging depth contrast, it ensures that nearby regions undergo stronger pro- jective motion, while distant areas move more subtly prey---ireeiingggg 3D structure (see the well-edited human face in Fig. ((a, right)). 2) Spatial plane modulation: Addressing the limitations of using 3D geometry alone, GeoDrag incorporates a spatial influence function based on 2D pixel plane. This improves local structure preservation and editing precision, especially in flat or geometry-ambiguon ambiguous
2

---

Published as a conference paper at ICLR 2026
User Drag
GeoDrag
User Drug
GeoDrag
Drappingg Accuratey Index
Draglliteden Draghale
Proetrezt Faetiig
GroDrag
Menory (GI))
(Ours)
Fast
Drag
6:5
1.4x
Gustrag
Drag.
Free
18.00
6.3-
1.1x
Drag
I836
21,12
18,33
Mean Distance
Diflusion
22.46
(a) Visual Quality
(b) Quantitative Results
(c) Time and GPU Memory
Figure 2: Experimental Comparison. (a) Representative edits across diverse scenarios. (b) Quanti- tative results on DragBench: lower MD and DAI indicate more accurate editing. (c) Runtime and memory comparison across our GeoDrag and previous SoTAs.
regions. 3) Conffict-Free Partitioning: To mitigate conflicts in multi-point editing, GeoDrag seg- ments the editing mask into non-overlapping sub-regions, each associated with its nearest handle point. Independent displacement fields are computed per region, avoiding destructive interference and ensuring coherent multi-point manipulation. Together, these contributions allow GeoDrag to perform fast, high-quality, and semantically consistent edits in a single step aadanninnn the state of controllable, geometry-aware image manipulation.
Extensive experiments verify the effectiveness and efficiency of GeoDrag. As shown in Fig. 2, we provide three-part visualization: (a) Visual Quality GeoDrag delivers high-quality edits across challenging scenarios, including multi-ponn point editing (e.g., bulb), structure-preser preserving defor- mation (e-g., mountain), localized manipulation (e.g., lion's mouth), and geometry-aware tasks (e.g., face rotation); (b) Quantitative Results GeoDrag achieves superior alignment accuracy, improving runner-up's dragging accuracy index metric (DAI) by 1.4x and mean distance metric (MD) by 1.1x; (c) Efficiency GeoDrag offers favorable trade-off between speed and memory. While its runtime gain is modest, remains lightweight and highly competitive due to its strong editing performance.
2 RELATED WORK
Text-Based Image Editing. Text-based image editing manipulates images via natural language prompts. Gal et al. (2023) utilize textual inversion for personalized generation by embedding user- specific concepts. DiffusionCLIIP (Kim et al., 2022) fine-tunes diffusion models with CLIP super- vision, while Prompt-to-Poome (Hertz et al., 2022) achieves train-free editing by modifying cross- attention maps. Null-text inversion (Mokady et al., 2022) optimizes unconditional text embeddings for faithful reconstruction of real images. Imagic (Kawar et al., 2023) interpolates between text and image-specific embeddings but requires per-task tuning. Other approaches like CycleDiffusion (Wu & la Torre, 2023) and DDPM inversion (Huberman Spieerman--Siigggllaaa et al., 2024) explore latent spaces to support high-quality editing. InstructPix2Pix (Brooks et al., 2023) trains on instruction-image pairs, enabling direct prompt-driven editing. RPG (Yang al., 2024b) introduces multimodal L.M for reasoning and planning editing. Despite their flexibility, text-based methods often lack spatial precision and fine-grained control, limiting their applicability for detailed editing tasks.
Interactive Point-Based Image Editing, Point-based methods enable precise manipulation by directly dragging image elements. DragGAN (Pan al., 2023) introduced this paradigm with GANs, later improved by diffusion-based approaches (Ho et al., 2020; Rombach et al., 2022; Luo al., 2023) like DragDiffusion (Shi et al.. 2024), which integrates motion supervision and identity- preserving fine-tuning. Extensions enhance usability (EasyDrag (Hou et al.. 2024)), stability (Free- Drag (Ling al., 2024)), semantic control (DragNoise (Liu al., 2024)), or robustness (Good- Drag (Zhang et al., 2025), StableDrag (Cui et al., 2024)). To boost efficiency, FastDrag (Zhao et al., 2024), RegionDrag (Lu et al., 2024), and SDEDrag (Nie et al., 2024) use lightweight latent manip- ulations, while DragonDiffusiin (Mou et al., 2024b), DiffEditor (Mou et al., 2024a), and Instant- Drag (Shin et al., 2024) introduce energy- based or real-time formulations. Despite progress, they remain limited to 2D pixel reasoning, restricting realism in ecometry-sensittee this with GeoDrag for controllable, structure structure-preserving. and efficient image editing. parallel, FlowDrag (Koo et al., 2025) uses mesh reconstruction and iterative deformation to improve editing
edits. We address
3

---

Published as a conference paper at ICLR 2026
Geometry-Aware Field
Spatial Plane Modulation
Confllct-Free Partitioning
Modeling
Depitt Prediction
Influence Function
Influence Fuueeton
Geometry-aware
Original Image
Plane-aware
ZT
Reference
Guidance
Edited Image
F(
Confliet-Free Partitioning
BNNI
Latent
Segmendation
Figure Overall framework of GeoDrag. In drag pipeline, the mask is split into sub-regions, each with pair of drag points. For each sub-region, the geometry- and plane-aware displacement fields are independently calculated (see Sec. 3.1 and Sec. 3.2). Subsequently, these fused fields aggregated without conflict (see Sec. 3.3). The final field enables one-step editing via latent relocation and interpolation, with reference guidance to preserve semantics.
quality, but at a higher computational cost, that limits its responsiveness. By contrast, GeoDrag achieves geometry-consistent control while remaining responsive for interactive editing.
3 METHODOLOGY
We begin by formalizing the task of interest. Given an image and set of & point pairs {(h;, t,) ) where each h, is a handle point and t; its corresponding target, the goal is to move each h, to- ward t; while preserving semantic consistency and visual realism. Recent efficient methods such as FrstDrag (Zhao et al., 2024) and RegionDrag (Lu et al., 2024) enable a fast one-step editing by computing displacement field RxxWx2 x2 which warps the latent zr at timestep T via forward mapping:
Ti( haitthaattttoo Zr(i))
(1)
Each ft, (ft,,1 f(, defines how much the latent feature at spatial location (i,j) should be shifted along the x and y axes in the image. Next, the modified latent 27 is then passed into a well-trained diffusion model to generate the edited image.
Existing methods (Zhao et al., 2024; Lu et al., 2024; Nie et al., 2024) construct using only 2D pixel-plane heuristics, ignoring the underlying 3D structure. This estimated f often leads to realistic deformations, semantic breaks, and perspective issues-est essucillppciallly edits like rotations or viewpoint shifts (see Fig. (a)). Additionally, multiple handle-target pairs can create overlapping and conflicting displacement fields, causing inconsistent guidance and editing failures (see Fig. (c)).
geometry-sensitive
To address these issues, we propose GeoDrag which jointly leverages 3D geometry and 2D spa- tial priors to produce accurate and coherent displacement fields. As illustrated in Fig. 3, GeoDrag consists of three components: (I) geometry-aware field modeling module that adjusts motion strength using depth cues; (2) a spatial plane modulation that combines 3D and 2D guidance; and (3) conflict-free partitioning that decomposes the editing mask to resolve conflicting drag signals. These components respectively resolve three fundamental challenges associated with geometry- aware drag-based editing, and they together enable high-quality and geometry-consistent image edits in a single step. Moreover, to avoid the over-smoothing often introduced by interpolation, further refine the interpolated latents with masked stochastic DDIM update, which injects ran- domness only inside the interpolated region while keeping the rest deterministic. Formally, given a binary mask M indicating the interpolated area, the sample step is
v āc-1 of ORMM co(z).t ( M).
2 Vāi-12
4

---

Published as a conference paper at ICLR 2026
This post-ineeppolation refinement preserves global coherence, effectively alleviating blur without incurring extra sampling overhead. Below, we elaborate on these three components and their re- solved challenges in turn. Specifically,, focus the case of single handle-targed pair in Sec. 3.1 and Sec. 3.2, and extend the approach to multi-point editing in Sec. 3.3.
3.1 GEOMETRY-AWAL FIELD MODELING
Incorporating 3D geometry into 2D image editing poses a key challenge: pixel-level operations on the image plane do not directly correspond to transformations in 3D space. For instance, applying the same 3D displacement to different points can lead to inconsistent 2D motions due to perspective distortion and depth variation. To bridge this gap, our core idea is to project 3D displacements into the image plane while preserving 3D structure information. Accordingly, we design a geometry- aware influence function that converts 3D drag displacements into pixel plane by incorporating relative depth between pixels and the handle point. This strategy ensures that pixels respond to the displacement in depth more, while those at larger depths move less. By aligning the 2D displacement strength with 3D proximity, the method preserves the consistency of the 3D structure during 2D "dragging", avoiding spatial tearing or inconsistent deformation on the image plane.
manner: pixels with lower depth to handle point are influenced
Specifically, consider a drag operation applied 3D space (x, 2) with 3D displacement tor (ōx, dy. 6z). Given a camera intrinsic K which in unknown in this work, the corresponding projected 2D coordinate (u, v) on the image plane can be computed as follows:
fa = 0
x
2 U
K
[y
Cy
(3)
0
[2]
Applying a small 3D displacement (ōx, σy, 82), its projected 2D shift (8u, ōv) becomes:
( + òx
x
+ δy + 5z
y
du = fz
8e = Jv
(4)
2 8z
where fo and fy are the focal lengths of the camera. Since the drag operations are defined on the 2D image plane (in our task), the motion along the optical axis (i.e., the z-axis) can be reasonably neglected. Thus, Eq. (4) can be simplified as:
du (ōx/22, bv= fy(y/(0
Furthermore, consider another arbitrary 3D point (z',8', 2') which is subjected to the same dis- placement vector (ōær, dy, 8z). We also compute its corresponding 2D displacement (ōu', 5v') as ou' fa and du' = fy ey In this way, combining Eq. (5) yields
du' (z/²))8u, 8o' (z/2')8u)
Eq. (6) implies that (u', v²) with smaller depth (closer to the camera) exhibit greater pixel-plane displacement due to the inverse proportionality between displacement and depth. Based on this observation, the geometry-aware field can be constructed as follows:
fo = (Sn/C)) d = (Sn//)) -t ht-
(7)
where Ç denotes the depth map within mask, and Çn is the depth of handle point h. The scalar serves as a modulation factor, controlling the sensitivity of displacement scaling to depth vari- ations. d is the drag direction from handle h target t. The geometry-aware influence function (i.e.. Eq. (7)) calculates the geoeetry-aware displacement field (as shown in the central blue-shaded region of Fig. 3) based on the underlying 3D structure. By incorporating depth-dependers modula- tion. GeoDrag brings 3D geometric information into 2D drag editing, enabling structure- structure-preserving manipulations. Our strategy resolves the challenge by maintaining geometric consistency between the perceived 2D deformation and the actual 3D transformation.
3.2 SPATIAL PLANE MODULATION
While geometry aware displacement field provides structural consistency by incorporating 3D depth information, it alone is insufficient for producing high-quality edits particcuaullllllyy in regions with fine
5

---

Published as a conference paper at ICLR 2026
details or near object boundaries. This limitation arises because geometry-aware motion distributes influence uniformly in 3D space, making it less responsive to subtle and local deformations in the 2D image plane (see Fig. 1(b)). This is the second major challenge discussed in Sec. 1. To over- come this, we propose a spatial plane modulation strategy that complements the global structure- preserving behavior of the geometry-awar field with local and pixel-level controllability. This hybrid approach enables precise and sharp edits while retaining geometric coherence.
Our fusion mechanism draws inspiration from elastic force propagation: deformation peaks at the force point and decays with distance. Mimicking this behavior, we define a plane-aware field that decays spatially from the handle point. This formulation allows localized and responsive editing, especially near fine structural features. While this idea is conceptually similar to the influence decay used FastDrag (Zhao et al., 2024), our method avoids plane geometric constructions like similar triangles and instead adopts simpler, vectorized formulation that is more computationally efficient and easier integrate.
Given a handle point h and its target point t, the drag vector is = t - h. The displacement at each pixel is then computed by modulating the displacement vector d according to its spatial dis- tance to h. following the spatial influence function:
fp -1 (1- d.
(8)
Figure Illustration of L in the plane- aware field. O and r are the center of the outer circle and radius, respectively.
where 1 Raxx denotes a matrix with all elements as one, P E Rhx denotes the Euclidean distance from each pixel to the handle point h, L E R hxx the maximum propagation distance along the ray from h to each pixel, and 3 controls how sharply the influence falls off with distance.
The propagation restricted within a circular region enclosing the editing mask, ensuring the influ- ence fades smoothly near the mask boundary. As shown in Fig. 4, to compute L, solve for the ray-circle intersection:
h + to 0 2 ,2
-=
(0 h)
(v O h))² (10 h12 r2).
(9)
where v
h is the unit direction vector from he to pixel q, and L /h+tv) gives the maximum
g- h- extent of influence.
To achieve both structural consistency and local flexibility, we fuse the geometry-aware field fa and the plane-aware field fp into a single displacement field f:
f (1 A) fp A fa.
(10)
Here, a spatially adaptive fusion weight based on the distance from each pixel handle point, formulated as X P/(P + 7) Where 2 0 is hyperparameter controlling the balance between global geometry-aware and local plane-aware influence. A smaller favors geometric consistency, while larger y increases responsiveness to localized changes. As shown in the rightmost part of Fig. 3, the fused field integrates 3D geometric priors and 2D plane cues to enable semantically coherent and structure preserving displacements. Since the ideal fusion scale varies across different object sizes and editing regions, we define 7 as a scalar multiple of the diameter of the enclosing mask circle, making the fusion strategy adaptive the editing context.
3.3 CONFLICT-FREI PARTITIONING
When multiple handle-target pairs are involved in drag-based editing, directly aggregating their displacement fields can lead destructive conflicting motion directions. This is the third major challenge outlined in Sec. 1. Such interference often results weakened motion strength, ambiguous displacement patterns, and ultimately, failed or unintuitive edits. Naive approaches like sestance-basel weighting are insufficient in these scenar- ios since they cannot fully decouple the influence of closely spaced or competing drag handles (see Fig. 6). To resolve this, we introduce conflict-free partitioning that enforces local independence by spatially partitioning the editing mask into multiple sub-regions. Each sub-region is exclusively influenced by a single handle point, ensuring that conflicting displacements are isolated and han- dled separately. This strategy significantly improves editing precision and prevents cross handle interference.
when nearby handles induce

---

Published as a conference paper at ICLR 2026
User Drag
DragDiftusion
DragNoise
FreeDrag
FastDrag
Ours
Figure 5: Qualitative comparisons with the state-of-the-art interactive point-based methods. See Appendix D for extended qualitative comparisons, including additional visualizations Fig. 23. In addition, see Appendix G for details how mask selection influences editing behavior. Red points mark handles, and blue points mark targets; the same applies to the following figures.
Given editing mask M, divides it into disjoint sub-regions S; via Eq. (11), where each pixel q E M is assigned to the nearest handle point h;
S, = E M i arg ming l|g h2ll2)
(1I)
This Voronoi-like partition ensures that each sub-region S controlled only by corresponding handle point h, effectively decoupling the influence zones and eliminating destructive overlap.
Once the partitioning is established, we compute the displacement field f: independently for each sub-region S using the hybrid geometry- and plane-aware formulation from the previous section. The final displacement field f is constructed by assigning the corresponding sub-field each pixel:
f(g) f(()), for q E S..
(12)
This region-wiss aggregation ensures that each pixel influenced by only one handle, avoiding directional conflicts and enabling precise and localized edits even when multiple drags applied. Despite being hard partition, our ablations (see Fig. 6) show it performs better than soft partitioning (Directly Add, Pixel Distance, and Drag Magnitude).

---

Published as a conference paper at ICLR 2026
Quantitative results on DRAGBENCH. Lower MD and DAI indicate higher editing pre- cision, and higher IF reflects greater similarity between original and edited images. Time is the average editing time per point, and Mem is the peak GPU memory (GB).
Table I:
Approach
MD
DAI
DA
2A
IF
Preparation
Time (s)
Mem
DragDiffusion (Shi et al., 2024)
34.57
0.181
0.170
0.160
0.871
min (LoRA)
22.46
18.63
FreeDrag (Ling et al., 2024)
30.80
0.183
0.166
0.151
0.845
~ min (LoRA)
42,90
18.90
CLIPDrag (liang al., 2024)
34.62
0.195
0.174
0.158
0.891
~ min (LoRA)
38.21
22.72
AdaptiveDrag (Chen al., 2024)
32.38
0.180
0.154
0.146
0.830
min (LoRA)
46.30
7.71
DragNoisc (Liu et al., 2024)
33.84
0.179
0.169
0.158
0.861
min (LoRA)
21.12
18.36
FastDrag (Zhao et al., 2024)
32.10
0.131
0.123
0.115
0.850
X
3.23
5.85
GeoDrag (Ours)
29.24
0.128
0.120
0.111
0.847
3.95
5.44
User Drag
Directly Add
Pixel
Drag Magnitude
Figure 6: Ablation study on multi-poin drag strategies. See quantitative results Fig. 10(a).
EXPERIMENTS
4.1 QUALITATIVE EVALUATION
We conduct qualitative comparisons against existing state-of-the-art drag-based image editing meth- ods, including Diffuusuuiion (Shi et al., 2024), DragNoise (Liu et al., 2024), FreeDrag (Ling et al., 2024), and FastDrag (Zhao al., 2024). As shown in Fig. GeoDrag achieves superior perfor- mance and high image quality. For instance, in the first row of Fig. 5, GeoDrag accurately drags the handle points toward the target points, preserving both structural integrity and semantic coher- ence, while other methods, such as FastDrag and FreeDrag, fail to maintain precise alignment. In multi-point editing (e.g.. the second, fourth, and fifth rows Fig. 5), GeoDrag successfully reshapes the wings, adjusts postures in alignment with the user-specified editing intention. In contrast, due to conflicts among multiple drag points, other methods struggle to generate coherent deformations. Observation from the last three rows of Fig. 5, GeoDrag produces 3D structure-coherent manipula- tion. These advantages are derived from the integration of geometric information, which provides a displacement field toward 3D structure alignment, enabling structurally consistent editing.
4.2 QUANTITATIVE EVALUATION
Here we conduct quantitative evaluations to validate GeoDrag where its modulation factors a, 33 and Y are set 1.0. Apart from (Shi al., 2024: Liu al., 2024; Ling al., 2024; Zhao al., 2024), CLIPDrag (Jiang et al., 2024) and AdaptiveDrag (Chen et al., 2024) are included for comparison. ENAC dataset (Shi et al., 2024) is used as the benchmark, while Mean Distance (MD) (Pan et al., 2023) and Image Fidelity (IF) (Kawar et al., 2023) are metrics for evaluating the editing pre- cision and similarity between edited and original images. Dragging Accuracy Index (DAI) (Zhang et al., 2025) measures consistency in the dragged region, with DAI, evaluating radius-r patch. We also report average editing time per point and peak GPU memory.
As reported in Table 1, GeoDrag achieves the best editing precision with the lowest MD and DAI, while maintaining competitive perceptual quality reflected by IF metric. Despite not requiring any preparation overhead, such LoRA tuning. GeoDrag outperforms all baselines. GeoDrag edits each point in 3.95 seconds on average faatter little GPU memory, making it suitable for responsive applications. More results scalability w.r.. the number of drag points are in Appendix We also conduct a user study using 10 randomly selected images, each edited by FreeDrag (Ling al., 2024), FastDrag (Zhao al., 2024), and
faster than most diffusion based methods and consumes
8

---

Published as a conference paper at ICLR 2026
Average Rank
User Drag
GroDrag
w/o Depth
w/o Plane
2.75
2.00
1.25
0.50
GeoDrag FreeDrag FastDrag
Figure 7: User study ranking on editing quality.
Figure 8: Ablation on displacement field.
User Drag
y
Moan Dananet
Mean Distance
31.2
Image BiAARII
Image
Mean Distanel-)
kwap horts
Distasol
lea
11
-
2.0
e.2
Naan Dbeanrs
1A
15
120
28
Hyperparameter sensitivity study. Left: Visual comparisons under different perceptual region ?. Right: Quantitative comparisons with varying modulation factors a and B. See more results in Appendix C.2 and Appendix C.3.
Figure 9:
GeoDrag. 60 Participants asked rank the edited results (1 for best, 3 for worst). As shown in Fig. 7, GeoDrag is superior to other methods.
4.3 ABLATION STUDY
Displacement Field. We conduct ablation studies to investigate the contribution of each component in our hybrid displacement field. Specifically, w/o Depth removes the depth-aware field, while w/o Plane removes the plane-aware field. As shown in Fig. 8, removing the depth-aware aware field leads inaccurate editing (e.g., failure rotate the car). Removing plane-aware field leads to insufficient editing. The results highlight the complementary roles of 3D geometry and 2D plane prior in achieving structure-preserving and semantically coherent editing. The quantitative resuls are shown in Fig. 10(a). GeoDrag with geometry- and plane-aware displacement field achieves the best performance. Removing the geometry prior (w/o Depth) or the plane modulation (w/o Plane) leads to degradation across all metries.
Conftict-Free Partitioning. We evaluate the proposed conflict-free partitioning strategy with direct summation (Directly Add), pixel-distance weighting (Pixel Distance), and drag- maggnitue weight- ing (Drag Magnitude). The mathematical formulations alternative strategies are provided in Ap- pendix C. As shown in Fig. 6, directly adding displacement fields leads conflict and cancellation when directions oppose; Pixel Distance and Drag Magnitude cannot effectively separate influence regions, producing duplicated and unsatisfactory results; Our method avoids interference among multiple drag points, yielding accurate results. The quantitative results are shown in Fig. 10(b). GeoDrag outperforms alternative strategies all metrics, demonstrating the effectiveness of the conflict-free partitioning.
Hyperparameters. We visually analyze the influence of y. As shown in Fig. 9left), setting be- tween 0.5 and 1.5 strikes a good balance between geometry-awant consistency and local editability. We analyze the impact of and | in Fig. (right). The lower a indicates a smoother scale (see Eq. (7)), weakening the influence of depth contrast. This results in larger overall deformation and improved alignment. Larger a increases depth sensitivity, causing sharper displacement changes. This allows for finer-grained control in regions with significant depth changes. For plane-aware field, a higher 3 enforces more localized and sharper deformations (see Eq. (8)), thereby better aligning the edits with user intention and resulting in lower MD. The study of the noise-scaling term or is shown Fig. 11. Setting 7 = leads to fully deterministic update, often causing over- smoothed interpolations and blurred backgrounds. Increasing r adds randomness the interpolated

---

Published as a conference paper at ICLR 2026
GeoDrang Depth wio Plane
DAI
DAI
Magalledd
1-IF
4
DAI10
1-IF
DAI10
0.08
0.0%8
0:11
0.98
0.98
0.11
0:08
0.08
0.3
2660
0.088
32.0
38.0
0.47
0:47
MD
DAI00
MD
DAl20
(a) Displacement field variants (cf. Fig. 8).
(b) Multi- drag strategies (cf. Fig. 6).
Figure 10: Quantitative results of ablation study.
Original Image
User Drag
0.2
0.4
0.6
0.8
n=1
Figure 11:
Ablation study on the noise-scaling term o, controlled by n,. We vary 7 from 0 (deter- ministic) to 1 (stochastic).
region, helping the diffusion model better recover local details. More ablation studies and detailed analyses are provided in Appendix C.
CONCLUSION
In this paper, we propose GeoDrag, a novel interactive editing framework that integrates 3D geomet- ric priors with 2D spatial cues. GeoDrag achieves geometry-consistent and semantically coherent image manipulation by constructing hybrid displacement field. A geometry- influence func- tion leverages depth model 3D-consistent displacements, while complementary plane-awar aware function improves the controllability of editing. To resolve multi-point conflicts, a region-aware de- composition strategy ensures conflict-free aggregation. This work offers new perspective on how 3D geometric priors can be beneficial for precision, coherence, and controllability of 2D interactive image editing.
Limitations. Although GeoDrag supports one-step editing, computing multiple displacement fields introduces additional computational overhead compared to lightweight 2D-only baselines such as FastDrag (Zhao et al., 2024). But GeoDrag often yields much more geometry-consisten consistent and high- quality image edits, e.g., 9% and 7% improvements in terms of MD and GPU memory than FastDrag shown in Table 1.
10

---

Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This research is supported by the National Natural Science Foundation of China (205.52441503 and 62302093), the Natural Science Foundation of Jiangsu Province Nos.BK20230833)) the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier grant (Proposal ID: 23- SIS-SMU-070), This work is supported in part by the grant of the CIPS-SMP-ZZN -Zipu Large Model Fund. We thank the Big Data Computing Center of Southeast University for providing the facility support on the numerical calculations. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Ministry of Education, Singapore.
ETHICS STATEMENT
Image editing model may contain biases or occasionally produce sensitive or offensive outputs. Our models are presented strictly for academic and scientific research purposes. Any generated content does not reflect the personal views of the authors. Our work remains guided by commitment to advancing Al technologies in ways that uphold ethical standards and resonate with societal values.
REPRODUCIBILITY STATEMENT
We detail the main framework of our work Sec. 3, and provided the implementation details in Appendix B.
REFERENCES
Tim Brooks, Aleksander Holynski, and Alexei A. Efros. Instructpix2pix: Learning to follow image editing instructions. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR. pp. 18392-18402. 2023.
DuoSheng Chen, Binghui Chen, Yifeng Geng, and Liefeng Bo. Adaptivedrag: Semantic-driven dragging on diffusion-based image editing. arXiv preprint arXiv:2410. /2696, 2024.
Hansam Cho, Jonghyun Lee, Seoung Bum Kim, Tae-Hyun Oh, and Yonghyun Jeong. Noise map guidance: Inversion with spatial context for real image editing. In The Twelfth International Conference on Learning Representations, ICLR, 2024.
Yutao Cui, Xiaotong Zhao, Guozhen Zhang, Shengming Cao, Kai Ma, and Limin Wang. Stabledrag: Stable dragging for point-based image editing. In The 18th European Conference on Computer Vision, ECCV, pp. 340-356, 2024.
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit Haim Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion. In The Eleventh International Conference Learning Representations, ICLR, 2023.
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfrr Aberman, Yael Pritch, and Daniel Cohen-Or. Soompt-to-prompn image editing with cross attention control, arXiv preprint rxXiv:220.00126 2022.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Pro- cessing Systems 2020, NeurlPS, 2020.
Xingzhong Hou, Boxiao Liu, Yi Zhang, Jihao Liu, Yu Liu, and Haihang You. Easydrag: Efficient baset manipulation on diffuusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 8404-8413, 2024.
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. In The Tenth Interna- fional Conference Learning Representations, ICLR, 2022.
11

---

Published as a conference paper at ICLR 2026
Inbar Sutberman-Spiegelglle Vladimir Kulikov, and Tomer Michaeli. An edit friendly DDPM noise space: Inversion and manipulations. In IEEE/CVE Conference on Computer Vision and Pattern Recognition, CVPR, pp.
2024.
Ziqi Jiang, Zhen Wang, and Long Chen. Combining text-based and drag-basee editing for precise and flexible image editing. arXiv preprini urXiv:2410.03097, 2024.
Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. Imagie: Text-based real image editing with diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 6007-6017, 2023.
Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye. Diffusionclip: Text-guided diffusion mod- els for robust image manipulation. IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2416-2425,200 2022.
Gwanhyeong Koo, Sunjae Yoon, Younghwan Lee, Ji Woo Hong, and Chang D. Yoo. Flowdrag: 3d- aware drag-based image editing with mesh-guided deformation vector flow fields. arXiv preprint MrXiv:2507.08285. 2025.
Pengyang Ling, Lin Chen, Pan Zhang, Huaian Chen, Yi Jin, and Jinjin Zheng. Freedrag: Feature dragging for reliable point-based image editing. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 6860-6870, 2024.
Haofeng Liu, Chenshu Xu, Yifei Yang, Lihua Zeng, and Shengfeng He. Drag your noise: Interactive point-based editing via diffusion semantic propagation. In IEEE/CVF Conference on Computer Vision and Pultern Recognition, CVPR, pp. 6743-6752, 2024.
Jingyi Lu, Xinghui Li, and Kai Han. Regiondrag: Fast region-based image editing with diffusion models. In The 18th European Conference on Computer Vision, ECCV, pp. 231-246, 2024.
Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. Latent consistency models: Synthe- sizing high-resolution images with few-step inference. arXiv preprint arXiv:2310.04377, 2023.
Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen- Or. Null-text inversion for editing real images using guided diffusion models. arXiv preprint arXiv:2211.09)) 2022.
Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, and Jian Zhang. Diffeditor: Boosting ac- curacy and flexibility on diffusion-baset image editing. IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 8488-8497, 2024a.
Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, and Jian Zhang. Dragondiffusion: Enabling drag-style manipulation on diffusion models. In The Twelfth International Conference on Learn ing Representations, ICLR, 2024b.
Trong-Tung Nguyen, Quang Nguyen, Khoi Nguyen, Anh Tran, and Cuong Pham. Swiftedit: Light- ning fast text-guided image editing via one-step diffusion. arXiv preprint arXiv:2412.04301, 2024.
Shen Nie. Hanzhong Allan Guo, Cheng Lu, Yuhao Zhou, Chenyu Zheng, and Chongxuan Li. The blessing of randomness: SDE beats ODE in general diffusion- image editing. In The Twelfth International Conference on Learning Representations, ICLR, 2024.
Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Abhimitra Meka, and Christian Theobalt. Drag your GAN: interactive point-based manipulation on the generative image mani- fold. In ACM SIGGRAPH 2023 Conference Proceedings, SIGGRAPH, pp. 78:1-78:11, 2023.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In IEEF/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 10674-10685, 2022.
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aber- man. Dreambooth: Fine tuning eext-to-image diffusion models for subject-driven generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 22500-22510,
2023.
12

---

Published as a conference paper at ICLR 2026
Yujun Shi, Chuhui Xue, Jun Hao Liew, Jiachun Pan, Hanshu Yan, Wenqing Zhang, Vincent Y. F. Tan, and Song Bai. Dragdiffusion: Harnessing diffusion models for interactive point-based image editing. In /EEE/CVF Conference Computer Vision and Pattern Recognition, CVPR, pp. 8839- 2029.
Joonghyuk Shin, Dachyeon Choi, and Jaesik Park. Instantdrag: Improving interactivity in drag- based image editing. In SIGGRAPH Asia 2024 Conference Papers, SA, pp. 39:1-39:10, 2024.
Chen Henry Wu and Fernando De la Torre. A latent space of stochastic diffusion models for zero- shot image editing and guidance. In IEEE/CVF International Conference on Computer Vision, ICCV, pp. 7344-7353, 2023.
Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything V2. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurlPS, 2024a.
Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, and Bin Cui. Mastering text- to-image diffusion: Recaptioning, planning, and generating with multimodal llms. In Forty-first International Conference on Machine Learning, ICML, 2024b.
Zewei Zhang, Huan Liu, Jun Chen, and Xiangyu Xu. Gooddrag: Towards good practices for drag editing with diffusion models. In The Thirteenth International Conference on Learning Repre- sentations, ICLR, 2025.
Xuanjia Zhao, Jian Guan, Congyi Fan, Dongli Xu, Youtian Lin, Haiwei Pan, and Pengming Feng- Fastdrag: Manipulate anything in one step. In Advances in Neural Information Processing Sys- tems 38: Annual Conference on Neural Information Processing Systems 2024, NewrIPS, 2024.
Jun Zhou, Jiahao Li, Zunnan Xu, Hanhui Li, Yiji Cheng, Fa-Ting Hong, Qin Lin, Qinglin Lu, and Xiaodan Liang. Fireedit: Fine-grained instruction- based image editing via region-aware vision language model. arXiv preprint arXiv:2503. 19839, 2025.
13

---

Published as a conference paper at ICLR 2026
Appendix
Broader Impact
14
Implementation Details
15
Supplementary Ablation Study
15
C.1 Mathematical Formulations of Alternative Strategies
15
C.2 Visual results of modulation factors
15
C,3 Quantitative results of different y
15
C.4 Ablation study on inversion steps
15
More Visualization Comparison
16
E Effect of LoRA Finetuning GeoDrag
17
F Scalability with the number of drag points
17
G Effect of Masks
17
H Visualization of Displacement Fields
18
I Details of User Study
18
J Robustness to Depth Map
18
K Failure Cases
20
L Per-Stage Runtime Analysis and Improvements
21
M Effect of the Scaling Factor on Handle Handle-Target Alignment
21
N Additional Visual Comparisons with SDEDrag and GoodDrag
22
A BROADER IMPACT
GeoDrag is developed to improve the controllability and fidelity of interactive point-based image editing, enabling precise user-driven visual manipulation. The framework applies wide range of scenarios, including digital content creation, artistic editing. and AR/VR-basee scene editing. By improving the structural consistency and semantie fidelity, GeoDrag allows especialpyy non- experts-to produce high-quality and visually coherent results with minimal effort, potentially de- mocratizing advanced visual editing workflows. However, the increased realism and control enabled by GeoDrag may raise negative societal impacts. In particular, malicious actors could leverage the system to generate visually convincing but deceptive content, contributing to disinformation. digital impersonation, reputational harm. In addition, advancements in image editing tools increase the risk of fake imagery, potentially undermining public trust. Unethical use may also raise concerns related individual consent and personal privacy. To address these risks, advocate for respon-
14

---

Published as a conference paper at ICLR 2026
sible deployment, transparent provenance of generated content, and further research into detection and authentication techniques.
B IMPLEMENTATION DETAILS
GeoDrag is implemented based on a pretrained latent diffusion model (Stable Diffusion 1.5 (Rom- bach et al., 2022)), and incorporates LCM-accelerated U-Net (Luo et al., 2023) to enable efficient low-step inference. We set the number of sampling steps to 10 and use an inversion strength of 0.7. The depth prediction model used in geometry-awane field modeling (Sec. 3.1) is Depth Anything V2 (Yang et al., 2024a). Following prior drag-based editing methods (Shi et al., 2024; Ling et al., 2024; Liu al., 2024), we disable classifier-free guidance. For fair comparison, all baseline meth- ods are evaluated using their default hyperparameter settings as specified in the original papers or official open-source implementations. Experiments are conducted on an RTX 4090 GPU with 24G memory.
C SUPPLEMENTARY ABLATION STUDY
C.I MATHEMATICAL FORMULATIONS OF ALTERNATIVE STRATEGIES
Directly Add:
f fo
(13)
1/P:
Pixel Distance:
f
f..
(14)
I/P:
[dj
Drag Magnitude:
f
(15)
[d.]
Zi=1
where P. is the pixel-wise distance map to handle point h;, and |d;| denotes the drag magnitude of h4-
C.2 VISUAL RESULTS OF MODULATION FACTORS
Moreover, present the visual results of different combinations of and |3 in Fig. 22. It is evi- dent that GeoDrag well aligns with user-specified drag points, demonstrating its robustness across parameter variations. We observe that increasing enhances the influence of geometric guidance during editing, As illustrated in the fourth row of Fig. 22, when a 2. the result exhibits a more plausible 3D transformation: the mushroom cap appears lifted along a realistic vertical trajectory, consistent with bottom-up viewpoint. This demonstrates the role of geometric priors in maintain- ing a consistent 3D perspective during editing.
C.3 QUANTITATIVE RESULTS OF DIFFERENT
The quantitative results of different perceptual region y presented in Fig. 12. As y increases, MD consistently decreases, while exhibits a non-monotonic trend-first increasing, then decreasing. Notably, 7 offers favorable balance between editing accuracy and image fidelity, and is thus selected as the default setting in our experiments.
C.4 ABLATION STUDY ON INVERSION STEPS
We experiment with f E [4, 7, 10, 13, 20, 35, 35,50) to examine the number of inversion steps in diffu- sion inversion. The qualitative and quantitative results are given in Fig. 14 and Fig. 13, respectively. We observe that GeoDrag achieves more precise and faithful edits when the number of inversion steps 10. In contrast, when t > 10, the editing quality degrades. For fair comparison with FastDrag (Zhao et al., 2024), and to balance editing performance with fidelity to the original image, adopt 10 the default setting for inversion steps throughout all experiments in the paper.
15

---

Published as a conference paper at ICLR 2026
Mean Distance (
Mean Distance
0.86
35.0
0.88
45.0
Image Fidelity
Image Fidelity
Mean Distance
32.5
0.85
Image Fidelity
0.85
40.0
30.0
0.82
35.0
0.84
27.5-
0.80
Mean Distance Image Fidelity
30.0
0.83
25.0
0.78
0.0
0.5
1.0
1.5
4 7103
35
Y
Number of Inversion Steps t
Figure 12: Ablation study y terms of quan- titative metrics.
Figure 13: Quantitative evaluation of the effect of varying inversion steps in diffusion inversion. 35
User Drag
stepps
stcpss
10steps
13steps
20
50
User Drag
steps
stepps
10steps
13steps
20steps
50eepps
Figure 14: Ablation study on the number of inversion steps
D MORE VISUALIZATION COMPARISON
We present additional qualitative comparisons between method and other state-of-the-art in- teractive point-based editing methods. As shown in Fig. 23, these results further demonstrate the advantages of GeoDrag across various scenarios, including rotation (e.g., dog, car), scale manipula- tion (e.g,, the avocado and burger), stretching (e.g., the bench), and geometry-consistent movement (e.g., the mailbox and car). Compared other methods, GeoDrag better preserves object struc- ture while producing edits that better conform to the user input. In challenging cases involving perspective shifts ge. the last row), GeoDrag generates geometry-consistet results that maintain alignment with the user-specified drag. These results demonstrate GeoDrag's 's ability to preserve visual coherence under complex editing operations.
Flow Legend
Depth/Distance Legend
Color wheel used for displacement field visu- alization.
(b) Color used to indicate depth or distance maps.
Figure 15: Visualization legends.
16

---

Published as a conference paper at ICLR 2026
Table 2: Quantitative comparison of GeoDrag with and without LoRA finetuning.
Method
MD 4
DAI
1A 4
2A +
IF f
w/o LoRA
29.24
0.128
0.120
0.111 0.146
0.848
w/ LoRA
30.91
0.186
0.163
0.851
E EFFECT OF LORA FINETUNING ON GEODRAG
To further investigate the generalization ability of GeoDrag, evaluate GeoDrag under two con- figurations: with and without LoRA (Hu et al., 2022) finetuning. Both versions share the same backbone and inference hyperparameters: the only difference is whether LoRA finetuning is ap- plied. As shown in visual examples (see Fig. 24), GeoDrag consistently produces high-quality, geometry-consister edits even without any finetuning. Nevertheless, LoRA finetuning can enhance local detail and fidelity in some cases (e.g., the fourth row of Fig. 24).
The quantitative results are reported in Table The model without LoRA achieves lower MD and DAI, indicating better alignment with editing guidance. Meanwhile, LoRA finetuning improves visual similarity between original and edited images (higher IF).
F SCALABILITY WITH THE NUMBER OF DRAG POINTS
To evaluate scalability the number of drag points increases, we conduct dedicated experiment: 10 representative images selected and applied 1-8 drag points, yielding total of 80 edited samples. We measured the average per-image editing time for each point count. As reported in Table 3, the number of drag points has minimal impact editing time, supporting the practicality of GeoDrag for interactive workflows.
Table 3: Average editing time (in seconds) for different numbers of drag points.
Number of points
3
4
7
Time (seconds)
12.71s
11.63s
11.74s
11.23s
12.27s
12.86s
12.03s
12.93s
G EFFECT OF MASKS
User Drag
Edited Result
User Drag
Edited Result
(a) Full-object mask (global edit).
(b) Edge-only mask (localized boundary edit).
Figure 16: Effect of mask selection editing.
Mask selection crucial for controlling the scope and locality of edits. Fig. illustrates the impact of different masks when edit same object. Applying full-object mask to the avocado enlarges
17

---

Published as a conference paper at ICLR 2026
Table 4: Evaluation GeoDrag under noisy depth maps
Noisy Level
MD +
DAI |
DAI 4
DAI20
IF
Baseline
29.24
0.128
0.120
0.111
0.847
0.01
+0.66
+0.00
+0.00
+0.00
+0.00
a 0.05
+1.23
+0.002
+0.002
+0.001
+0.00
0
+2.85
+0.004
+0.003
+0.003
-0.002
o 0.5
+9.81
+0.007
+0.004
+0.004
-0.006
the entire object (Fig. 16(a)) and the shape of the seed changed. In contrast, masking only the avocado's 's edges confines the effect to the boundary (Fig. 16(b)), preserving the shape of seed.
H VISUALIZATION OF DISPLACEMENT FIELDS
To provide a better understanding of how user-specified drags are propagated, visualize the 2D displacement fields (see Fig. and Fig. 3). These displacement fields are color-coded using a consis- tent scheme indicate direction and magnitude, shown in Fig. 15(a). Hue represents the direction of displacement, with each color corresponding to a specific motion orientation (c.g., rightward in red, upward in cyan). Brighter and more saturated regions correspond to larger displacement mag- nitudes.
In addition, Fig. 15(b) shows the legend used for visualizing depth distance maps. Warmer colors indicate closer regions and cooler colors denote farther ones. This legend is used visualizations such depth and distance maps in Fig.
DETAILS OF USER STUDY
Here, we provide additional details about the user study, including its design and aggregated ranking- based evaluation results. Fig. 25 shows the selected input images, the corresponding editing results from different methods, and the user-assigned rankings for each result. For each test case, users were asked to rank the edited results from three different methods. The ranking is based on how well each result aligns with the intended dragging operation while preserving the original visual identity. A lower rank indicates better quality. Specifically, rank I denotes the best edit-i.e.. the one that best aligns with the drag intention and maintains high image fidelity-while rank 3 corre- sponds to the least satisfactory result. Note that the anonymous labels (a), (b), and (c) correspond to FastDrag (Zhao et al., 2024), FreeDrag (Ling et al., 2024), and GeoDrag, respectively.
J ROBUSTNESS TO DEPTH MAP
Fig. 17 presents a qualitative evaluation of our method under challenging depth-prediction condi- tions, including transport-specular regions (first row), textureless regions (second row), and strong perspective view (third row). For each scenario, we compare two variants: geometry-only and the combined formulation employed in GeoDrag. As shown in the figure, the underlying depth maps in these scenes exhibit distinct failure modes: (1) due to the transparent glass surface and strong specu- lar reflections, the depth estimator can only separate foreground and background coarsely, producing flat and overly simplified depth map. When relying solely on depth cues, this leads to blurred de- formation along object boundaries; (2) the edited object lies almost flush against a uniformly colored guidance, therefore, becomes
wall, resulting in extremely weak depth gradient. Geometry-oni unreliable and introduces distortions; (3) the strong perspective foreshortening causes large depth discontinuities and makes it difficult for the estimator to recover the true geometry of the target object (the train).
In all these scenarios, the failure of the depth map to provide reliable geometric renders geometry-only guidance insufficient for stable deformation. However, the incorporation of Spatial Plane Modulation effectively compensates for these deficiencies by supplying depth-independden,
18

---

Published as a conference paper at ICLR 2026
User Drag
Depth Map
w/o Pane
W Pane
Figure 17: Evaluation in specular, textureless, and strong-perspective scenes using geometry-onke and combined displacement.
Original Image
User Drag
Edited Result
Original Image
User Drag
Edited Result
Figure 18: Failure cases of drag editing when generating previously unseen content. Left: overlap- ping objects cause ambiguous interpolation and distorted regions. Right: large unseen areas lack valid latent correspondence, leading to unrealistic synthesis.
spatially smooth prior that preserves coherent structure even when depth information severely degraded. This design enables GeoDrag to maintain stable and plausible deformations.
Moreover, we conducted an additional perturbation study to evaluate the robustness of GeoDrag under noisy monocular depth predictions. We injected controlled Gaussian noise into the predicted depth maps, scaling the variance according to the depth range of each image:
(16) where (xx, y) is the original depth. AD D Dmax Dmin denotes the depth range, and or is the noise factor. This formulation ensures that the injected perturbation is proportional to the scene's
D(,,v) D(x, v) e(x,y)
e(z,y) (O, (a (D))).
19

---

Published as a conference paper at ICLR 2026
Original
User Drag
Scale=0.5
Scale=1.0
Scale=1.25
Scale=1.277
Scale=1.35
Scale=1.4
Scale=1.5
Scale=2.0
Figure 19: Effect of the scaling factor handle target alignment. Increasing the scale improves point alignment, while overly large scales lead to structural distortion.
User Drug
SPEDred
GoodDris
Owrs
User Drag
SPEDrag
GoodDrag
Ours
User Draz
GeodDrag
Ours
User Drag
GoodDrag
Ours
User Drag
GoodDreg
Ours
Figure 20: Qualitative comparison with SDEDrag (Nie et al., 2024) and GoodDrag (Zhang al., 2025).
depth variation. As reported in Table 4, GeoDrag exhibits strong robustness to depth prediction noise. Under mild and moderate perturbations (e.g., a S 0.1), the performance remains largely stable across all evaluation metrics, indicating that the model is not overly sensitive to small depth fluctuations. Even when the noise magnitude increases to a 0.3. the performance degradation only becomes noticeable at this extreme level, suggesting that the method is capable of maintaining reliable tracking quality under realistic sensor and prediction uncertainties. Overall, these results demonstrate that GeoDrag preserves its effectiveness in challenging noisy-depth scenarios and does not rely heavily on precise depth estimates to function correctly.
K FAILURE CASES
We present two representative failure cases in Fig. 18 to clarify the limitations of GeoDrag. Both examples require generating previously unseen content rather than simply deforming the visible gions. The example on the left additionally involves a complex scene with multiple overlapping ob- jects. Although the editing direction is partially correct, the newly exposed regions exhibit distorted and blurry structures. This occurs because our interpolation strategy reconstructs unseen content by sampling nearby latent features; when multiple objects heavily overlap, the mixed semantic context
20

---

Published as a conference paper at ICLR 2026
Table 5: Averaged per-stage execution time of GeoDrag over the full benchmark (in seconds).
Stage
DDIM inversion
DDIM sampling
Interpolation
Depth prediction
Time (8)
1.2721
1.2416
0.2247
0.0995
Stage
Spatial plane modulation
Latent relocation
Partition
Geometry-aware field modeling
Time (8)
0.0109
0.2714
0.0006
0.0003
Table 6: Comparison of dynamic (original) and static (optimized) interpolation in GeoDrag.
Model
MD
DAI
DAI
DAIx
IF
Interpolation Time (s)
Latent Relocation Time (s)
GeoDrag (original)
29.
0.128
0.120
0,111
0.847
0.2247
0.2714
GeoDrag (optimized)
0.121
0.111
0.0014
0.0009
introduces feature ambiguity and leads to unstable reconstruction. The example on the right exposes large area that is completely invisible in the original image. In this case, the missing content has no reliable correspondence within the existing latent featureslacks reliable correspondence within the existing latent features, interpolation-base based completion cannot provide meaningful structural cues, resulting inpainting-base based latent completion module may alleviate this issue and is promising direction for future work.
L PER-STAGE RUNTIME ANALYSIS AND IMPROVEMENTS
To better characterize the efficiency of the proposed GeoDrag pipeline, provide full-stack runtime breakdown across all modules, revealing where the computational budget is primarily spent and how to further optimized. The pipeline can be decomposed into eight major stages: DDIM inversion, DDIM sampling, partition, geometry-aware field modeling, spatial plane modulation. interpolation, latent relocation, and depth prediction. The averaged per-stage execution times (in seconds) over the Other Object subset of DRAGBENCH are reported in Table 5. The results show that the diffusion-bartt -based components (inversion and sampling) dominate the total latency, whereas the remaining geometric modules contribute negligibly to the runtime. Within the non-diffusion stages, interpolation and latent relocation are most expensive component.
Therefore, we focuse our efficiency improvements specifically on these two modules: (1) we ob- served that latent relocation can be fully vectorized, allowing us to remove unnecessary per-point loops; and (2) we replace the dynamic BNNI update with a static four-neighbor interpolation rule, enabling parallel computation and substantially faster execution. The quantitative comparison reported in Table 6. Surprisingly, this simplified interpolation not only reduces runtime but also improves performance. We believe this because removing the iterative BNNI updates prevents error accumulation, while the static strategy still retains the essential semantic structure in the latent space.
M EFFECT OF THE SCALING FACTOR ON HANDLE-TARGES ALIGNMENT
During the drag operation,
displacement field computed the diffusion latent space rather than directly on image pixels space. When converting drag points from the pixel domain (e.g.. 512 x 512 pixels) to the latent domain (e.g., 64 x 64 features), the coordinates must be downscaled accordingly. This conversion inevitably reduces the effective displacement magnitude in the latent space, leading to small residual gap between the handle and target points after editing.
To compensate for this attenuation, apply scaling factor rescale the displacement field after converting coordinates to the latent space. This restores the motion magnitude lost during down- sampling and improves handle- target alignment.
As shown in Fig. 19, the scaling factor significantly influences alignment and deformation quality. A moderate value enhances correspondence between handle and target points, while overly large
21

---

Published as a conference paper at ICLR 2026
one leads to overcorrection and geometric distortion. In practice, scale of 1.2-1.3 offers good balance between point-level accuracy and global consistency.
N ADDITIONAL VISUAL COMPARISONS WITH SDEDRAG AND GOODDRAG
We provide additional qualitative comparisons with SDEDrag (Nie et al., 2024) and Good- Drag (Zhang et al., 2025) in Fig. 20. The results show that our method achieves comparable or even better editing results. Compared with GoodDrag (Zhang et al., 2025), GeoDrag does not rely on LoRA fine-tuning and multi-step optimization, making it significantly more efficient.
DECLARATION OF LLM USAGE
We used large language model (GPT-5) solely for grammar and language refinement. All research ideas, analyses, and conclusions are our own.
22

---

Published as a conference paper at ICLR 2026
Original Image
User Drag
a 0.5
Step 0.5
2
8
0.5
Step = 0.5
Original Image
User Drag
a 0.5
Step = 0.5
2
B
0.5
Step 0.5
Figure 21: Visual Results of Hyperparameter Analysis.
23

---

Published as a conference paper at ICLR 2026
Original Image
User Drag
a 0.5
Step 0.5
2
8
0.5
Step = 0.5
Original Image
User Drag
a 0.5
Step = 0.5
2
B
0.5
Step 0.5
Figure 22: Visual Results of Hyperparameter Analysis.
24

---

Published as a conference paper at ICLR 2026
Original Image
User Drag
DragDifiusion
DragNoise
FreeDrag
FastDrag
GeoDrag(Ours)
Figure 23: More qualitative comparisons with state-of -fee---rt-e-rr interactive point- point-based methods.
25

---

Published as a conference paper at ICLR 2026
Original Image
Drag
LoRA
Original Image
User Drag
Figure 24: Visual comparison of GeoDrag with and without LoRA (Hu et al., 2022) finetuning. w/
LoRA and w/o LoRA denote our GeoDrag with and without finetuning, respectively.
26

---

Published as a conference paper at ICLR 2026
Original Image
User Ispul
(A)
(h)
Origiaal Image
User Input
(4)
(6)
Whtank
[el
31
Mfonk I votes
#Rank 2 voter
6Ronk 3 voter
Oviginal Imsgge
User Tmper
(A)
(b]
(b)
31
18
Original Imags
User Imput
votes
votes
24
votes
(+)
13
25
45
[d]
Original
User
Original Emagee
Inpat
#Ronk votes
ARonk votes
#Ronk
(+)
(h)
23
19
(e)
[c)
Original Emage
Inpur
Original
User
Altani votes
ARonk 2 votea
aRlank 3
(b)
40
13
AWtnk voses
Allosk 2 votes
BRonk 3 votes
10
21
29
13
Original
(b)
Original Image
Uver Input
(A)
(b)
BRonk
votes
(6)
14
13
34
10
(b)
[4]
Figure 25: Examples and detailed rankings for the user study. For each original image, the user input, and editing results from three anonymous methods labeled as (a), (b), and (c). The number of Rank-1, Rank-2, and Rank-3 votes collected from all participants is reported. A lower rank indicates better alignment with the intended manipulation and higher visual fidelity.
show the
27


Reviews:
[{"review_id": "q0UGNDwaOE", "reviewer": "Reviewer_VFmB", "summary": "This paper proposes GeoDrag, a drag-based image editor that injects 3D geometry into 2D dragging. The authors derive a geometry-aware influence field. Specifically, per-pixel motion scales with the relative depth to a handle point, so nearer pixels move more than farther ones, reducing perspective artifacts during editing. For multi-handle cases, they introduce a conflict-free hard partition so each pixel is driven by exactly one handle, avoiding direction cancellation. On DragBench, GeoDrag achieves the best edit precision (lowest MD/DAI) with competitive speed/memory.", "questions": "* In practice, one common idea is that LoRA should improve the performance in drag editing. However, Table 2 suggests that LoRA actually hurts the performance for most metrics. Is there any further explanation or examples?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The derivation from 3D displacement to 2D motion motivates a depth-modulated field that is simple, interpretable, and implementation-light. The proposed approach effectively reduces perspective-induced tearing during large drags.\n* On DragBench, GeoDrag attains best MD and best DAI1/10/20 with a one-step pipeline and 3.95s per point / 5.44 GB peak memory, competitive with FastDrag’s 3.23s / 5.85 GB and faster than LoRA-prep methods (∼1-min prep). Compared with baselines, for which LoRA-prep is often necessary, the proposed method shows better generalizability.\n* The partitioning successfully creates predictable, non-interfering edits. Ablations show it outperforms soft blends like Directly-Add, Pixel-Distance, and Drag-Magnitude across MD/DAI/IF (Fig. 10b)", "weaknesses": "* The simplification that neglects motion along the optical axis to obtain is reasonable for small motions, but the paper does not quantify robustness to depth noise (from monocular estimation), likely failure modes for strong perspective or specular/textureless regions. A perturbation study of these factors would better support claims of geometric stability. Additional failure cases will also help to understand the limitations of the work.\n* The runtime table shows FastDrag is still faster per point (3.23 s) than GeoDrag (3.95 s) on the same GPU, and the manuscript does not provide a full stack breakdown (depth inference, partitioning, field fusion, masked DDIM) to explain where time goes or how to optimize it. A consolidated per-stage timing would strengthen the efficiency story.\n* Although the proposed method is claimed to work better for long-range drag operation, the qualitative results show that it still has problems where the handle points are not actually moved to the target points (Figure 5 Ours, there are always small distances). However, such a case is usually not seen in copy-paste methods like SDEDrag. \n* The geometry-aware influence field is derived under small-motion, locally smooth assumptions and works best when pixels “move with” nearby handles. However, edits that require self-occlusion swaps or reveal previously unseen regions may lead to problems.", "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": "aRuYKZwq5m", "reviewer": "Reviewer_KdHj", "summary": "This paper proposes GeoDrag, a geometry-guided drag-based image editing method. Similar to FastDrag, it enables drag-based manipulation without LoRA and operates in a single forward pass. The key idea is to incorporate 3D geometric cues into 2D drag editing through geometry-aware field modeling, where displacement strength is modulated based on the depth map. In addition, the authors introduce spatial plane modulation to complement this, since geometry-aware motion distributes influence uniformly in 3D space, making it less sensitive to local details. The fusion of geometry and plane-aware fields allows the method to maintain both global structure and fine-grained local control (conceptually related to warpage vector modeling in FastDrag). Experiments on DragBench demonstrate that GeoDrag achieves lower Mean Distance (MD) and Dragging Accuracy Index (DAI) than previous state-of-the-art methods, while performing editing faster and without additional training overhead.", "questions": "As mentioned above, in Eq. (10), λ is defined as P / (P + γ), which does not incorporate depth. Have the authors considered using a joint distance–depth weighting function to better reflect 3D spatial relationships?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- GeoDrag achieves the best performance on DragBench, showing the lowest MD and DAI metrics while maintaining competitive image fidelity and efficiency.\n- The framework is simple yet effective, enabling drag-based editing in a single forward pass without LoRA tuning, similar to FastDrag but extended with geometry awareness.\n- By introducing geometry-aware field modeling and spatial plane modulation, the method is able to integrate 3D geometric cues into 2D editing, improving structure preservation and local controllability.\n- The proposed conflict-free partitioning strategy effectively handles multi-point editing, preventing interference between overlapping drags and ensuring stable, coherent results.", "weaknesses": "- In Section 3.2, the paper mentions that the geometry-aware displacement field “distributes influence uniformly in 3D space, making it less responsive to subtle and local deformations in 2D image plane.” However, this concept is not intuitive; additional explanation or visual illustration would help readers understand it better.\n- The method heavily relies on accurate depth maps. In scenes where the depth estimation is noisy, such as generated images or complex high-frequency scenes with multiple overlapping objects, the geometry-aware field may be incorrectly computed, potentially degrading results compared to purely 2D approaches. Failure case analysis would strengthen the paper.\n- Simply projecting 3D depth information into 2D space may not fundamentally resolve geometric ambiguity, since projection inherently loses spatial consistency across varying viewpoints.\n- The novelty of the plane-aware field in Section 3.2 feels somewhat limited. Its formulation resembles the displacement weighting in FastDrag, which also applies a distance-based decay for local control. The paper would benefit from a clearer analysis or ablation demonstrating how the proposed plane-aware formulation improves upon or differs from *FastDrag* in practice.\n- In Eq. (10), the geometry-aware and plane-aware fields are linearly interpolated with λ = P / (P + γ). This λ depends only on 2D distance and ignores depth variation, thus disconnecting from the true 3D structure. It would be interesting to see whether defining λ as a combined function of both distance and depth difference could yield a more geometrically consistent fusion.\n- Missing comparison with GoodDrag (ICLR 2025), a recent baseline for drag-based editing.", "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": "B6FATNct2U", "reviewer": "Reviewer_2Gtw", "summary": "This paper presents GeoDrag, a novel method for point-based image editing that addresses the limitations of 2D-only approaches in geometry-intensive scenarios. By incorporating 3D geometric cues (from a predicted depth map) into a unified displacement field, GeoDrag achieves structurally consistent edits like rotations and perspective shifts. The framework combines this geometry-aware guidance with 2D spatial priors for local detail preservation and introduces a conflict-free partitioning strategy for robust multi-point dragging, all within an efficient single-pass process.", "questions": "1.  Concurrent work like `Inpaint4Drag` uses inpainting to handle occlusions, which can sometimes cause warping distortions. Have the authors considered combining GeoDrag's superior geometry-aware displacement with a dedicated inpainting model? This synergy could potentially yield more realistic results at object boundaries by better handling disoccluded regions.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  **Novelty and Rationale:** The core strength is the novel integration of 3D geometry into the 2D drag-editing task. This is a well-motivated and logical advancement for the field, directly addressing a key limitation of prior work and yielding significant improvements in structural preservation.\n\n2.  **Clear and Effective Framework:** The paper effectively decomposes the problem into distinct sub-challenges and proposes clear, corresponding solutions (geometry-aware field, spatial modulation, and conflict-free partitioning). This systematic approach enhances the method's robustness and clarity.\n\n3.  **Efficiency for Interactivity:** By avoiding iterative optimization, the one-step editing process makes GeoDrag a practical and competitive solution for real-world interactive applications.", "weaknesses": "1.  **Texture and Identity Preservation:** In some cases, the edited regions exhibit a loss of texture fidelity. For instance, the \"mountain\" example on the project page shows the dragged peak becoming blurred, suggesting a potential trade-off between geometric deformation and preserving the original surface identity. A discussion on this limitation would be welcome.\n\n2.  **Foreground-Background Separation:** The method can sometimes cause unnatural co-movement of the background with the dragged object. This is visible in the \"duck\" example, where the surrounding grass is pulled along with the head. This suggests that handling disocclusion and complex object boundaries remains an area for improvement.\n\n3.  **Limitations of the 3D Motion Model:** The current geometric model (Eq. 7) effectively handles transformations parallel to the image plane but is a simplification of true 3D motion. This might limit its ability to perform more complex perspective changes, such as pulling an object \"out of\" the scene. Exploring a more comprehensive 3D motion model could be a valuable future direction.", "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": "lDUJBnn6lT", "reviewer": "Reviewer_m8ds", "summary": "GeoDrag is a geometry-aware drag-based image editing framework combining 3D structural cues and 2D spatial priors. It predicts displacement fields in diffusion latent space, incorporating depth-based modulation and spatial plane adjustment, along with a conflict-free partitioning scheme for multi-point editing.", "questions": "1.\tHow sensitive is the method to depth map errors?\n2.\tCan GeoDrag operate with estimated depth from monocular models?\n3.\tIs the diffusion model re-trained or used as a frozen backbone?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Addresses a concrete weakness: Most drag methods ignore geometry, causing distortions. GeoDrag effectively solves this.\n\n2. Principled integration: The combination of 3D depth modulation and 2D plane adjustment is physically intuitive and well-designed.\n\n3. Practical contribution: One-step editing drastically improves usability and speed over iterative gradient-based methods.\n\n4. Strong empirical results: Quantitative improvements (1.4× DAI, 1.1× MD) and excellent qualitative visuals demonstrate real impact.\n\n5. Multi-point conflict resolution: - The conflict-free partitioning module is a simple yet powerful idea that improves robustness.\n6. Extensive evaluation: Diverse examples (faces, landscapes, multi-object scenes) show generality.", "weaknesses": "1.\tDepth dependency: Accuracy hinges on reliable depth maps; the paper assumes ideal depth input without addressing noise robustness.\n2.\tLimited theoretical grounding: The interaction between geometry and diffusion latent space lacks mathematical explanation.\n3.\tAblation insufficiency:The individual contribution of geometry-aware vs. plane-aware modules is unclear.\n4..\tEfficiency discussion shallow: Though claimed efficient, actual GPU/latency data is minimal.\n5.\tApplicability scope: Focused on still images; no experiments on video or non-diffusion backbones.\n6..\tMinor clarity issues: Some figures are over-compressed, making displacement fields hard to interpret.", "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)