arxiv: 2605.01135 · v2 · submitted 2026-05-01 · 💻 cs.CV

Recognition: unknown

ScribbleEdit: Synthetic Data for Image Editing with Scribbles and Text

Anya Ji , George Ma , T\'ea Wright , Yiming Zhang , David M. Chan , Alane Suhr , Somayeh Sojoudi

Authors on Pith no claims yet

Pith reviewed 2026-05-09 18:55 UTC · model grok-4.3

classification 💻 cs.CV

keywords image editingsynthetic datascribblestext instructionsdiffusion modelsmultimodal modelsinpaintingfine-tuning

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The pith

Finetuning on a synthetic dataset of scribble-and-text pairs improves image editing models' spatial alignment and semantic consistency.

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

The paper sets out to solve the problem that current image editing models cannot reliably follow both rough freehand scribbles and detailed text instructions at the same time. It does this by building a large synthetic collection of source-target image pairs, each annotated with human-drawn scribbles marking edit regions and automatically generated text describing the desired change. A sympathetic reader would care because the combination of spatial hints and semantic guidance is exactly the intuitive interface users want, yet existing models trained only on text or only on masks fall short. If the approach works, fine-tuned models should start producing edits that respect both the drawn boundaries and the spoken intent without extra user effort.

Core claim

The central claim is that a synthetic dataset called ScribbleEdit, constructed by first using inpainting to create source-target image pairs, then overlaying human-drawn scribbles on the source images and generating matching text instructions with a vision-language model, supplies the missing training signal. When both diffusion-based and autoregressive unified multimodal editing models are fine-tuned on this data, they generate edits that are spatially aligned with the scribbles and semantically consistent with the text, whereas the same models before fine-tuning struggle with the abstract scribble inputs.

What carries the argument

The ScribbleEdit synthetic pipeline: automatic generation of source-target image pairs via inpainting, followed by human scribble annotation and vision-language model text generation to create paired training examples for combined scribble-plus-text editing.

If this is right

Off-the-shelf diffusion and autoregressive editing models perform poorly when given abstract scribble inputs without specialized training.
Fine-tuning on the synthetic data produces measurable gains in both spatial alignment with scribbles and semantic match to text.
The same pipeline can be used to create training data for other joint input modalities in image editing.
Unified multimodal models benefit more than single-modality baselines once the combined scribble-text data is available.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the synthetic pipeline captures enough variability in scribble style and text phrasing, the resulting models could reduce the need for users to provide perfect inputs.
The approach suggests a general route for creating training data whenever two complementary control signals are hard to collect together in the wild.
Extending the pipeline to video or 3D editing would require only swapping the inpainting step for the appropriate generative process.

Load-bearing premise

The generated pairs, scribbles, and text instructions have a distribution close enough to real user inputs that fine-tuning on them produces models that generalize to actual user scribbles and instructions.

What would settle it

Measure editing success metrics on a held-out set of real user-provided scribbles and text instructions collected from people using an actual editing interface, then check whether the fine-tuned models show a large drop in spatial alignment or semantic consistency compared with their performance on the synthetic test set.

Figures

Figures reproduced from arXiv: 2605.01135 by Alane Suhr, Anya Ji, David M. Chan, George Ma, Somayeh Sojoudi, T\'ea Wright, Yiming Zhang.

**Figure 1.** Figure 1: Overview of the ScribbleEdit pipeline for dataset con view at source ↗

**Figure 2.** Figure 2: Comparison of editing results across methods. Each row shows one dataset example (Edit Instruction, Original, Scribble, Target) view at source ↗

read the original abstract

Recent progress in generative models has significantly advanced image editing capabilities, yet precise and intuitive user control remains difficult. Specifically, users often struggle to communicate both exact spatial layouts and specific semantic details simultaneously. While natural language instructions effectively convey high-level semantics like texture and color, they lack spatial specificity. Conversely, freehand scribbles provide rough spatial boundaries but cannot express detailed visual attributes. Consequently, achieving precise control requires combining both modalities. However, existing models struggle to jointly interpret abstract scribbles alongside text due to a lack of specialized training data. In this work, we introduce ScribbleEdit, a large-scale synthetic dataset designed to bridge this gap by combining natural language instructions with freehand scribble inputs for more accurate, controllable edits. We construct this dataset through a synthetic pipeline that automatically generates source-target image pairs via inpainting, which are then paired with human-drawn scribbles and VLM-generated text instructions. Using ScribbleEdit, we evaluate and finetune both diffusion-based and autoregressive unified multimodal image editing models. Our experiments reveal that while off-the-shelf models struggle with abstract scribble inputs, finetuning on our synthetic dataset significantly improves their ability to generate spatially aligned and semantically consistent edits.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ScribbleEdit gives a workable synthetic dataset for scribble-plus-text editing and shows fine-tuning can help, but the abstract supplies no metrics or real-user tests so the gains are hard to judge.

read the letter

The core of this paper is a new synthetic dataset that generates image edit pairs through inpainting, then attaches human scribbles and VLM-written instructions. They fine-tune both diffusion and autoregressive models on it and report better spatial and semantic consistency, though nothing in the abstract quantifies that improvement or compares it to baselines. The construction method itself is the clearest advance: scaling up joint multimodal training data without needing paired real edits for every example. That addresses a practical pain point where text alone is too vague on location and scribbles alone are too vague on appearance. The pipeline is straightforward enough that others could replicate or extend it for similar controllable-generation tasks. What the work does well is identify the exact failure mode of current models on abstract scribble inputs and offer a data recipe to mitigate it. The stress-test concern about distribution shift is worth watching. Inpainting often produces boundary or lighting artifacts that casual user scribbles do not, and VLM text tends to be more precise than typical user prompts. If the paper only evaluates on its own synthetic splits, the claimed generalization to real inputs remains unproven. The absence of any numbers, ablations, or held-out real-user tests in the abstract makes the central empirical claim difficult to assess. This is the kind of dataset paper that researchers building editing interfaces or multimodal generators would want to look at, especially if the data is released. It is coherent on its own terms and engages the literature honestly, so it deserves a full referee process rather than a desk reject. I would send it for review but would expect the authors to add concrete metrics, baseline comparisons, and at least a small real-user validation set before acceptance.

Referee Report

2 major / 2 minor

Summary. The paper introduces ScribbleEdit, a large-scale synthetic dataset for image editing that pairs freehand scribbles with natural language instructions. Source-target image pairs are generated via inpainting, then annotated with human-drawn scribbles and VLM-generated text instructions. The authors evaluate off-the-shelf diffusion-based and autoregressive multimodal editing models on this data and fine-tune them, claiming that the fine-tuned models produce substantially better spatially aligned and semantically consistent edits than the base models.

Significance. If the reported improvements are quantitatively robust and generalize beyond the synthetic distribution, the work would provide a practical solution to the data scarcity problem for joint scribble-text image editing, a capability that is currently limited in user-facing generative systems. The scalable synthetic pipeline and coverage of both diffusion and autoregressive architectures are positive aspects that could be adopted by the community.

major comments (2)

[Experiments] Experiments section: The central claim that fine-tuning on ScribbleEdit 'significantly improves' the ability to generate spatially aligned and semantically consistent edits is not supported by any quantitative metrics, baseline comparisons, or ablation results in the provided manuscript text. Without these, the magnitude and reliability of the improvement cannot be assessed.
[Dataset construction] Dataset construction and evaluation: The synthetic pipeline (inpainting for source-target pairs + human scribbles + VLM text) is assumed to produce examples whose distribution matches real user scribble abstraction levels, text phrasing, and edit semantics. No held-out evaluation on authentic user-provided scribble+text inputs is reported, leaving the generalization claim untested and vulnerable to distribution shift (e.g., inpainting boundary artifacts or overly precise VLM captions).

minor comments (2)

[Abstract] The abstract would be strengthened by including at least one key quantitative result (e.g., a metric improvement on a held-out set) to allow readers to gauge the claimed gains without reading the full experiments.
[Method] Notation for the two model families (diffusion vs. autoregressive) should be introduced consistently when describing the fine-tuning procedure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important areas for strengthening the experimental validation and generalization claims, and we have revised the paper accordingly to address them.

read point-by-point responses

Referee: [Experiments] Experiments section: The central claim that fine-tuning on ScribbleEdit 'significantly improves' the ability to generate spatially aligned and semantically consistent edits is not supported by any quantitative metrics, baseline comparisons, or ablation results in the provided manuscript text. Without these, the magnitude and reliability of the improvement cannot be assessed.

Authors: We agree that quantitative support is required to rigorously substantiate the improvement claims. The original manuscript emphasized qualitative visual results comparing base and fine-tuned models. In the revised version, we have added quantitative evaluations using metrics for spatial alignment (region overlap with scribble masks) and semantic consistency (CLIP similarity to instructions), along with baseline comparisons to off-the-shelf models and ablations on fine-tuning data volume. These results are now reported in the Experiments section to quantify the gains. revision: yes
Referee: [Dataset construction] Dataset construction and evaluation: The synthetic pipeline (inpainting for source-target pairs + human scribbles + VLM text) is assumed to produce examples whose distribution matches real user scribble abstraction levels, text phrasing, and edit semantics. No held-out evaluation on authentic user-provided scribble+text inputs is reported, leaving the generalization claim untested and vulnerable to distribution shift (e.g., inpainting boundary artifacts or overly precise VLM captions).

Authors: We acknowledge the risk of distribution shift and the value of real-user validation. The pipeline uses human annotators for scribbles to approximate natural abstraction and VLM captions derived from target images for semantic alignment. In the revision, we have added a held-out evaluation on a small set of authentic user-provided scribble+text pairs, confirming that fine-tuned models retain improved performance. We have also expanded the discussion of potential artifacts and mitigation strategies in the Dataset Construction section. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical evaluation of synthetic data pipeline

full rationale

The paper presents an empirical contribution: a synthetic data generation pipeline (inpainting for pairs, human scribbles, VLM text) followed by finetuning experiments on diffusion and autoregressive models, with reported improvements in spatial alignment and semantic consistency. No equations, derivations, or fitted parameters are claimed to predict results by construction. No self-citations are used to justify uniqueness or load-bearing premises. The central claim rests on standard train/test splits and observed metric gains, which are externally falsifiable and do not reduce to the input data by definition. This is a typical non-circular ML dataset paper.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim depends on the quality and representativeness of the synthetic data generation process.

axioms (2)

domain assumption Inpainting produces realistic and diverse source-target image pairs suitable for training editing models
Core step used to generate the paired data without manual editing.
domain assumption VLM-generated text instructions accurately describe the intended edits
Used to create the text component of each training example.

pith-pipeline@v0.9.0 · 5534 in / 1165 out tokens · 45533 ms · 2026-05-09T18:55:36.506780+00:00 · methodology

discussion (0)

Reference graph

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