FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields

Chang D. Yoo; Gwanhyeong Koo; Ji Woo Hong; Sunjae Yoon; Younghwan Lee

REVIEW 4 major objections 6 minor 1 cited by

FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read FlowDrag reports that injecting a mesh-derived 2D vector flow field into diffusion denoising improves accuracy and geometric coherence of drag edits, outperforming existing methods on both VFD-Bench and DragBench.

desk verdict VFD-Bench is a genuinely useful benchmark idea, but the paper overclaims on DragBench and its VFD-Bench numbers are weakened by tuning on the same benchmark and an uncontrolled mesh-selection rule. read the letter →

arxiv 2507.08285 v1 pith:IAKWP7JZ submitted 2025-07-11 cs.GR cs.CV

Gwanhyeong Koo , Sunjae Yoon , Younghwan Lee , Ji Woo Hong , Chang D. Yoo This is my paper

classification cs.GRcs.CV

keywords drag-basedimageeditingdiffusionmodels3Dmeshdeformationas-rigid-as-possiblevectorflowfieldground-truthbenchmarkvideoframepairsgeometricconsistency

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Drag-based image editing lets a user drag handle points to targets, but existing methods match only those points and ignore the rest of the object, so rigid parts such as hats, torches, or limbs get distorted. FlowDrag's proposal is to give the edit an explicit geometric backbone: reconstruct a 3D mesh of the object, deform it with a progressive smoothed-rotation as-rigid-as-possible energy that moves the handle vertex toward the target while keeping local rotations consistent, project the per-vertex displacements back into a 2D vector flow field, and feed that field into the UNet denoising loop in place of point-only motion supervision. The paper also introduces VFD-Bench, a 250-pair benchmark built from consecutive video frames, so that for the first time drag edits have real ground-truth edited images rather than proxy fidelity scores. On that benchmark, and on DragBench, the paper reports that FlowDrag achieves better handle-to-target alignment and geometric fidelity than existing drag editors, with the largest advantages on edits that should preserve rigidity.

What carries the argument

The load-bearing object is the 2D vector flow field $\Phi = \{(\Delta x_i, \Delta y_i)\}$, the projection of per-vertex displacements between the original mesh $\mathcal{M}$ and the deformed mesh $\hat{\mathcal{M}}$ onto the image plane. It is produced by progressive SR-ARAP deformation, an extension of the as-rigid-as-possible energy that moves the handle vertex toward its target over $K$ steps, smooths rotations between adjacent vertices, and penalizes large position changes between steps. The field supplies the motion-supervision loss with a sparse set of sampled vectors, replacing the single handle-to-target direction with a shape-aware displacement map, while the deformed mesh projection is separately injected into the UNet's attention features at an early denoising timestep to provide global layout context.

What would settle it

On VFD-Bench, replace FlowDrag's mesh-derived vector field with a smooth interpolation of each handle-to-target displacement spread over the edit mask, keeping everything else identical; if the interpolated field matches FlowDrag's PSNR and Mean Distance within noise, then the SR-ARAP mesh deformation is not responsible for the reported gains, whereas if the mesh-derived field wins, the mechanism is confirmed.

Watch

Extended reading notes

Core claim

FlowDrag's central claim is that a continuous 2D vector flow field derived from a 3D mesh deformation is sufficient to make diffusion-based drag editing geometrically coherent. The method maps the user's handle point, target point, and edit mask onto a mesh, deforms the mesh with progressive SR-ARAP, and defines the flow field as the projected per-vertex displacements between original and deformed meshes. That field, rather than the single handle-to-target direction, is what drives motion supervision and point tracking, and the projected deformed mesh is additionally injected as an early-timestep layout feature. The paper's experiments report that this geometry-aware guidance improves alignment of handle points to targets and preserves object structure on both the new VFD-Bench, which has ground-truth edited frames, and the existing DragBench.

Load-bearing premise

The whole method rests on the assumption that the single-image depth map or image-to-3D reconstruction yields a mesh that is geometrically faithful to the object, because the vector flow field inherits every error in that mesh; the paper's uncontrolled rule of switching between DiffMesh and DepthMesh when one looks bad is never quantitatively validated.

Editorial extensions

If this is right

Edits that rotate, relocate, or change the pose of a rigid object become more stable, because the flow field carries shape context beyond the two clicked points.
VFD-Bench gives drag editing a ground-truth evaluation protocol, so future methods can be compared on actual edit accuracy rather than proximity to the unedited input.
The method is deliberately bounded: it suits moderate drags and rigid edits, and is not designed for large content creation or removal.
Sampling around ten flow vectors with the largest magnitudes gives the best fidelity, so the vector-selection step is as important as the mesh deformation itself.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Our inference: if the vector flow field is the real source of the gains, feeding the same field into gradient-guidance drag editors should also improve their geometric coherence; this is not tested in the paper.
Our inference: VFD-Bench's paired frames could measure the paper's mesh-faithfulness assumption directly, by correlating per-sample edit failures with reconstruction error between the generated mesh and the actual frame-to-frame object change.
Our inference: the optimal-vector-count result suggests the flow field helps most where displacement is concentrated, so a natural follow-up is to test whether the winning sampled vectors sit on occlusion boundaries or high-curvature regions.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things to know up front. VFD-Bench is a genuinely good idea: drag-editing evaluation has been stuck with no-reference metrics that penalize successful rigid edits, and building input-GT pairs from video frames directly addresses that. The second thing is that the paper's central outperformance claim is not yet supported by its own tables. On DragBench, FlowDrag lands at 0.82 1-LPIPS versus 0.89 for DiffEditor and 0.86 for GoodDrag, and the MD advantage over GoodDrag is 0.08—noise range without error bars. The VFD-Bench margins are larger, but they come from an author-created benchmark where beta, vector count, and the sampling strategy were all tuned on that same benchmark, with no fixed rule for choosing between DiffMesh and DepthMesh per image.

What is actually new: connecting SR-ARAP mesh deformation to the diffusion motion-supervision loop is a reasonable integration, and the qualitative figures do show better structural coherence on rigid edits like the statue and the face rotation. The idea of projecting a deformed mesh into a 2D vector flow and injecting those vectors into latent optimization is worth exploring. The ablation and sensitivity studies are more thorough than most drag-editing papers; the authors also say plainly that the method targets moderate rigid edits, not large structural changes.

The soft spots, in proportion. First, the mechanism is under-specified. Section 4.3 says the summation in Eq. 3 is restricted to the sampled flow vectors, but it never says the flow displacement replaces the user's delta_i. If the flow field only selects which patches get supervised, the geometry-aware direction is not actually entering the loss and the title mechanism is untested. That needs a clear sentence or an updated equation. Second, the per-image mesh choice is uncontrolled—"when DiffMesh exhibits significant artifacts" is a subjective criterion, and because the paper compares methods on the same images, this can only bias the results in FlowDrag's favor. Third, the evaluation uses VFD-Bench for both model selection and final reporting. Fig. 8 peaks at 10 vectors, and Table 3 picks magnitude-based sampling; those choices need a validation split before the numbers mean anything. There are no error bars or significance tests anywhere, and no code or data release.

The benchmark alone justifies a serious referee. The method is plausible, the limitations are stated honestly, and the evaluation framework is a step forward for the field. I would send this to review, but with a request for major revision: fixed mesh-selection protocol, held-out hyperparameter tuning, error bars, and a precise statement of how the flow field enters the loss. I'd engage with the work and probably cite VFD-Bench once it's released; I wouldn't take the performance tables as they stand.

Referee Report

4 major / 6 minor

Summary. FlowDrag is a drag-based image editing method that constructs a 3D mesh from an input image (either from a monocular depth estimator, DepthMesh, or from an image-to-3D diffusion model, DiffMesh), deforms this mesh using a progressive SR-ARAP energy, projects the per-vertex displacements into a 2D vector flow field, and injects this flow plus the deformed mesh's projected layout into the UNet denoising process of a motion-based drag-editing pipeline. The paper also introduces VFD-Bench, a new benchmark of 250 input/ground-truth pairs derived from consecutive video frames, with handle/target annotations. The central claim is that FlowDrag outperforms existing drag-editing methods (DiffEditor, DragDiffusion, DragNoise, FreeDrag, GoodDrag) on both VFD-Bench and DragBench while better preserving geometric consistency.

Significance. The high-level idea of using explicit 3D mesh deformation to produce a dense, geometry-aware displacement field for drag editing is promising and well motivated: existing motion-supervision methods only match user-specified points and ignore broader structure. The proposed VFD-Bench is also a constructive contribution, because DragBench has no ground truth and conventional fidelity metrics can penalize successful geometric edits. If the central outperformance claim were supported by a fixed, non-circular evaluation protocol, the paper would be a solid contribution to the drag-editing literature. However, as presented, the quantitative evidence is not currently reliable: the authors select several key hyperparameters and even the mesh-generation mode on VFD-Bench itself and then report final numbers on that same benchmark, with no error bars or significance tests. The mechanism by which the flow field enters the motion-supervision loss is also underspecified. These issues are load-bearing for the abstract's blanket claim of superiority, though they are potentially fixable with a clearer protocol and additional experiments.

major comments (4)

[Section 6.4, Table 3, Fig. 8] The paper selects hyperparameters by ablating on VFD-Bench and then reports final performance on VFD-Bench. Specifically, the Inter-Step Smoothness parameter β is chosen from Table 4, the vector count (10) is chosen from Fig. 8, and the magnitude-based sampling strategy is chosen from Table 3, all using VFD-Bench metrics. The final comparison in Table 2 is then reported on the same VFD-Bench set. This is test-set tuning: the reported gains (e.g., MD 28.23 vs. 35.31 for GoodDrag) may reflect choices fitted to this specific benchmark rather than genuine method superiority. To support the claimed outperformance, the authors must either pre-register all hyperparameters, use a disjoint validation split for ablations, or report VFD-Bench results under a fixed protocol, and they should include per-method error bars and significance tests.
[Section 6.1, Mesh Generation] The selection between DiffMesh and DepthMesh is made per image without a fixed, objective criterion: "When DiffMesh exhibits significant artifacts or deviates substantially from the original image, we employ DepthMesh instead." No threshold is defined, no ablation quantifies when each mesh is preferred, and no statistics are reported on how often each mesh type is used or on the sensitivity of the final numbers to this choice. As written, this is a post hoc selection rule that makes the evaluation unfalsifiable. The authors should specify a deterministic rule (for example, a quantitative fidelity threshold between the rendered DiffMesh and the input image) and demonstrate that the reported results are robust to this choice, or report results separately for DepthMesh and DiffMesh.
[Section 4.3, Eq. (3)] The description of vector-flow sampling does not specify whether the user direction δ_i in the motion-supervision loss is replaced by the flow displacement at the sampled point. The text says "We then restrict the summation in Eq.3 to q ∈ ˆΦ," but the mesh-derived vector (∆x_i, ∆y_i) from Eq. (10) never explicitly appears in Eq. (3). If the flow field only changes which patch positions q are summed over while the direction remains the user's normalized δ_i, then the deformation geometry does not actually control the movement direction, and the central mechanism claimed by the paper is not exercised. The authors must state clearly whether δ_i is replaced by the flow vector, provide the revised loss function if so, and include an ablation that isolates the effect of using flow directions versus user directions.
[Abstract, Table 1, Section 6.3] The abstract's statement that "FlowDrag outperforms existing drag-based editing methods on both VFD Bench and DragBench" is not supported by Table 1. On DragBench, FlowDrag is second on 1-LPIPS (0.82 vs. DiffEditor's 0.89), and the MD advantage over GoodDrag (22.88 vs. 22.96) is within the range where noise or a single case could change the ranking. The authors acknowledge the 1-LPIPS caveat in Section 6.3, but the claim should be qualified to say "best MD and competitive fidelity on DragBench, and best overall on VFD-Bench," and the MD difference should be accompanied by a significance test.

minor comments (6)

[Section 6.4, Table 4 caption] The text says "β = 0.8 yields the best results, achieving maximal mean edge length ratio and mean ARAP error," but a lower ARAP error is better and the table indeed shows the lowest error at β=0.8. This should read "minimal mean ARAP error."
[Section 5, Appendix C] VFD-Bench pairs are consecutive video frames, which may include lighting changes, camera motion, or non-rigid background motion even within the user mask. The paper should describe the frame-filtering criteria (e.g., how much background change was tolerated, whether frames with occlusion or blur were removed) and report any manual cleaning protocol.
[Appendix B, Algorithm 1] The algorithm's input declaration lists "D: Depth map" but Step 1 computes the depth map from the input image I. The input/output signature should be made consistent (e.g., input is I and τd, τb; D is an internal variable).
[Appendix A.1, Eq. (11)] Equation (11) is referenced as "Eq. 11" but the equation itself is not numbered in the text; please add the number or change the reference.
[Section 6.3, User Study] The user study reports average rankings from 25 volunteers on 50 images, but no inter-rater agreement, per-method standard error, or statistical test is provided. A paired test (e.g., Wilcoxon) across images would strengthen the claim that FlowDrag is preferred.
[Section 7, Limitations] The paper acknowledges in the limitations that FlowDrag "primarily supports rigid edits" and struggles with content creation/removal, but this scope restriction is not carried into the abstract or the main claims. Please state the rigid-edit scope prominently when summarizing the method's capabilities.

Circularity Check

0 steps flagged · score 0.0 of 10

No by-construction circularity found: the core FlowDrag derivation is self-contained, and the VFD-Bench hyperparameter tuning is an evaluation-protocol limitation, not a circular reduction.

full rationale

The derivation chain in FlowDrag is not circular by construction. The 3D mesh is generated by external models (Marigold and Hunyuan3D, cited as prior work), the deformation uses the external SR-ARAP energy (Eq. 7) plus a proposed progressive interpolation (Eq. 8) and an inter-step smoothness term (Eq. 9), and the resulting 2D flow field (Eq. 10) is injected into the standard motion-supervision loss (Eq. 3). No equation in the paper equates the evaluation metrics (PSNR, 1-LPIPS, MD) to the method's fitted constants or to the vector flow by definition. VFD-Bench supplies genuine ground-truth edits from consecutive video frames, and DragBench is an external benchmark, so the central outperformance claim has independent empirical content. The hyperparameters (beta, vector count, sampling strategy) are ablated on VFD-Bench in Section 6.4 and then used for the final VFD-Bench table, which is a legitimate benchmark-tuning concern but is not a circular reduction: the chosen values do not force the reported metric values by construction, and the method's predictions remain nontrivial with respect to its inputs. The per-image mesh-selection rule in Section 6.1 (DiffMesh unless it 'exhibits significant artifacts' or 'deviates substantially') and the lack of a separate validation split weaken the reliability of the comparison, but they are empirical-validity issues rather than circularity. Self-citations appear only in related work and in the layout-injection motivation, and they are not load-bearing: the early-vs-late timestep behavior is also supported by an external citation (Wu et al., 2023b). The limitation section explicitly concedes that projecting 3D deformation to 2D loses structural information, further confirming that the authors are not assuming the conclusion they claim to demonstrate.

Assumptions & free parameters 10 free parameters · 6 assumptions · 0 invented entities

The method rests on standard diffusion inversion and ARAP machinery, but on several domain assumptions about mesh quality from single images and about video frames being valid ground-truth edits. The DiffMesh/DepthMesh selection is an uncontrolled ad hoc rule, and the centrality of VFD-Bench as both tuning and evaluation set adds to the circularity burden.

free parameters (10)

Progressive step size lambda (Eq. 8) = not specified (0 < lambda <= 1)
Controls per-iteration fraction of remaining handle-target distance; value unknown, affects deformation smoothness and final flow field.
Progressive deformation iterations K = not specified
Number of iterations for handle vertex movement; affects how gradually deformation is applied and computational cost.
SR-ARAP rotation regularization alpha (Eq. 7) = 0.2-0.4 (range)
Penalty weight on adjacent rotation differences; selected by authors without per-task criterion (Section 6.1).
Inter-step smoothness beta (Eq. 9) = 0.8 (best on VFD-Bench ablation)
Penalizes per-iteration vertex displacement; beta=0.8 chosen from ablation on the same VFD-Bench used for final evaluation (Table 4).
Number of sampled flow vectors in Phi_hat = 10 (best on VFD-Bench)
5-30 vectors sampled from the 20x20 grid; 10 selected by Fig. 8 ablation on VFD-Bench, meaning the evaluation benchmark also served as the tuning set.
Sampling grid size N = 20
N=20 grid for candidate vectors; specified in Section 6.1.
Depth threshold tau_d (Algorithm 1) = 0.1
Controls facet formation from depth; set in Appendix B, affects mesh topology.
Background threshold tau_b = mean depth + 0.3
Removes background vertices; setting from Appendix B, affects which vertices are movable.
Layout injection timestep t' = 30
Timestep at which deformed-mesh layout features are injected; chosen without a reported ablation (Section 6.1).
Patch radii R (Eq. 3) and R2 (Eq. 5) = not specified
Radii for motion supervision and point tracking patches; inherited from prior work but not stated, so exact replication is impossible.

assumptions (6)

standard math DDIM inversion is deterministic and invertible, so a real image can be mapped to a latent and back (Eqs. 1-2).
Section 3.1; the whole editing pipeline operates on this inverted latent.
standard math SR-ARAP energy minimization yields locally rigid deformation under sparse handle constraints.
Section 3.3, Eqs. 6-7; relies on Sorkine & Alexa 2007 and Levi & Gotsman 2014.
domain assumption Marigold and Hunyuan3D produce meshes that faithfully represent the input image geometry.
Section 4.1; no quantitative validation of mesh accuracy is given, and the paper switches to DepthMesh when DiffMesh fails.
domain assumption U-Net feature maps provide stable per-pixel semantic correspondence during denoising (DIFT).
Section 3.2 and Eq. 5; point tracking assumes feature locations move with object points.
domain assumption Consecutive video frames constitute valid ground-truth drag edits.
Section 5; camera motion, lighting shifts, and non-rigid appearance changes could violate this, though masked metrics mitigate it.
ad hoc to paper The choice between DiffMesh and DepthMesh can be made per image with no fixed criterion.
Section 6.1: 'When DiffMesh exhibits significant artifacts or deviates substantially from the original image, we employ DepthMesh instead.'

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Cite this review

Pith. "Pith review of FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields." pith.science (2026). https://pith.science/paper/IAKWP7JZ

@misc{pith2026250708285,
  author       = {Pith},
  title        = {Pith review of: FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IAKWP7JZ}},
  note         = {Machine review of arXiv:2507.08285}
}

read the original 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.

Figures

Figures reproduced from arXiv: 2507.08285 by the authors.

**Figure 1.** Results of drag-based image editing. While other methods 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 or hand, whereas other methods fail to maintain geometric consistency. Tumanyan et al., 2023; Cao et al., 2023;… view at source ↗

**Figure 2.** (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 to generating a 2D vector flow field. (d) Comparison of FlowDrag with other methods on DragBench. (e) Comparison of FlowDrag with other methods on o… view at source ↗

**Figure 3.** Overview of the FlowDrag pipeline. (a) Step 1: 3D mesh generation using depth-based and diffusion-based approaches. (b) Step 2: Progressive mesh deformation via SR-ARAP, with differences projected as a 2D vector flow field. Point Tracking Following motion supervision, point tracking updates the handle points {h k i } by searching for the best matching features within a local patch. Specifically, h k+1 i = arg min q∈… view at source ↗

Figures from the paper (11 more)

**Figure 4.** Figure 4: Step 3: Vector flow-based drag editing in FlowDrag. The 2D vector flow field is employed for motion supervision and point tracking. Meanwhile, the 3D deformed mesh is projected onto a 2D plane and incorporated as a layout feature through the guidance branch at an early…

**Figure 5.** Figure 5: Example images from VFD-Bench. We constructed this drag-based image editing dataset by selecting closely spaced frames from DAVIS, LOVEU-TGVE, and copyright-free Pexels videos, focusing on noticeable changes in pose or structure. and keeps only the top few, and (2) Uni…

**Figure 7.** Figure 7: User study on drag accuracy and image quality. (a) PSNR value over the number of vectors (b) 1-LPIPS value over the number of vectors [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

**Figure 8.** Figure 8: Impact of vector count on (a) PSNR and (b) 1-LPIPS. When 10 vectors are sampled, both metrics achieve their highest values, indicating the importance of optimal vector selection. User Study. We conducted a user study on 50 images from DragBench and VFD-Bench, comparing…

**Figure 9.** 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 de…

**Figure 10.** Figure 10: 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 corresponding target points (…

**Figure 11.** Figure 11: DepthMesh deformation results when generating the mesh from the depth map with and without background consideration. (a) Result with background. (b) Result without background. Next, we examine DiffMesh sensitivity by adjusting the sampling steps in the image-to-3D dif…

**Figure 13.** Figure 13: Additional qualitative comparisons on DragBench. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_13.png]

**Figure 14.** Figure 14: Additional qualitative comparisons on VFD-Bench. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_14.png]

**Figure 15.** Figure 15: Sensitivity analysis and qualitative comparison of mesh deformation and drag-editing results. (a) We evaluate the robustness of DepthMesh under different reduction ratios, which control the density of facet connections during mesh construction (see Algorithm 1). A rat…

**Figure 16.** Figure 16: More visualization of the mesh-guided editing process using DiffMesh. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_16.png]

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