FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Authors: Gwanhyeong Koo
Sunjae Yoon
Younghwan Lee
Ji Woo Hong
Chang D. Yoo
Venue: ICML 2025 spotlightposter (2025)
Keywords: Drag-based editing
Image editing
Diffusion model
Primary area: applications->computer_vision
TL;DR: FlowDrag leverages 3D mesh deformation to guide stable, geometry-consistent drag-based image editing and introduces VFD-Bench, a benchmark with explicit ground-truth edits.

Abstract: Drag-based editing allows precise object manipulation through point-based control, offering user convenience. However, current methods often suffer from a geometric inconsistency problem by focusing exclusively on matching user-defined points, neglecting the broader geometry and leading to artifacts or unstable edits. We propose FlowDrag, which leverages geometric information for more accurate and coherent transformations. Our approach constructs a 3D mesh from the image, using an energy function to guide mesh deformation based on user-defined drag points. The resulting mesh displacements are projected into 2D and incorporated into a UNet denoising process, enabling precise handle-to-target point alignment while preserving structural integrity. Additionally, existing drag-editing benchmarks provide no ground truth, making it difficult to assess how accurately the edits match the intended transformations. To address this, we present VFD (VidFrameDrag) benchmark dataset, which provides ground-truth frames using consecutive shots in a video dataset. FlowDrag outperforms existing drag-based editing methods on both VFD Bench and DragBench.

FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Gwanhyeong Koo
Sunjae Yoon
Younghwan Lee
Ji Woo Hong' Chang D. Yoo
Abstract
Drag-based editing allows precise object manipu- lation through noint-based control, offering user convenience. However, current methods often suffer from a geometric inconsistency problem by focusing exclusively on matching user-defined points, neglecting the broader geometry and lead- ing artifacts unstable edits. We propose FlowDrag. which leverages geometric informa- tion for more accurate and coherent transforma- tions. Our approach constructs a 3D mesh from the image, using an energy function to guide mesh deformation based on user-defined drag points. The resulting mesh displacements are projected into 2D and incorporated into UNet denoising process, enabling precise handle-to-targe point alignment while preserving structural integrity. Additionally, existing weiggee benchmarks provide no ground truth, making it difficult to as- sess how accurately the edits match the intended transformations. To address this, we present VFD (VidFrameDrage benchmark dataset, which pro- vides ground-truth frames using consecutive shots in video dataset. FlowDrag outperforms existing drag-based based editing methods on both VFD Bench and DeagHench.
YY
N
YN
(a) User Edit
(b) DiffEditor
(c) GoodDrag
(d) FlowDrag (ours)
Figure I. Results of drag-based image editing. While other meth- ods only optimize around user-specified points, failing to preserve the Statue of Liberty's structure (first row). FlowDrag maintains overall integrity. In the second row, FlowDrag stably rotates the woman's face from her nose without distorting her hat hand, whereas other methods fail to maintain geometric consistency.
Tumanyan et al., 2023; Cao et al., 2023; Koo et al., 2024a), which uses user-provided prompts, has shown promising results but often struggles with precise and fine-grained ed- its. Even slight variations in text can lead to vastly different results, making it difficult for users to achieve consistent and detailed modifications, thereby highlighting the chal- lenges of achieving precision in text-to-image editing. To address this, DragGAN (Pan et al., 2023) introduced a point- dragging method, enhancing user convenience for more precise editing. However, due to the constraints GANs, struggled with general image performance. This limitation led interest in exploring drag- image editing us- ing diffusion models, which can be categorized into two main approaches: motion-based (Shi et al., 2024; Ling et al., 2023; Liu et al., 2024; Zhang et al., 2024) and gradient- guidance-bart based (Mou et al., 2023; 2024) methods. The motion- based approach in drag-based image editing consists of two processes: motion supervision and point tracking. Motion supervision measures the difference between the handle and target points in the UNet decoder's feature map and applies it to optimize the latent, gradually shifting the handle point toward the target. Subsequently, point track- ing updates the handle point's position in the feature map, ensuring alignment with the progressively edited result. In contrast, maudient-based methods derive inspiration from score-based (Song al., 2020b; Dhariwal & Nichol, 2021)
drag-
1. Introduction
The advancements in text-to-image (T2I) generation dif- fusion models (Ramesh et al., 2022; Saharia et al., 2022; Nichol et al., 2021; Li et al., 2023) have significantly en- hanced image generation capabilities. Leveraging pretrained T21 diffusion models (Rombach et al., 2022) trained on large scale dataset, the image editing field has also seen substan- tial progress. Text-based image editing (Hertz et al., 2022;
"orraa Advanced Institute of Science and Technology (KAIST). Correspondence to: Chang D. Yoo krik.
Proceedings of the 4nnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
XT
Goodbing
Destaror
U-Net
085
Mettech
User
27:
Drighene
(b) Current Drag-based Editing System
065
Sowithes
U-Net
[cl) Comparison of Drag-Editing Methods DragBench
Edit
2D Vector Flow Field
Result
(VF Field)
3D Mesh
Mesh Deformation
2D VF field
wegg wegg
Draphate
User Edit
GoodDrag
FlowDrag (ours)
Handle
Target Point
(e) Comparison of Drag-Editing Methods on VFD bench
Drag Editing Results
(c) Overview of the FlowDrag for Generating 2D Vector Flow Field
Figure (a) GoodDrag vs. FlowDrag (ours). GoodDrag fails to preserve object geometry, while FlowDrag retains structural integrity. (b) Existing drag-editing systems focus only on moving user-defined handle points to target positions. (c) FlowDrag overview: from 3D mesh creation generating a 2D vector flow field. (d) Comparison FlowDrag with other methods on DragBench. (e) Comparison FlowDrag with other methods on our VFD-Bench dataset.
diffusion model, utilizing gradient guidance governed by an energy function to perform the drag-based editing.
proceeds through several stages. First, we construct a 3D mesh from the original image to represent the object's ge- ometry. Second, we employ a progressive SR-ARAP (Levi & Gotsman, 2014) approach to deform the mesh from user- defined handle points target points, preserving the object's geometric integrity throughout the transformation, illus- trated in Fig. 2(c). Third, after the mesh deformation, we compute the differential coordinates between the original and modified mesh and project them onto 2D vector field. Finally, this vector field is integrated into the motion supervi- sion phase of the UNet's denoising process, which enhances both spatial accuracy and edit stability. Unlike existing methods restricted to user-defined drag editing points, Flow- Drag leverages a continuous displacement field derived from mesh deformations, preserving both spatial and geometric coherence for more reliable results.
However, existing drag editing methods often suffer from a geometric inconsistency problem, leading to artifacts or significant deviations from the original object's structure. These issues become evident in the first row of Fig. (b)- (c), where the existing model fails to preserve the statue's s integrity, causing the arm and torch to become noticeably altered and leading to visible artifacts. Similarly, in the second row of Fig. we attempt to rotate a woman's 's face by placing drag arrow on her nose. While her hat and the hand resting on her face should naturally follow this rotation, existing methods shift the nose but fail to preserve the hat and hand, resulting in unnatural deformation. We identified that these problems primarily stem from current methods' exclusive focus on matching feature correspondence be- tween user-defined handle and target points, neglecting the broader geometric context of the image, as shown in Fig. 2(b). This issue is particularly prominent in edits that re- quire preserving rigid parts of the object. Therefore, we define any transformation that must maintain rigidity (such as rotation, relocation, or pose changes) as a "Rigid Edit." " In contrast, transformations that do not require strict rigidity, such as rescaling (which alters proportions), are referred to as "Non-rigid Edits." " In this paper, we focus on "Rigid Edits" ensure structural integrity during drag editing.
We also propose a new drag-editing benchmark called VFD- Bench. Existing benchmarks, such as DragBench, do not provide ground-truth edited images for each input, making it difficult to accurately assess editing quality. The Image Fidelity metric (I-LPIPS) measures similarity between the original input and the edited result, often assigning lower scores to successful soometry-preserving edits. For instance, Fig. (d)) preserves geometry well but receives a relatively low 1-LPIPS score of 0.72 compared to Fig. 1(a), indicating that this metric does not fully capture structural accuracy. To address this, VFD-Bench provides video-derived datasets where consecutive frames serve paired input images and ground-truth edits, enabling more precise evaluation. Our FlowDrag achieves the highest MD metric on DragBench,
To overcome these limitations, propose FlowDrag, novel method designed to ensure stable and accurate image edits by preserving geometric information. Our approach
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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
and shown in Fig.2(d), also outperforms competing ap- proaches the new VFD-Bench, validating its effective- ness in maintaining geometric consistency.
wal & Nichol, 2021), enabling more creative edits but often causing artifacts reduced fidelity. Although motion-based approaches better preserve visual fidelity, they still struggle with geometric integrity due to limited structural understand- ing. To address this, we incorporate 3D mesh deformation to add explicit geometric information in 2D.
2. Related Work
2.1. Text-based Image Editing
3. Preliminary
Early GAN-based methods (Patashnik et al., 2021; Xia et al., 2021) edited images by inverting them into StyleGAN latent spaces conditioned on textual descriptions. However, these methods had limited flexibility due to inherent trade-offss between generalized editing capability and reconstruction quality. Recently, efffusiin-bassed methods (Kawar et al., 2023; Couairon et al., 2022; Brooks et al., 2023) enabled more precise and diverse edits by directly guiding the diffu- sion process with text prompts. Prompt-to-Prompt (Hertz et al., 2022) and Plug-and-Play (Tumanyan et al., 2023) further improved editing precision through attention fea- ture injection mechanisms. More recent work also explored adjusting object poses and perspectives (Cao et al., 2023; Koo et al., 2024a; Yoon et al., 2024a). With advances in text-based editing techniques, various studies utilized these methods for tasks such as improving editing efficiency (Koo al., 2024b; Deutch et al., 2024), human image animation (Yoon et al., 2024c), and virtual try-on (Hong et al., 2025). However, text-based methods often lack the precision re- quired for fine- fine-grained control, as minor textual variations can result in unintended edits. This limitation has motivated the emergence of drag- based editing methods, which offer more direct and intuitive control.
3.1. DDIM Inversion
DDIM (Song et al., 2020a) eliminates the stochastic ele- ments of DDPM (Ho et al., 2020), producing deterministic, non- Mon-Markovian process for precise control over diffusion steps. A U-Net denoiser network, cg. enables both sampling (Eq. (I)) from noise to image and inversion (Eq. (2)) from image to noise. Here, at denotes the noise schedule at step t, and 2 the latent representation that step.
lat+1
a+t1 21 + (V1-
ar - 1) co(2e,t), (1)
21+1 =
at 04-1
2-11 + (V V--ae 1) co(=i-1:1-1 1). (2)
In drag editing, DDIM Inversion is applied to obtain the final DDIM latent 2 by progressively adding noise from 20 to 24. As detailed in Section 3.2, this latent is optimized via motion supervision to refine editing results.
3.2. Diffusion Latent Optimization in Drag Editing
Motion Supervision Motion-based drag editing typically applies a motion supervision loss, Liss to iteratively shift n handle points {he toward target points {t For- mally, the motion supervision loss is defined as:
2.2. Drag-based Image Editing
Drag-based image editing modifies images using user-drawn drags, offering precise and interactive control for tasks such as rotation, relocation, and rescaling. While early GAN- based methods (Pan et al., 2023) suffered from limited edit fidelity, diffusion models significantly improved both edit- ing quality and diversity. Diffusion-based drag editing is divided into two categories: motion-based and gradient- guidance-based. Motion-based methods (Shi et al., 2024; Ling et al., 2023; Liu et al., 2024; Zhang et al., 2024) rely on motion supervision and point tracking to iteratively shift handle points toward targets, preserving the original struc- ture. Specifically, DragDiffusion optimizes the DDIM latent at specific timestep (t=35), while Drag Your Noise targets bottleneck features in the U-Net across all timesteps. Good- Drag further introduces the AIDD framework, alternating drag operations and denoising across multiple timesteps to reduce cumulative changes and enhance fidelity. contrast, gradient-guidance guidance-based methods (e.g., DragonDiffusion (Mou et al., 2023), DiffEditor (Mou et al., 2024)) use gra- dient updates driven by energy function derived from score-basee based diffusion models (Song et al.. 2020b; Dhari-
Lams (ate=t)
Σ
Potta(=)-sg((1) sg(F((=)))
qEP(N .2)
sg(=f_1) @1 (1- M) (3) + X|| (
where 6, [| la is the normalized direction from I to 4i P(ht, R) is a patch of radius R around ht, F denotes the U-Net feature map, k is denoising timestep and sg(-) is the sop-gradient operator. The term À weights the regularization that keeps { close to the reference (>f_1) } outside the masked region M. At each iteration, we compute dCms/ /0z and update 2f via gradient descent:
4
2 Cma (2f) 2z
2
(4)
where 77 is the learning rate. This process gradually moves each handle point closer to its target in the latent space.
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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Step 1. 3D Mesh Generation
Step 2. Progressive Mesh Deformation and 2D Vector Flow Generation
Depth-bases Approach
3D Mapping
Handle Vertex Pn
Target Vertex P;
Intermediate Vertex of Edit Mask
Input Image
Handle Point
Input Image
Depth Map
DepthMesh
Target Point
Movable Vertices
Top-view
Constraint
Vt 1/ Ph
Diffusion-based Approach
Progressive SR-ARAP
Projection
Image-to-3D Diffusion Model
Input Image
DiffMesh
Original Mesh
k-th iteration
Final Deformed Mesh
Difference
2D Vector Flow Field
Figure 3. Overview of the FlowDrag pipeline. (a) Step I: 3D mesh generation using depth-based and diffusion- based approaches. (b) Step Progressive mesh deformation via SR-ARAP. with differences projected as vector flow field.
Point Tracking Following motion supervision, point tracking updates the handle points { by searching for the best matching features within a local patch. Specifically,
function, maintaining local rigidity under fixed constraints. The energy function is defined as:
h arg min [F((++)) Fng((e))l (z))l (5) MM)
Σ Σ why ||s: R. 0;) -(;; 1z)ll iEV JEN()
(EP(N))
(6) where RG and 8; are internally optimized rotation matrices and local scale factors for each vertex i (with 8; set to 1, preventing scale changes). The terms Wy represent cotan- gent weights, reflecting the stiffness rigidity between vertices. The notation (2) denotes the set of neighbors for vertex i, specifically those vertices directly connected by an edge, and j denotes each neighboring vertex within this set. Here, 0;,0; represent vertex positions optimized during deformation, whereas v;, Uy refer to positions from the orig- inal undeformed mesh. During the optimization process, these positions ég.0; iteratively updated to minimize the energy function, with only movable vertices adjusted, while constraints remain fixed as boundary conditions.
where P(h R2) is square patch of radius R2 around h. This step aligns each handle point with the corresponding features in the updated latent. Alternating motion supervi- sion and point tracking guides handle points progressively closer to their targets.
3.3. Geometric Mesh Deformation
this section, we introduce widely utilized geometric mesh deformation methods, the As-Rigid-As- -eei------Pllll (ARAP) (Sorkine & Alexa, 2007) approach and its enhanced vari- ant, Smoothed Rotation As-Rigid-As-Posibbll (SR-ARAP) (Levi & Gotsman, 2014). Let M = (V, F) represent the source mesh, where V consists of vertices v2 = (a,, 3/s 2,) in R and F forms faces that are triangles formed by these vertices. The deformed mesh is denoted as M (V. F), where V consists of the deformed vertex positions 0 (6,, Di, 2,), maintaining the same connectivity.
SR-ARAP.
Smoothed Rotation ARAP extends the ARAP formulation by adding a ceaation-consittnng term:
ESR. FEARAP (M) + Σ
Σ RR R1l?, (7)
JEN()
where R; and R, are the per-vertex rotation matrices, is a regularization parameter. This extra term penalizes sig- nificant rotational discrepancies between adjacent vertices, resulting smoother deformations, Specifically. a larger rotation difference || Ry I| increases the energy, prompt- ing the optimization process minimize these differences. As result, rotations among adjacent vertices remain as consistent as possible, ensuring smoother and more natural deformations. These formulations facilitate controlled mesh deformation, beneficial for achieving smoother and locally rigid structural modifications,
ARAP. ARAP aims to preserve local rigidity while al- lowing controlled deformations. Initially, users designate certain vertices as constraints, including vertices explicitly moved to desired positions (e.g., handle points moved tar- get points in drag editing) and vertices outside the editable region that remain unchanged. The remaining vertices are considered movable. Setting these constraint vertices trans- forms the original mesh V to the new constraint positions V. from which optimization begins. The ARAP method adjusts movable vertex positions by minimizing an energy

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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Step 3. Vector Flow-based Drag Editing
Mesh Deformation
Handle Vertex
Point Sampling
Motion Supervision Tracking
Handle points P. Target points
Terget Vertox Edit Mack
Original Mesh
Deformed
Difference
2D Vector Flow Field
UNet Decoder's Feature Map
Invert
Drag Editinng
Optimized Map 4;
XT
Branch
DDIM
U-Net
Noisy Latent Map 2
Input Image
User Edit
Result
Guidance Branch
DDIM Inversion
U-Net
2D Projection of the Deformed Mesh
Layout Feature Injection
Reconstruction
Figure Step 3: Vector Flow-Based Drag Editing in FlowDrag. The 2D vector flow field employed for motion supervision and point tracking. Meanwhile, the 3D deformed mesh is projected onto 2D plane and incorporated as layout feature through the guidance branch at an early denoising stage. Through these processes, geometric consistency is enhanced during drag editing.
4. Method
summarizes this procedure in detail.
FlowDrag addresses the geometric inconsistency problem in drag-based editing by incorporating 3D mesh deformation. First, we generate a 3D mesh from the input image using a depth-based or diffusion-based approach (Fig. 3-Step 1). Next, we deform the mesh using progressive SR-ARAP and derive 2D vector flow from deformed mesh (Fig. 3-Step 2). Finally, we integrate the 2D vector flow into the motion-based drag-edit pipeline to achieve geometry-aware movements, and utilize the 2D projection of the deformed mesh layout features to preserve structural consistency and ensure accurate shape preservation (Fig. 4-Step 3). The following sections describe each these steps detail.
Although this depth-based approach is simple and fast, cannot account for unseen regions from single viewpoint. Therefore, we additionally employ image-to-3D diffusion models (Xiang et al., 2024; Zhao al., 2025), which infer hidden structures to produce more complete meshes. We refer to the mesh generated by depth-based approach as DepthMesh, and the one produced by the diffusion based approach as DiffMesh, as illustrated Fig. 3-Step 1. We employ both meshes in our experiment to explore different levels geometry detail and completeness.
4.2. Progressive Mesh Deformation and 2D Vector Flow Generation
4.1. 3D Mesh Generation: Depth- and Diffusion-based Approaches
We begin by mapping the user-defined handle point, target point, and mask from the 2D drag-editing setting onto our 3D mesh M = (V, F). In this mapping, handle vertex Up E V corresponds to the handle point, and its target vertex t2 E V corresponds to the target point. The masked region indicates which vertices are movable, while all other vertices become constraints (see Fig. 3-Step2). Following the ARAP principle, each movable vertex adjusts its position by minimizing the ARAP energy, preserving local rigidity. In contrast, the constrained vertices remain fixed their designated positions.
We first generate 3D mesh from the input image by ex- tracting a depth map and using it as a foundation. For depth extraction, we adopt the Marigold (Ke al., 2024) model, which reliably provides depth information from single image within few seconds. The depth-based mesh struction then proceeds in three stages. First, in the vertex mapping step, each pixel's depth value is converted into a corresponding vertex coordinate 3D space. Next, the facet formation step connects adjacent vertices whose depth values are sufficiently similar, forming smooth surfaces. Finally, the artifact reduction step excludes connections between vertices with large depth differences. minimizing discontinuities and effectively separating the foreground object from its background. Algorithm I in the appendix
Progressive Deformation with SR-ARAP. In ARAP- based methods, the handle vertex 2n is typically moved directly to its target position 2/ followed by mesh deforma- tion through ARAP energy minimization. However, when
5

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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
the distance between Uj and Vt is large, directly moving th can cause abrupt local distortions and unnatural defor- mation (Sorkine & Alexa, 2007; Chen et al., 2017). Such distortions arise because large vertex displacements signif- icantly stretch local edges, increasing ARAP energy and potentially causing convergence to suboptimal local minima. To mitigate these issues, we propose a progressive deforma- tion strategy with two components. First, we incrementally move the handle vertex toward its target over K iterations:
[k+1] = u/2 + + - ),,, )*),, 0 O<ASı, 1, U/2
Iepust
Image
Imputt
(8)
Figure 5, Example images from VFD-Bench. We constructed this drag-bacc based image editing dataset by selecting closely spaced frames from DAVIS, LOVEU-TGVE, and copyright-fre free Pexels videos, focusing on noticeable changes in pose or structure.
(K) = Ut and where 2/h is the initial handle position, 2//
0 S k < K. The parameter À controls the fraction of re- maining distance covered at each step, smoothly distributing large displacements. Second, we introduce Inter-Step Smoothness term to the SR-ARAP energy (Eq. 9), penaliz- ing large vertex displacements between iterations to ensure gradual and stable deformation:
and keeps only the top few, and (2) Uniform sub-sampling, which retains vectors at regular intervals to ensure broad coverage. Either method yields a final subset Φ typically containing 5-30 vectors that capture the most significant or representative displacements. We then restrict the summa- tion in Eq.3 to $ focusing the motion supervision on these carefully selected flow vectors.
ESR- Anterttt (1)( = FESR-RRAT ( (NT(++)))
Σ 10 (((k+)) - 0/ 0/(0
+ +33
NEV
Layout Feature Injection In addition to leveraging our vector flow for latent optimization, we introduce a guid- ance branch utilizing the 2D projection of the deformed mesh, (M). to provide complementary geometric context. Specifically, as illustrated in Fig. 4, we first invert T (M) via DDIM Inversion to obtain a latent noise representation. During the denoising process, we inject selected attention features from this representation into the primary drag-edit branch. Previous diffusion studies (Wu et al., 2023b; Yoon et al., 2024b) have shown that earlier timesteps establish broad structural outlines, while later timesteps refine finer details. Following this insight, we inject attention features only from earlier or intermediate timesteps, embedding ap- proximate layout information derived from the deformed mesh. This ensures broader geometric context is transferred without imposing overly specific or potentially conflicting details, as the deformed mesh may not perfectly align with the user's final desired details. Thus, this layout feature injection complements cector-flow-bard optimization by explicitly providing structural context, guiding the main edit branch toward a more geometrically consistent edited result.
where e(*)) and o(*+1) are the positions of the movable vertices at iterations & and k+1. The parameter 3 adjusts the strength of this regularization, with higher values promoting smoother transitions and smaller vertex movements, while lower values allow larger displacements.
2D Vector Flow Generation. After K iterations, the orig- inal mesh M converges to final deformed mesh M. We then project both meshes onto the 2D image plane, denoted by π (M) and T T(M), respectively. The 2D vector flow 4 is defined based per-vertex displacements:
+- = ((Ar,, A9:)) | Ar; = 2/- Is. Au = 5/ }
(10)
where (I.,, 34) and (x/: 16) are the 2D coordinates of vertex i in (M) and π (M). Thus, by capturing how each ver- tex moves from M to M, the vector flow & encodes the geometric changes induced by our progressive SR-ARAP approach. In the next section, we show how both & and (M) integrate into the motion-based drag-edit pipeline, providing geometry-awarc guidance for image editing.
4.3. Vector Flow-based Drag Editing
5. VFD-Bench Dataset
Vector Flow Sampling We focus on selecting an optimal set of vectors from the 2D flow field & for motion supervi- sion (F).3) and point tracking (Eq. 5). First, we uniformly sample an N x N grid of candidate vectors within the edit mask, taking N positions along each axis. To refine this set, we explore two approaches: (1) Magnitude-basee based sam- pling, which sorts all candidates by displacement magnitude
Existing drag-based editing benchmarks, such Drag- Bench, provide input images along with user-defined han- dle/target points and masks, but do not include ground-truth (GT) edited images. As a result, commonly used metrics like Image Fidelity (IF) and Mean Distance (MD) are com- puted between the input and the edited output. Specifically, IF (measured as 1-LPIPS) assesses how closely the edited
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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
image resembles the original, while MD uses DIFT features to measure how effectively the handle points have moved to their targets. However, in cases where rotation or pose changes are successfully introduced, IF often yields low scores simply because the edited object differs from the original layout, even though the edit is successful.
Table 1. Performance of recent drag- baag-baaeddde editing methods on the DragBench dataset.
Method
1 LPIPST MD.
DragBench
DiffEditor (Mou et al., 2024)
0.89
28.46
DragDiffusion (Shi et al., 2024)
0.89
33.70
To address these limitations, we propose VFD-Bench, new dataset that provides an explicit GT image for each input. As illustrated in Fig. 5, VFD-Bench is constructed by selecting pairs of video frames (from sources such as DAVIS (Pont-Tuset et al., 2017). LOVEU-TGVE (Wu et al., 2023a), TVR (Lei et al., 2020) and copyright-free clips on Pexels') where an object undergoes a clear change in pose or structure. In total, we construct 250 input-GTT pairs suitable for drag-based editing. For each pair, the handle points and target points defined based on the differences between the input and ground-truth (GT) images, with 1-5 drags assigned per sample. Further details regarding the dataset annotation process and the number of images per category are provided in Appendix C.
DragNoise (Liu et al., 2024)
0.63
33.41
FreeDrag (Ling et al., 2023)
0.70
35.00
GoodDrag (Zhang et al., 2024)
0,86
22.96
FlowDrag (ours)
0.82
22.88
Table 2. Performance of recent drag- baag-based editing methods on the VFD-Bench dataset. Method
PSNRT 1 -LPIPS MDJ
VFD-Bench
DiffEditor (Mou al., 2024)
21.5
0.72
24.88
DragDiffusion (Shi et al., 2024)
24.5
0.78
36.52
DragNoise et 2024)
22.3
0.68
36.21
FreeDrag (Ling et al., 2023)
22.0
0.74
38.32
GoodDrag (Zhang et al., 2024)
25.2
0.82
25.65
FlowDrag (Ours)
26.3
0.85
24.51
5.1. Evaluation Metrics
Eq. 6 using Open3D library, and set a between 0.2 and 0.4. Additionally, we conduct ablation study on the Inter-Step Smoothness term (Section 6.4), exploring 3 E [0, 1] in Eq. 9. To integrate vector flow into drag editing, we uniformly sample points from a 20 x 20 grid (N = 20) within the 2D flow field over the edit mask, selecting between 5 and 30 points for optimization. This setup balances computational efficiency with adequate coverage of the flow field.
Unlike previous benchmarks, VFD-Bench enables direct comparison of edited images to actual GT results. We mea- sure Image Fidelity using both PSNR (at the RGB level) and LPIPS (at the feature level). For assessing how well handle points align with target points, we retain the Mean Distance (MD) metric. Since video frames can contain changing backgrounds unrelated to the target object, we compute all metrics within the user mask to focus on the edited region. This approach allows for a more reliable and detailed evaluation of drag-basee based editing methods.
6.2. Datasets and Evaluation Metrics
Datasets We evaluate FlowDrag on the DragBench (Shi et al., 2024) and our proposed VFD-Bench. DragBench contains 205 images of diverse content, along with 349 pairs of handle and target points. Each image has or more dragging instructions (i.e., and a mask that specifies the editable region. VFD-Bench, introduced in Section 5, includes 250 images selected from video sources (DAVIS, TGVE, TVR, and Pexels), cach paired with a ground-truth (GT) edit.
6. Experiments
6.1. Implementation Details
point pairs)
We validate FlowDrag using the pre-trained Stable Diffu- sion 1.5 model (Rombach et al., 2022), processing images at a resolution of 512* x 512. For image encoding and decod- ing, we use VQ- VAE (Razavi et al., 2019). Following prior motion-based approaches (Ling et al., 2023; Zhang et al., 2024; Liu al., 2024), we fine-tune the input image with LoRA (Hu et al., 2021) (rank- 16) for 200 steps. DDIM Inversion is applied up step 38 (75% of the total 50 de- noising steps), as in (Zhang et al., 2024), with layout feature injection at timestep /' = 30. For 3D mesh generation, we primarily utilize DiffMesh. However, when DiffMesh exhibits significant artifacts or deviates substantially from the original image, we employ DepthMesh instead. In Pro- gressive SR-ARAP mesh deformation, compute my in
Metrics We evaluate both fidelity and precision for each edited image. On DragBench, where no ground truth (GT) is provided, we measure Image Fidelity (IF) as 1-LPIPS and compute the Mean Distance (MD) using DIFT (Tang et al., 2023) track how well handle points move their targets. In VFD-Bench, which provides GT images, quantify fidelity with both PSNR (RGB-level) and LPIPS (feature-level) while retaining MD for point alignment, as described in Section 5. Since VFD-Bench frames can in- volve irrelevant background changes, we compute metrics within the user-defined mask to focus on the edited region.
Pexelss frree videos at (Btps://www.pexel..cmm)
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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
User Edit
DiffEditor
FreeDrag
GoodDrag
FlowDrag (ours)
Drag Accuracy
Average Score
Image Quality
3
2-
0
DiffEditor
FreeDrag
GoodDrag
FlowDrag
Method
Figure 7. User study on drag accuracy and image quality.
0.54
L.PPPS
P3HR
34.5
0.78
10
20
Number of Vectors
PSNR value number
(b) 1-UPIP value the number
Figure 8. Impact of vector count on PSNR and (b) 1-LPIPS. When 10 vectors are sampled, both metrics achieve their highest values, indicating the importance of optimal vector selection.
Figure Qualitative results with recent drag-based image edit- ing systems. Our FlowDrag produces outputs that maintain geo- metric consistency more effectively than other methods.
User Study. We conducted user study on images from DragBench and VFD-Bench, comparing FlowDrag with DiftEditor, FreeDrag, and GoodDrag. We recruited 25 volunteers to rank each method's 's edited results (4 = best, 1 == worst) based on drag accuracy and image quality, As shown in Fig. 7, FlowDrag consistently received higher scores than the other methods in both aspects.
6.3. Experimental Results
Qualitative Comparisons. We compare Flow Drag with current drag-based editing methods in Fig. 6. Although motion- based-aadddddeee approaches generally outperform the gradient- guided DiffEditor, FreeDrag and GoodDrag often neglect the object's broader spatial structure by relying solely on user drag points. contrast, FlowDrag leverages vector flow field from mesh deformation, enabling more cohesive transformations. For example, in the first row of Fig. 6, only one drag point is provided. DiffEditor fails to move the face, FreeDraag relocates the face but leaves the hat unchanged, and GoodDrag removes the brim. However, FlowDrag adjusts both the face and the hat, illustrating how spatially informed vectors yield more natural edits with fewer artifacts. Additional qualitative comparisons on Deach and VFD-Bench are provided in Appendix F.
6.4. Ablation Study
We conducted an ablation study in FlowDrag on VFD-Bench by evaluating the following three aspects: (1) influence of the regularization parameter 3 in progressive SR-ARAP deformation, (2) effect of selected vector count on perfor- mance, (3) magnitude-basee vs. uniform sub-sampling in 2D vector flow field sampling.
Influence of /3 in Progressive SR-ARAP We investi- gate the effect of varying the parameter 3 of the Inter-Step Smoothness term in Eq.9 on local rigidity preservation dur- ing mesh deformation. To quantify this, we introduce rigid- ity measure (see Appendix A. based on mean edge length ratios (MELR) and the Mean ARAP Error (mARAP MuAARPPErroo between the original mesh M and its deformed counterpart M. The results for E {0.2, 0.4, 0.6, 0.8, 1.0] are pre- sented in Table 4. Higher 3 values penalize large vertex displacements between iterations, resulting in smoother de- formations but potentially restricting flexibility. Notably, 8 = 0.8 yields the best results, achieving maximal mean edge length ratio and mean ARAP error, indicating effective shape preservation with stable and smooth vertex transitions.
Quantitative Results. On DragBench, FlowDrag achieves the best Mean Distance (MD), indicating effective dragging, as shown in Table However, DiffEditor scores highest the 1-LPIPS fidelity metric because it induces minimal edits. In VFD-Bench, which includes actual ground-truth images from real video frames, FlowDrag outperforms all methods in PSNR, 1-LPIPS, and MD, demonstrating its effectiveness in preserving geometric consistency while delivering accurate edits.
8

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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Editing
Mesh
User Edit
DiffMesh
2D vector flow
Results
Constraints
Deformation
Handle Points
Handle Vertex
Movable Vertices
Deformation Vector
Target Points
Target Vertex
Constraint Vertices
Figure 9. Visualization of the mesh-guided editing process using DiffMesh. Our FlowDrag process begins with user-defined edits specifying handle and target points. DiffMesh reconstructs a structured 3D mesh, from which editing constraints (movable and constraint vertices) are defined. Guided by these constraints, the mesh undergoes deformation, yielding clear deformation vectors. These deformation vectors are projected onto 2D vector flow field, where representative vectors are sampled. These sampled vectors provide accurate geometric guidance, resulting stable and precise image editing outcomes.
7. Limitation and Future Work
Table Comparison of sampling strategies using MD and 1-LPIPS metrics on VFD-Bench.
FlowDrag effectively addresses geometric inconsistencies prevalent in current drag-based editing methods, but sev- eral limitations remain. First, our method relies on stable mesh deformation, inherently limiting feasible dragging dis- tances. Thus, our method is optimal for moderate dragging operations that preserve structural coherence. Additionally, FlowDrag primarily supports rigid edits, but struggles with significant content creation or removal tasks requiring ma- jor structural changes. Lastly, FlowDrag projects 3D mesh deformation onto a 2D plane, inherently losing detailed 3D structural information. This issue is compounded by FlowDrag's 's reliance on Stable Diffusion, which lacks 3D understanding. While our approach enhances geometric coherence, it cannot fully preserve the original 3D geometry. We believe future work could benefit from exploring inher- ently 3D-aware or motion-aware diffusion models, such as video diffusion, to better capture object dynamics and structures, enhancing 3D understanding in image editing.
Sampling Strategy
MD +
1-LPIPS
Uniform Sub-sampling
24.75
0.83
Magnitude-basee Sampling
24.51
0.85
Effect of Selected Vector Count on Performance We analyzed the impact of the number of selected vectors in & on the PSNR metric. Fig. 8 shows the results, indicating the best performance at 10 vectors. These results emphasize the importance of selecting an optimal vector count for effective editing performance.
Magnitude- vs. Uniform Sub-sampling in 2D Vec- for Flow Field Sampling We compare magnitude-based sampling and uniform sub-sampling using Mean Distance (MD) and Image Fidelity (I-LPIPS). As shown in Table 3, magnitude- based sampling consistently achieves lower MD and higher 1-LPIPS scores, indicating more effective vector selection. This highlights the benefit of prioritizing vectors with larger magnitudes in the flow field.
8. Conclusion
In this paper, we propose FlowDrag, a framework designed to address geometric inconsistencies in drag-based image editing via 3D mesh deformation. Additionally, we intro- duce VFD-Bench, benchmark providing explicit ground- truth edits. Our experiments demonstrate FlowDrage supe- rior editing quality and geometrie coherence both Drag- Bench and VFD-Bench.
Detailed visualizations of FlowDrag's 's mesh-guided editing process are shown in Fig.9 and Fig. 16. Additional sensitivity analysis on how varia- tions in mesh deformation influence editing outcomes can be found in Appendix D, and further analysis mesh de- formation efficiency provided in Appendix E.
More Visualization Results

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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Impact Statement
Ho, J., Jain, A., and Abbeel, Denoising diffusion proba- bilistic models. Advances in neural information process- ing systems, 33:6840-6851, 2020.
Drag-based image editing offers an intuitive and precise way to alter images, greatly enriching creative workflows. How- ever, this technology also raises concerns about authenticity and potential misuse in generating deceptive manipulated media. Ensuring transparency through watermarking and responsible deployment is crucial to mitigate risks while harnessing its full creative potential.
Hong. J. W., Ton, T., Pham, X., Koo, G., Yoon, S., and masked
Yoo, C. D. Ita-mdt: saage-timestep-adaaee diffusion transformer framework for image-based virtual try-on. arXiv preprint arXiv:2503.204111 2025.
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L.,, and Chen, W. Lora: Low-rank adaptation of large language models. arXiv preprini wxXiv:216.00968. 2021.
Acknowledgements
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea governmen((MSIT) (No.SS- 2021-1121 1381, Development of Causal AI through Video Understanding and Reinforcement Learning, and Its Ap- plications to Real Environments) and partly supported by Institute of Information & communications Technology Planning& & Evaluation (IITP) grant funded by the Korea www.RSS2222-112000 Development and Study of AI Technologies to Inexpensively Conform to Evolving Policy on Ethics).
Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I., and Irani, M. Imagie: Text-based real image editing with diffusion models. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 6007-6017, 2023.
Ke, B., Obukhov, A., Huang, S., Metzger, N., Daudt, R. C., and Schindler, K. Repurposing diffusion-basee image generators for monocular depth estimation. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9492-9502, 2024.
Dovernmen((MSIT)
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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Yoon, S., Koo, G., Lee, Y., and Yoo, C. Tpe: Test-time procrustes calibration for diffusion-based human image animation. Advances in Neural Information Processing Systems, 1FF118654-118777 2024c.
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2.0: Scaling diffusion models for high resolution textured
3d assets generation. arXiv preprint arXiv:2501.12202, 2025.
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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
A. A comparison of progressive SR-ARAP performance in mesh deformation
Table 4. Ablation study on the regularization parameter in Pro- gressive SR-ARAP. A higher MELR and lower mARAP indicate better preservation of mesh rigidity.
By varying the parameter 3 of the Inter-Step Smoothness term in our progressive SR-ARAP formulation (Eq.9), we evaluate how effectively the deformation preserves original geometry. To quantitatively assess these characteristics, we define two rigidity metries: the Mean Edge Length Ratio (MELR) and the Mean ARAP Error (mARAPEreo (see SectionA.1 for detailed definitions). The MELR measures the average ratio of edge lengths in the deformed mesh relative their original lengths, indicating how well the deformation preserves the original geometry. The ARAP Error measures a simplified residual per triangle, reflect- ing how closely local rotations match between the original and deformed meshes. We measure these metries for 3 values ranging from 0.2 1.0 to observe their effects on deformation quality. As shown in Table 4, 3 0.8 effec- tively balances smooth transitions and rigidity preservation, yielding stable deformations with minimal distortions.
MELR(1)
mARAP Error (4)
0.2
0.87
17.24
0.4
0.88
14.91
0.6
0.92
12.56
0.8
0.94
10.12
1.0
0.93
11.60
respectively, for k E 1,, (Eq. 13), We first compute their centroids, c and é, as follows:
pk. Pk for k: E 10,
(13)
Po Pi
P2
po + PI
D2
(14)
3
3
Then, we form local point sets centered by these centroids:
A.1. Rigidity Metrics
To quantify how closely a deformed mesh M FV preserves the original geometry M = (V, F), we compute two metrics specifically for the vertices optimized through the energy function (i.e., movable vertices), as constraint vertices remain fixed during deformation.
Po c
Pg
P
P1 c
p
p1
(15)
P2 - c
Next, the optimal rotation R aligning P to P is obtained via the SVD-based Kabsch algorithm. Using this rotation, the ARAP residual for each face A is calculated as:
Mean Edge Length Ratio (MELR). Let {(i,j)} be the set of unique edges connecting movable vertices derived from the triangular faces in M. For each such edge (i,j), we measure the ratio of its length in the deformed mesh to its length in the original mesh, as defined in Eq. 11.
2 EIr = Σ |R(pk - c) (0x E)]
(16)
We then compute the overall ARAP Error by summing resid- uals across all faces and normalize it by the total number of faces |F to obtain the Mean ARAP Error:
0ū - 0
Ration
U1 0/
(11)
where U; Ug E Vovaable, 0, bg E V movablee
mARAP Error
Erra.
(17)
F
LEF
We then compute the mean edge length ratio (MELR), across all movable edges, as shown in Eq. 12. A MELR value closer to 1.0 indicates minimal distortion of edge lengths.
A lower mARAP Error indicates better preservation of lo- cal rigidity in the deformation, enabling fair comparisons regardless of mesh size or face count.
MELR
Rationj
(12)
B. 3D Mesh Generation Based on Depth Approach
Emovabbe|
Ef.j)
The
3D mesh generation discussed in Sec- tion 4.1 is formalized as the algorithm shown in Algorithm I. We set the depth threshold (Ta) to 0.1 and defined the back- ground threshold (T₂) as the mean depth value plus 0.3. This approach effectively removes background noise.
ARAP Error. To measure local distortion between orig- inal and deformed meshes, we compute a best-fit rotation matrix R for each face = (io, i1, 12) E F. Let the vertex coordinates of the original and deformed face be Pk and Pr.
13

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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Algorithm Depth Map and Mesh Generation
Input Image
Image
Image
User Aroow
input D: Depth map (normalized to [0, ID, Tá: Depth
threshold, 7y: Background threshold
output M F), M: Generated 31D mesh, V: Set of
vertex. F: Set of facets
Step 1: Depth Map Extraction
D < Marigold(Z): Extract depth map using Marigold
Step 2: Vertex Mapping
For each pixel (x, v) E "D do
2 4- ((x; y): Map depth value to z-coordinate
Add vertex (zz y, 2) M
Step 3: Facet Formation
For each vertex V Viz, z) E M do
if [D(z, y) - Daadjacent] < Ta
Connect V to adjacent vertices, add facet to F
Step 4: Artifact Reduction
For each facet (ve, Uy, vx) E F
if [P(x.) D(u,) Ta or ((x))= Tb
Remove (U, 22,24) E M
Return: M (V, F): Return the final 3D mesh
C. VFD Dataset
Figure I0. Visualization of the labeling procedure in the VFD Dataset. We first blend the input and ground-truth (GT) images to identify structural differences. Then, user-defined arrows are drawn, indicating precise handle points (arrow tails) and corre- sponding target points (arrowheads).
C.1. Dataset Categories
We constructed the VFD Dataset by selecting pairs of video frames from various sources, including DAVIS (Pont-Tuset et al., 2017), LOVEU-TGVE (Wu et al., 2023a), TVR (Lei et al., 2020), and copyright-free clips available on Pexels. Each pair captures significant structural or pose changes suitable for drag-based editing. The dataset comprises a total of 250 image pairs categorized as follows:
D. Sensitivity Analysis
We perform sensitivity analysis to evaluate the robustness of mesh deformation and its subsequent effects on drag-based editing results. Specifically, we examine how variations in mesh construction parameters inffuence the editing quality for both DepthMesh and DiffMesh methods (Fig. 15). First, we analyze DepthMesh by varying its reduction ratio, which determines the density of facet connections during mesh construction (Supplementary Algorithm 1, Step 3). A reduction ratio of corresponds to a fully detailed mesh, while lower values progressively simplify geometry. As illustrated in 15g.) we quantify the editing robustness using the PSNR ratio and I-LPIPS ratio, comparing results to the baseline (reduction ratio 1). Our results show that the editing performance remains robust and stable within the reduction ratio range from down to 0.001, indicating that our method effectively preserves essential geometric in- formation even under significant mesh simplification. Qual- itative results (5b)) visually confirm that drag-based editing remains accurate and stable across these reduction ratios, while excessively simplified meshes (reduction ratio = 0.0001) yield distorted and unsatisfactory results due to insufficient geometric information.
Animal (140 images). Captures diverse animal move- ments and expressions, including general motion changes, head movements, and mouth opening or closing.
Human (65 images). Includes human-related changes cat- egorized into facial expressions, head rotations, and overall pose adjustments involving face, hand, and arm movements.
Object (45 images). Consists of various object move- ments, including household items (e.g., coffee cups, kettles), food-related actions (e.g., pizza movement, tomato slicing), and miscellaneous objects (e.g., trains, clocks).
C.2. Labeling Procedure
To accurately define user arrows between input and ground- truth (GT) images extracted from videos, we first blend the two images and then draw user-defined arrows indicating the desired handle and target points (Fig. 10). Using this la- beling method, we produce precise annotations, facilitating reliable evaluation of drag-based editing performance.
Next, we examine DiffMesh sensitivity by adjusting the sampling steps in the image-to-31 diffusion process (Hun-
14

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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
[1] Before deformation landle Rohm Edtt Mart
(2) After deformation
(3) Visualize Vector Flow
Torget Plant
(a) 3D mesh with background
Vertices
262,144
16,607
4,445
Depth Mop
Faces
522,242
30,752
7,687
Deformation
Generation
Time
[1] Before deformation (2) After deformation
(3) Visualize Vector Flow
background
Figure 11. Mept deformation results when generating the mesh from the depth map with and without background consideration. (a) Result with background. (b) Result without background.
Figure 12. Deformation time and results based the number of vertices and faces in the 3D Mesh. As vertices and faces decrease, deformation time also decreases, but the ability capture detailed mesh changes is reduced.
E.2. 2. Deformation Time Relative to Face Count
yuan3D 2.0 (Zhao al., 2025)). As demonstrated in 11g)) editing quality is robust across sampling steps ranging from 10 to 40, ensuring consistent geometric guid- ance. Qualitative evaluation (Fig.15(d)) further verifies that our method maintains stable and accurate editing perfor- mance for sampling steps of 10 or higher. However, fewer sampling steps (e.g., step = 5) degrade the mesh geometry, resulting in noticeable quality deterioration drag editing outcomes.
Mesh deformation is critical step in FlowDrag, as it gener- ates the 2D vector flow field required for input. To assess the time required for mesh deformation, we measured the deformation time while adjusting the number of faces the mesh. Fig. 12, the sample on the far left represents mesh generated directly from the depth map of the image, with deformation time of 5.27 seconds. As move to the right, the number of faces in the mesh decreases, and we observe corresponding reduction in deformation time. However, reducing the number of faces also diminishes the mesh't ability to capture fine deformations accurately. Therefore, to better preserve the fidelity of the mesh transformations, we opted not reduce the face count during deformation. On average, this approach resulted in processing time of 5 seconds per sample.
These analyses collectively confirm that our method demon- strates robustness and stability in drag-based editing across wide range of mesh deformation parameters.
E. Analysis of Mesh Deformation Efficiency
E.1. 1. Impact of Background Separation on Mesh Deformation Quality
F. Additional Qualitative Comparisons
Unlike DiffMesh, which inherently isolates foreground ob- jects due to the diffusion generation process, the quality of DepthMesh deformation can significantly vary depend- ing whether the background is included. To analyze this effect, we performed mesh deformation experiments both with and without background separation. The results are presented in Fig. 11. As shown Fig. (a). when the background is included in the mesh the deformation appears constrained and unnatural due the influence of the background. In contrast, Fig. (b)b) shows that when the background is removed, the mesh deforms much more freely and naturally. Based on these observations, we de- cided to focus on foreground meshes only, excluding the background, when generating the mesh for deformation.
Additional qualitative results on DragBench and VFD- Bench are presented in Fig. and Fig. 14, respectively, FlowDrag consistently preserves spatial coherence and pro- duces more stable editing outcomes compared to existing drag-based methods, demonstrating the benefits of leverag- ing structured 3D spatial context.
15

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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
User Edit
DiffEditor
FreeDrag
GoodDrag
FlowDrag (ours)
12
2
9
8
Figure Additional Qualitative Comparisons on DragBench.
16

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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
User Edit
FreeDrag
GoodDrag
FlowDrag (ours)
GT
Figure 14, Additional Qualitative Comparisons on VFD-Bench.
17

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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Sensitivtty analysis of DepthMesh respect varying reduction ratios.
(c Sensitivity analyss DfffMeshh respect varying sampling steps.
Depthiftshh
Conevttion
Commitin
Redaction
0.0003
Sampling
Verlees
200,
4,885
292
19
Verkees
10,992
19,941
15,952
15,12
$22,342
7,887
72
20,354
23,383
31,564
Mach
Medh
Deformation
Wester
Weitor
Flow
(b) Visulizattion of mesh deformation and editing results usng Depthiiehh with different reduction
[d] Visuaizaaiion mesh deformation and editing results using Drffitesh with different sampling steps.
Figure 15. Sensitivity analysis and qualitative comparison of mesh deformation and drag-editing results. (a) We evaluate the robustness DepthMesh under different reduction ratios, which control the density facet connections during mesh construction (see Algorithm 1). A ratio of denotes fully connected mesh, while lower values lead degraded geometry. Since no ground-truth exists for DragBench dataset, we compute PSNR ratio and 1-LPIPS ratio using the result at reduction ratio = as reference. The editing remains robust within the reduction ratio range of 0.001 1. (b) Qualitative results confirm that method maintains stable and accurate editing across the reduction ratio range 0.001 to 1. (c) We assess the robustness of DiffMesh by varying the sampling step in the image-to 3D-3D diffusion model (Hunyuan3D 2.0 (Zhao et al., 2025)). Editing quality remains stable for sampling steps of 10 to 40, (d) Visual results show that our method remains robust for sampling steps of 10 or higher.
18

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FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Editing
Mesh
User Edit
DiffMesh
2D vector flow
Results
Constraints
Deformation
Handle Points
Handle Vertex
Movable Vertices
Deformation Vector
Target Points
Target Vertex
Constraint Vertices
Figure 16. More Visualization of the mesh-guided editing process using DiffMesh.
19


Reviews:
[{"review_id": "v5P35teKhf", "reviewer": "Reviewer_cw4s", "summary": "This paper proposes FlowDrag, which focuses on improving geometry consistency of drag-based image editing. It reconstructs a 3D mesh from the image, and uses an energy function to guide mesh deformation. The deformed mesh is then projected into 2D and used to guide the image editing denoising process. This paper also proposes a new benchmark dataset, VidFrameDrag (VFD), as the first drag-editing benchmark that has ground truths using consecutive view shots. \nExperiments are conducted on both the proposed VFD benchmark and an existing DragBench and validate the effectiveness of the proposed method, evaluated by MD and 1-LPIPS as metrics and a user study.\n\n## update after rebuttal\nThe rebuttal has addressed most of my concerns. I believe that maintaining my original positive rating appropriately reflects my overall favorable impression of the work.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[Q1] The method facilitates overall geometric consistency, but edited images still show some fine-grained inconsistencies, e.g., the hat shape in the first sample of Fig.6.**\n\n**[A1]** Yes, we agree with the reviewer’s observation. While FlowDrag significantly improves overall geometric consistency, some fine-grained geometric inconsistencies remain (e.g., the hat shape in Fig. 6). We speculate that this issue primarily arises due to the inherent limitation of the pre-trained 2D Stable Diffusion model (version 1.5), specifically its insufficient 3D understanding. This limitation is commonly observed across diffusion-based drag editing methods. Nonetheless, FlowDrag mitigates overall geometric inconsistency and shows enhanced robustness compared to existing methods (please refer to detailed comparisons in Tables 1 and 2, as well as Figures 6 and 9).\nTo further illustrate this strength, we provide additional visualization results in Fig. 10 (additional comparisons on VFD-Bench and Drag100 datasets, including extra examples from Prompt-to-Prompt) and detailed visualizations of our mesh-guided editing process in Fig. 11.\nAdditionally, we believe that utilizing backbones inherently capable of stronger geometric reasoning (e.g., video diffusion models) could potentially address and further reduce such fine-grained inconsistencies. We hope our work and results inspires future research in this promising direction.\n\nFig 10. https://anonymous.4open.science/r/FlowDrag-B950/Fig_10.pdf\n\nFig 11. https://anonymous.4open.science/r/FlowDrag-B950/Fig_11.pdf\n\n\n\n\n**[Q2] How important and robust is the 3D mesh reconstruction? Additional ablations or insights would help clarify this.**\n\n**[A2]** To analyze the importance and robustness of the 3D mesh reconstruction, we conducted additional sensitivity analyses for both DepthMesh and DiffMesh, as shown in Fig. 13 (please refer to the link provided below).\nFor DepthMesh (Fig. 13(a)-(b)), robustness is evaluated by varying the reduction ratio, which directly controls the density of facet connections during mesh construction (as detailed in Supplementary Algorithm 1, Step 3). Specifically, a ratio of 1 indicates a fully connected mesh, while lower ratios substantially reduce vertices and facets, causing geometry degradation and unintended outcomes in mesh deformation and subsequent drag editing. We performed experiments on 20 images from DragBench. Since DragBench lacks ground-truth edited images, we quantified robustness by computing the ratios of metrics relative to the highest-quality mesh (reduction ratio = 1, used as reference image), defined explicitly as follows:\n\n$$ \\text{PSNR ratio} = \\frac{\\text{PSNR (source image)}}{\\text{PSNR (reference image)}} $$\n\n$$ \\text{1–LPIPS ratio} = \\frac{\\text{1–LPIPS (source image)}}{\\text{1–LPIPS (reference image)}} $$\n\nAs shown in Fig. 13(a)-(b), FlowDrag demonstrates stable and robust editing outcomes within an effective reduction ratio range (0.001–1).\nFor DiffMesh (Fig. 13(c)-(d)), we similarly assessed robustness by varying the diffusion sampling steps (40, 20, 10, and 5) in the image-to-3D mesh generation process (using Hunyuan3D 2.0). Evaluations on the same 20 DragBench images with identical metrics revealed that sampling steps between 10 and 40 consistently maintained overall object geometry. However, at sampling steps below 10 (e.g., step=5), geometry degraded significantly, causing unintended deformation and editing results. Nevertheless, FlowDrag showed strong robustness for sampling steps of 10 or higher (Fig. 13(c)-(d)).\nThese detailed analyses confirm FlowDrag’s robustness across various mesh reconstruction conditions, validating our method’s effectiveness.\n\nFig 13. https://anonymous.4open.science/r/FlowDrag-B950/Fig_13.pdf", "claims_and_evidence": "- The motivation is reasonable and straightforward. To preserve geometric consistency, the paper adds a 3D mesh as an intermediate representation for 2D editing to inject 3D geometric prior. \n- It is also great to clarify that the model specifically tackles the kind of \"rigid edit,\" which only contains rigid transformations.", "methods_and_evaluation_criteria": "- [Pipeline]: The pipeline is carefully designed. The input image is used to generate a 3D mesh leveraging off-the-shelf tools. The drag modification is done in the 3D space by offsetting the mesh vertices using an energy-function based method ARAP (and its followup SR ARAP). A progressive process is carefully crafted for better deformation. The deformed mesh vertices are then sampled using two candidate strategies, and used to extract a 2D vector flow map for motion supervision and point tracking, as well as used to obtain a 2D projection and inject the Unet layout features to guide the spatial and geometry information.\n- [VFD-Bench]: The introduced VFD-Bench provides ground truths by leveraging video frames. This effectively provides fair evaluation capabilities and could have a positive impact on the field.", "theoretical_claims": "NA", "experimental_designs_or_analyses": "- The experiment results show that the proposed method outperforms existing methods, including DiffEditor, DragDiffusion, DragNoise, FreeDrag, and GoodDrag, on the VFD-Bench, evaluated by 1-LPIPS and MD. \n- On the DragBench dataset, it has slightly lower 1-LPIPS values than the bests, while the paper reasonably argues it is because the other methods induce minimal edits. \n- User study is also conducted to validate the method’s superior performance.", "relation_to_broader_scientific_literature": "- This paper focuses on improving the challenging geometric consistency issue in existing dragging-based image editing works, showing an interesting and promising improvement on a specific kind of dragging editing that focuses on rigid transformations.\n- The paper proposes VFD-Bench as an addition to existing dragging-based editing benchmark, e.g., DragBench. This VFD benchmark provides ground truths by leveraging video frames. It effectively provides fair evaluation capabilities and could have a positive impact on the field.", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "- [Fine-grained geometry inconsistency]: The method facilitates overall geometric and spatial consistency; while the edited images still showcase some inconsistency in the fine-grained geometry. For example, the hat's shape in the first sample of Fig.6.", "other_comments_or_suggestions": "NA", "questions_for_authors": "- [3D mesh quality] How important is the quality of the 3D mesh reconstruction? What will happen if the 3D mesh fails to represent the image? How often will it succeed in generation, and is it robust? More ablation and insights on this would help clarify.", "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": "vJUbX1ivEF", "reviewer": "Reviewer_JQSi", "summary": "This paper proposes a novel drag-based editing framework called FlowDrag. Its key feature is the introduction of control points generated through the deformation of a 3D mesh, which helps to mitigate the geometric discontinuities commonly present in existing drag-based editing methods. Judging from the results provided in the paper, this method is quite effective and shows a noticeable improvement over current approaches.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[Q1] The authors do not discuss the impact of 3D mesh construction. Could failures or severe artifacts in mesh reconstruction cause image editing to fail?**\n\n**[A1]** Yes, severe artifacts or failures in 3D mesh reconstruction could hinder the accurate generation of an accurate 2D vector flow, potentially causing editing failures. To address this concern, we evaluated the robustness of FlowDrag across various mesh reconstruction conditions, as detailed in our response to Reviewer 1 (A2) and illustrated in Fig. 13 (please refer to the link provided below). Due to the space limitations, we respectfully ask the reviewer to refer to these detailed analyses.\nBriefly, we analyzed two mesh-generation approaches used in FlowDrag: DepthMesh and DiffMesh. For DepthMesh, varying the reduction ratio (controlling mesh density during construction) can degrade the original geometry if reduced excessively. For DiffMesh, changing the diffusion sampling step of the image-to-3D diffusion model (Hunyuan3D 2.0) can introduce artifacts and geometry degradation when sampling steps become very short. Our comprehensive experiments identified robust operating ranges: DepthMesh remains robust within a reduction ratio range of approximately 0.001–1, and DiffMesh maintains robustness for diffusion sampling steps of 10 or higher.\nThese analyses demonstrate FlowDrag's robustness, confirming its capability to consistently produce stable and accurate editing outcomes, even under varying conditions of mesh reconstruction quality.\n\nFig 13. https://anonymous.4open.science/r/FlowDrag-B950/Fig_13.pdf\n\n\n**[Q2] Regarding the selection of control points, as seen in Figure 3, the geometry of the edited dog's head is inconsistent after mesh editing. If control points are chosen in a regular manner, could this lead to bad results?**\n\n**[A2]** As shown in Fig 3, minor geometric inconsistencies may arise from the mesh deformation itself, since the deformed mesh does not perfectly preserve fine-grained rigidity. However, we do not directly use this mesh for editing. Instead, we project the deformed mesh onto a 2D vector field and select optimal control points (referred to as \"2D vector flow\") from this projection. Furthermore, we conducted the experiment on selecting control points in a regular manner corresponding to the \"Uniform sub-sampling\" approach described in our paper (Sec 4.3). Our experiments show that this approach still effectively maintains overall geometric consistency and outperforms many existing methods. (We guess this effectiveness is attributed to our multiple drag vector concept.) Additionally, we proposed \"Magnitude-based sampling\", which selects more effective vectors with the largest displacements, achieves optimal editing results, as quantitatively demonstrated in Table 5.\n\n\n**[Q3] The paper presents limited qualitative visualization results.**\n\n**[A3]** We provide additional qualitative visualization results in Fig. 10 (additional results on VFD-Bench and Drag100), Fig. 11 (visualization of the mesh-guided editing process using DiffMesh), Fig. 12 (comparison of mesh deformation pipelines using DepthMesh and DiffMesh), Fig. 13 (sensitivity analysis and robustness comparison of mesh deformation), and Fig. 14 (additional qualitative comparisons of drag-based editing results). We will also incorporate these results in the final manuscript.\n\nFig 10. https://anonymous.4open.science/r/FlowDrag-B950/Fig_10.pdf\n\nFig 11. https://anonymous.4open.science/r/FlowDrag-B950/Fig_11.pdf\n\nFig 12. https://anonymous.4open.science/r/FlowDrag-B950/Fig_12.pdf\n\nFig 13. https://anonymous.4open.science/r/FlowDrag-B950/Fig_13.pdf\n\nFig 14. https://anonymous.4open.science/r/FlowDrag-B950/Fig_14.pdf", "claims_and_evidence": "The authors introduced a dedicated dataset for testing called VFD-Bench, which provides a more comprehensive quantitative analysis than existing methods. However, the paper presents a limited number of visualization results, and a qualitative analysis should include more visual effects.", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "The paper improves the SR ARAP, and provides formula derivations in the supplementary material. I have reviewed them and found no significant issues.", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "Image editing", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "The paper introduces a 3D Mesh model to determine the control points needed for editing, which is innovative。The quantitative and qualitative analysis results provided demonstrate the feasibility and effectiveness of the approach. \nWeaknesses:\nHowever, the authors do not discuss the impact of the 3D Mesh model on the results. It is well known that algorithms for constructing mesh models from a single image are still immature. If mesh construction fails or there are serious artifacts, could this lead to image editing failure? At the same time, regarding the selection of control points, as can be seen from Figure 3, the geometry of the edited dog's head is not consistent after mesh editing. If control points are chosen in a regular manner, could this lead to bad results?", "other_comments_or_suggestions": "Refer to Weaknesses", "questions_for_authors": "The paper should provide more visual editing results to further demonstrate the effectiveness of the proposed method. This would help to substantiate the claims made in the paper and offer a more comprehensive understanding of the technique's capabilities and potential applications.\nMy view is that this paper is meaningful in improving existing drag-based image editing methods from a 3D perspective, but the proof of visual effects should be strengthened. Therefore, I have recommended a \"weak accept.\"", "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": "hqVmGqFH30", "reviewer": "Reviewer_GpMK", "summary": "This paper proposes FlowDrag, a method that leverages pre-trained stable diffusion models for drag-based image editing. This method improves the drag-based image editing by building a field of 3D-aware dragging instruction from the user's input. Specifically, FlowDrag first leverages an image-to-depth or image-to-mesh model to generate a mesh for the foreground object. It then applies SR-ARAP, a mesh deformation algorithm, to calculate how the user's drag deformed the object. Through the novel progressive deformation with SR-ARAP, it obtains a flow field showing how a larger area of the object would move. This flow field is then sampled and projected to 2D space to act like the densified dragging instruction. By providing a field of 3D-aware dragging instructions, the diffusion model receives more guidance on how each pixel is going to move and yields a better editing result with the standard motion supervision and point tracking pipeline. Moreover, FlowDrag further uses the projected deformed mesh as a guide to improve the editing result. It also constructs a new drag-based image editing benchmark, VFD-Bench Dataset, with ground-truth editing results to compensate for the fact that the existing benchmark, DragBench, does not provide ground-truth images. FlowDrag is evaluated on both DragBench and VFD-Bench datasets and shows superior performance than previous methods.\n\n## Update after rebuttal\nI appreciate the author's response and insightful discussion. I would keep my original rating.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[Q1] What is the value of $\\lambda$ in Equation 8?**\n\n**[A1]** The parameter $\\lambda$ in Eq. (8) represents the incremental step size at each iteration, indicating the fraction of the displacement between the handle vertex ($v_h$) and the target vertex ($v_t$). However, we discovered a minor typo in the original version of Eq. (8), and we sincerely appreciate the reviewer’s careful observation. The corrected Eq. (8) is:\n\n$ v_h^{(k+1)} = v_h^{(k)} + \\lambda (v_t - v_h), \\quad 0 < \\lambda \\le 1 $\n\nIn this equation, both $v_h^{(k+1)}$ and $v_h^{(k)}$ are intermediate vertices positioned incrementally between the initial handle vertex ($v_h$) and the target vertex ($v_t$).\nTo facilitate a clearer understanding, we provide an enhanced illustration in Fig. 15 (please refer to the link provided below), complementing Fig. 3 in the paper.\nThis formulation implies that instead of moving the handle vertex directly from $v_h$ to $v_t$ in one step, our method progressively moves it through multiple intermediate positions. Specifically, we set $\\lambda=0.2$ in our implementation, meaning the vertex moves 20% closer to its target at each iteration. For example, with $\\lambda=0.2$, the handle vertex path would be as follows:\n\n$v_h (\\text{handle vertex}) = v_h^{0} → v_h^{1} → v_h^{2} → v_h^{3} → v_h^{4} → v_h^{5} = v_t (\\text{target vertex})$\n\nThis progressive SR-ARAP algorithm thus prevents abrupt mesh distortions by smoothly distributing large vertex displacements across multiple intermediate steps, achieving more stable and coherent deformations.\nWe will clearly correct Eq. (8) accordingly in the revised manuscript. We sincerely thank the reviewer for this valuable clarification.\n\n\nFig 15. https://anonymous.4open.science/r/FlowDrag-B950/Fig_15.pdf\n\n\n**[Q2] I am also confused with the handle-matching term in Equation 9.**\n\n**[A2]** The handle-matching term in Eq. (9) acts as a soft constraint, ensuring handle vertices smoothly approach their intended intermediate (and ultimately final) positions. Since the SR-ARAP algorithm primarily optimizes vertex positions by minimizing local rigidity (minimal local distortion), handle vertices may not exactly reach intermediate targets. To resolve this, the handle-matching term gently penalizes deviations from target positions:\n\n$\\beta \\sum_{v_h \\in \\text{handles}} \\Bigl\\|\\, v_h^{(k+1)} - v_t \\Bigr\\|^2$\n\nThis ensures balanced optimization between local rigidity and accurate vertex positioning. We will clarify this explicitly in the revised manuscript.\n\n**[Q3] $v_t$ and $v^{(k+1)}_{h}$ are fixed. What is the learnable in Eq (8)? Is it $\\lambda$?**\n\n**[A3]** Yes, $v_t$ and $v^{(k+1)}_{h}$ are both fixed. Additionally, as explained in A1-2, the parameter $\\lambda$ is a manually set hyperparameter (set as 0.2) and not learnable. Eq. (8) itself contains no learnable parameters. The learnable parameters are the positions of vertices (other than handle and target vertices), which are optimized through the SR-ARAP energy function with the handle-matching term in Eq. (9).\n\n**[Q4] The paper mentions using both DepthMesh and DiffMesh. Which one is preferred and why? Additionally, which mesh (DepthMesh or DiffMesh) is used to achieve the reported results in Tables 1, 2, and 3?**\n\n**[A4]** We provide additional experimental comparisons of drag-editing results using DepthMesh and DiffMesh in Fig. 12 (please refer to the link provided below).\nAs shown in Fig. 12(a), DepthMesh struggles to generate geometry for regions unseen in the single input image, resulting in unnatural mesh deformation. In contrast, DiffMesh (Fig. 12(b)), generated via a diffusion model, effectively captures complete geometry, enabling more natural and coherent mesh deformation. Therefore, we prefer DiffMesh due to its better geometric consistency, crucial for accurate mesh deformation using our SR-ARAP algorithm.\nAll reported results in Tables 1, 2, and 3 are based on DiffMesh. The detailed editing process using DiffMesh is illustrated in Fig. 11.\n\nFig 11. https://anonymous.4open.science/r/FlowDrag-B950/Fig_11.pdf\n\nFig 12. https://anonymous.4open.science/r/FlowDrag-B950/Fig_12.pdf\n\n\n**[Q5] Section C of the supplementary mentions background separation. How is it achieved?**\n\n**[A5]** Background separation is performed during the DepthMesh generation by applying a background threshold ($τ_b$) in Step 4 of Algorithm 1 (Supplementary, Section B). Specifically, any mesh facets with depth values smaller than $τ_b$ are removed. For example, Fig. 7(a) in Section C illustrates a result without background separation ($τ_b=0$), whereas Fig. 7(b), with $τ_b=0.3$, effectively removes background facets.\n\n**[Q6] What are the hardware requirement for FlowDrag?**\n\n**[A6]** FlowDrag requires less than 14GB of GPU memory for processing a 512×512 input image, as evaluated on a single NVIDIA A100 GPU. We will include detailed hardware requirements in the final manuscript.", "claims_and_evidence": "There are two main contributions claimed by the paper: the densification of dragging instruction through mesh deformation and VFD-Bench datasets. Both of them are well substantiated by the experiment results. The effectiveness of FlowDrag is demonstrated by the results in Table 1, Table 2, and Table 3. Each component in the method is studied in the ablation study. The VFD-Bench dataset provides ground-truth images for drag-based image editing, which is exactly what the community needs, as it provides an accurate and objective way to evaluate the result of image editing. Therefore, the claims in this paper are very solid.", "methods_and_evaluation_criteria": "The proposed method is a novel and clear way to improve the drag-based image editing using diffusion models. It realized that a good editing result should comply with local 3D rigidity constraints. However, the user's input is sparse and existing methods rely on the inference ability of the diffusion models to hallucinate how the object may change. This could be inaccurate as the diffusion models are trained on pure 2D data so it has no idea how the object should deform in 3D. This paper chooses an intuitive and explicit way to achieve 3D-aware editing: lifting the object from 2D to 3D, simulating the change in 3D caused by the drag, and reflecting these changes in 2D in the form of a flow field. This design targets the main problem very well, and the design is intuitive and logical. \n\nThe paper also proposes a new dataset VFD-Benchmark, a drag-based image editing benchmark with ground-truth editing result. The existing benchmark, DragBench, only provides images and instructions but no ground-truth editing results. It relies on Mean Distance (MD) and 1-LPIPS to evaluate the editing results. However, these two metrics cannot accurately reflect the editing effect: MD only compares DIFT feature similarity between source and target keypoints, and 1-LPIPS ignores object's deformations before and after editing. VFD-Benchmark provides ground-truth editing images. This allows the editing to be accurately and objectively evaluated by comparing it with the ground-truth image. The dataset offers a good solution to an existing issue in the evaluation. \n\nTherefore, the paper provides solid solutions to both the editing pipeline and evaluation, making significant contributions to this field.", "theoretical_claims": "This paper is an application paper with no significant theoretical contribution.", "experimental_designs_or_analyses": "The experiments are thorough and detailed, with no significant issue. Given that the flow field generation is independent of a specific editing method, a potential improvement in the experiment could be applying the flow field to a range of drag-based editing methods, such as DragDiffusion, GoodDrag, etc, to see whether the flow field may improve existing methods. If it could, the significance of this work would be much greater.", "relation_to_broader_scientific_literature": "The paper is built on the existing drag-based image editing pipeline. It follows motion supervision, point tracking and latent representation optimization proposed by DragDiffusion. The evaluation metrics, Mean Distance and 1-LPIPS, are commonly used by the literature.", "essential_references_not_discussed": "All related works have been discussed.", "other_strengths_and_weaknesses": "Some place requires further clarifications, which I would elaborate in the Question section. These questions do not affect the contribution and significance of this work.", "other_comments_or_suggestions": "The proposed method is solid and intuitive, and the paper is well-written and easy to follow. It makes concrete contributions to the field. Therefore, I recommend the acceptance of this paper.", "questions_for_authors": "I would appreciate it if the author could address the following questions:\n\n1. Regarding the Progressive Deformation with SR-ARAP, what is the value of $\\lambda$ in Equation 8? I am also confused with the handle-matching term in Equation 9. $v_t$ is fixed because it is the target point, $v^{(k+1)}_{h}$ is calculated based on Equation 8. Therefore, both terms seem fixed. What is the learnable in here? Is it $\\lambda$?\n\n2. The paper mentioned that both DepthMesh and DiffMesh are used, yet there is no experiment or discussion on which one is preferred. Could you provide more information on this matter? Which one is used to achieve the reported result in Table 1, 2 and 3?\n\n3. Section C of the supplementary mentions background separation. How is it achieved?\n\n4. What are the hardware requirement for FlowDrag?", "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": "GUorDNOId0", "reviewer": "Reviewer_bFUV", "summary": "This paper proposes a novel method for drag-based image editing. Compared with the previous work, the proposed method takes the 3D geometric information into consideration through mesh construction, ensuring a stable and 3D-plausible editing. The method is claimed to achieve state-of-the-art performance.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[Q1] The paper lacks comparison with DragGAN, which achieves better performance in certain cases.**\n\n**[A1]** We provide additional qualitative comparisons between FlowDrag and DragGAN in Fig. 14(a) (please refer to the link below). To ensure fair comparisons, we reproduced DragGAN using the official GitHub repository and employed PTI [1] for GAN inversion. Since the provided DragGAN weights are limited to the StyleGAN-Human model, our comparisons focus exclusively on human images. As shown in Fig. 14(a), DragGAN struggles to preserve the original person's identity, whereas FlowDrag maintains identity effectively and exhibits better geometric consistency.\n\nFig 14. https://anonymous.4open.science/r/FlowDrag-B950/Fig_14.pdf\n\n[1] Pivotal Tuning for Latent-based Editing of Real Images (ACM TOG 2022)\n\n**[Q2] I would like to see additional results from the \"content removal\" or \"content creation\" categories of the Drag100 dataset.**\n\n**[A2]** We applied FlowDrag to the \"content creation\" and \"content removal\" examples from the Drag100 dataset, and present additional results in Fig. 14(b)-(c). In the \"content creation\" category (Fig. 14(b)), FlowDrag successfully generates natural results with fewer artifacts compared to GoodDrag. Similarly, in the \"content removal\" category (Fig. 14(c)), FlowDrag demonstrates editing quality that is comparable or superior to GoodDrag.\nAlthough FlowDrag primarily targets \"rigid edits\" to preserve object rigidity during editing (as stated in our introduction), these results confirm that our method is also effective in handling non-rigid editing scenarios.\n\n**[Q3] The two orange arrows in Fig.4 overlap with text, making the figure quite messy.**\n\n**[A3]** Thank you for pointing this out. We will adjust the positioning of the orange arrows in Fig.4 to avoid overlap and improve visual clarity in the final manuscript.\n\n\n**[Q4] There is no link in citations and references to figures and tables.**\n\n**[A4]** We will include hyperlinks in citations and references to figures and tables in the final manuscript to enhance readability.\n\n\n**[Q5] The axis metrics in Fig.2 (d) and (e) are inconsistent.**\n\n**[A5]** Yes, the axis metrics in Fig. 2(d) and Fig. 2(e) intentionally differ. Fig. 2(d) compares methods on the DragBench dataset using metrics such as 1-LPIPS (for image fidelity) and MD (Mean Distance, for evaluating handle-point movement accuracy). In contrast, Fig. 2(e) shows comparisons on our proposed VFD-Bench dataset, which provides ground-truth edited images from video frames, enabling evaluation using RGB-level (PSNR) and feature-space metrics (1-LPIPS and MD). Fig. 2(e) specifically plots results using PSNR and 1-LPIPS. We will explicitly clarify this distinction in the final manuscript.", "claims_and_evidence": "- The method is not comparing with the very first work of this task: DragGAN. Even though the proposed method is diffusion-guided, DragGAN is still observed achieving better performance in certain cases. This hurts the soundness of this paper. \n    - As a comparison: GoodDrag compares with DragGAN.\n- The paper shows some high-quality qualitative results. This can demonstrate the claim of state-of-the-art performance.\n- However, this paper is not using Drag100 benchmark, which is created in the most powerful baseline GoodDrag, and contains various types of editing as shown in GoodDrag's Fig.6 (editing involves content removal and creation). This raises lots of concerns about whether the proposed method can also achieve this.", "methods_and_evaluation_criteria": "- The method is reasonable and well-motivated. I am especially impressed by the progressive deformation part.\n- As mentioned in \"Claims And Evidence\", Drag100 benchmark proposed in the paper of the most powerful baseline GoodDrag is not used for experiments. This raises concerns about whether the proposed method can support editing involving content removal and creation.\n    - In fact, from the method, I think the proposed method cannot well support content creation as it will be hard to generate the new contents in 3D. I would like the author to show some results to disprove this.", "theoretical_claims": "N/A.", "experimental_designs_or_analyses": "-  As mentioned in \"Claims And Evidence\"\n    - The method is not comparing with the very first work of this task: DragGAN. Even though the proposed method is diffusion-guided, DragGAN is still observed achieving better performance in certain cases. This hurts the soundness of this paper. \n    - Drag100 benchmark proposed in the paper of the most powerful baseline GoodDrag is not used for experiments. This raises concerns about whether the proposed method can support editing involving content removal and creation.", "relation_to_broader_scientific_literature": "This paper proposes a novel approach to inject 3D awareness and basis to drag-based editing pipelines. This enables the requirement of 3D-plausibility in these editing tasks and augments the capability.", "essential_references_not_discussed": "-  As mentioned in \"Claims And Evidence\", the method is not comparing with the very first work of this task: DragGAN, just cited once.", "other_strengths_and_weaknesses": "- The two orange arrows in Fig.4 are overlapped with texts, which makes the figure quite messy.\n- There is no link in citations and references to figures and tables.\n- The axis metrics in Fig.2 (d) and (e) are inconsistent.", "other_comments_or_suggestions": "I would like to see some additional results (1) comparing with DragGAN, and (2) from the \"content removal\" or \"content creation\" categories of Drag100 dataset.\n\nAll these results are received in rebuttal, resolving most of my concerns. Therefore, I will raise my score from 3 to 4.", "questions_for_authors": "Please refer to the reviews above.", "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:

Meta-review: This paper presents a novel and well-motivated approach to improving geometric consistency in drag-based image editing by introducing a 3D mesh as an intermediate representation. The method effectively addresses the challenge of rigid transformations by leveraging off-the-shelf tools to generate 3D meshes, using energy-function-based deformation techniques (ARAP/SR ARAP) for progressive mesh editing, and translating 3D changes into 2D flow fields for motion supervision. The proposed VFD-Bench dataset addresses a critical gap in existing benchmarks by providing ground-truth editing results, enabling fair and objective evaluation of editing methods. The paper demonstrates promising improvements in geometric consistency and editing quality, supported by both quantitative and qualitative results.

The method is innovative in explicitly incorporating 3D geometric priors into diffusion-based editing, which helps overcome the limitations of relying solely on 2D data. The progressive deformation process and careful pipeline design further enhance the robustness and effectiveness of the approach. While the paper does not compare with DragGAN, a key baseline in this area, and may have limitations in supporting content creation or complex deformations, the contributions to improving geometric consistency and advancing evaluation methodologies are significant. The high-quality qualitative results and the introduction of VFD-Bench strengthen the paper's impact on the field. Overall, the paper provides a solid solution to a challenging problem and makes a meaningful contribution to drag-based image editing.
Decision: Accept (spotlight poster)