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Grounded Exploration for Open-Ended Image Editing

Jieyu Zhou, Mehdi Ben Ayed, Shubham Gupta, Zhenyu Tan, Jiajing Xu, Dmitry Kislyuk, Eric Tzeng, Christopher J. MacLellan, Charles Rosenberg

arXiv:2610.33810v1cs.CV

TL;DR

Open-ended image-editing requests allow multiple plausible outcomes, while repeated sampling can be redundant and diversity prompting may yield ungrounded variation. The paper structures feasible edit scopes before generating concrete instructions, and reports higher semantic dispersion and groundedness than direct diversity prompting across 300 tasks.

  • Problem

    Open-ended image-editing requests admit multiple plausible outcomes, but repeated sampling may remain concentrated and diversity prompting does not ensure meaningful, plausible edits.

    Source: §1 · p. 1

  • Method

    A two-stage framework identifies image-grounded edit scopes and then generates concrete edit instructions for those scopes.

    Source: §2 · p. 2

  • Results

    24.6% higher mean semantic dispersion and 17.1% higher mean groundedness than direct diversity prompting were achieved across 300 tasks.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The results suggest open-ended multimodal tasks may benefit from strategies that preserve and expose multiple valid modes rather than optimize toward one preferred realization.

    Source: §5 · p. 4

Abstract

from arXiv · show

Open-ended image-editing requests, such as ``decorate this room,'' admit multiple valid realizations. Direct sampling may produce ungrounded variation, whereas explicit diversity prompting often produces superficial differences. We study how to ground these requests without prematurely eliminating their legitimate ambiguity. We introduce a two-stage framework that first identifies diverse, feasible edit scopes and then realizes them as executable edit instructions. Across 300 open-ended image-editing tasks, our approach achieves 24.6% higher mean semantic dispersion and 17.1% higher mean groundedness than direct diversity prompting, while producing more valid and distinct realizations. These results show that structuring feasible edit directions before realization supports more useful grounded exploration.

1 Introduction

Open-ended editing requests admit many plausible outcomes, but repeated sampling can produce redundant edits and diversity prompting can produce alternatives that are not meaningful or plausible. The paper frames the task as exploring distinct edits while respecting both user intent and the source image.

1 Introduction

  • Repeated sampling can cluster around one interpretation, while diversity prompting can vary outputs without ensuring meaningful or plausible editing directions.For example, repeated edits may add plants while overlooking rugs, art, lighting, or shelving.Source: §1 · p. 1
  • Image edits must explore underspecified intent while preserving visible scene structure and avoiding changes to unrelated regions or spatial constraints.Too little variation produces redundant alternatives; aggressive diversity can drift from the request.Source: §1 · pp. 1–2
  • 24.6% higher mean semantic dispersion and 17.1% higher mean groundedness than direct diversity prompting were achieved across 300 open-ended image-editing tasks.The framework first structures feasible edit directions, then realizes them as executable instructions.Source: §1 · p. 2

2 Grounded Exploration for Open-Ended Image Editing

The framework separates exploration from visual execution: it first identifies image-compatible regions and permitted edit extents, then produces concrete edit plans for those scopes.

2 Grounded Exploration for Open-Ended Image Editing

  • The method generates K meaningfully different candidate edits by separating spatial exploration from concrete realization.The scopes are grounded in both the source image and the underspecified request.Source: §2 · p. 2
  • Stage 1 specifies each editable region, permitted change extent, and preservation boundary without choosing a style, material, or replacement object.A room-design request could target the sofa area, fireplace, workspace, or whole seating area.Source: §2 · p. 2
  • Stage 2 creates one self-contained edit plan per scope, specifying objects and treatments while respecting that scope’s region and preservation boundary.The resulting plans are passed independently to the same image-editing model.Source: §2 · p. 2

3 Experiments

The experiments evaluate whether structured exploration yields candidate sets that are both diverse and grounded, using 300 open-ended editing tasks and set-level judgments. Results favor structured exploration over generic diversity prompting, with different stages contributing to diversity and grounding.

3.2 Experimental Setup

  • The 300 tasks came from employee testing of an online design platform; comparisons included stochastic image sampling, independently sampled or prompted instructions, and two-stage variants.Prompt-level methods used Qwen/Qwen3.6-35B-A3B and a fixed Qwen/Qwen-Image-Edit-2509 rendering backend; image-sampling tests varied random seeds across three backends.Source: §3 · pp. 2–3, §3.2 · p. 3

3.1 Evaluation Metrics

  • The primary evaluation rates eight-candidate sets for Instruction Following and Groundedness, and additionally counts Valid@8, semantic directions, distinct realizations, and pairwise Dispersion.Direction differences concern high-level editing choices; color, material, texture, accessories, or minor placement differences remain within one direction.Source: §3.1 · p. 3

3.3 Results

  • Structured exploration improves candidate-set quality over generic diversity prompting.Table 1 reports set-level evaluation over 300 tasks, with mean ± standard error across tasks and higher values better for all metrics.Source: §3.3 · p. 3, Table 1 · p. 4
  • Across 300 tasks, direct image sampling produced 2.46 directions and 3.63 distinct realizations, versus 2.90 and 4.42 for Naive instruction sampling and 4.17 and 5.63 for Realization only.This comparison uses the same Qwen image backend and indicates broader, less redundant sets from instruction-space exploration than stochastic sampling.Source: §3.3 · p. 3
  • Gemini had the highest instruction-following score (3.93) but frequent structural hallucinations; Flux had the highest groundedness (3.95) yet only 2.85 distinct realizations across 1.49 directions.Flux produced 5.71 valid outputs, illustrating that strong individual outputs need not yield useful set-level exploration.Source: §3.3 · pp. 3–4
  • Realization only achieved Direction Coverage (4.17) and 5.63 distinct realizations, while region-level Stage 1 improved groundedness and Valid@8 at comparable realization diversity.High-level exploration produced nearly identical results to Realization only, suggesting an abstract semantic scaffold added little benefit.Source: §3.3 · p. 4

4 Discussion

The discussion treats open-ended generation as coverage of multiple valid modes within a constrained solution space, rather than simply producing diverse samples. It presents preserving and exposing valid multimodality as a complementary goal to sharpening toward one preferred output.

4 Discussion

  • For open-ended generation, the target is coverage of multiple valid modes within a constrained solution space, not diversity alone.In image editing, candidate edits must remain grounded in the source image and user request.Source: §4 · p. 4
  • Repeated sampling may stay near similar high-level realizations, while generic diversity prompting can produce alternatives less grounded in the image or request.The goal is to broaden semantic coverage within the edits supported by both.Source: §4 · p. 4
  • Preserving and exposing multiple valid modes complements sharpening a model distribution toward a preferred output; learning such distributions directly remains a longer-term question.The current approach structures what can vary at inference time.Source: §4 · p. 4

5 Conclusion

The paper frames open-ended image editing as grounded exploration: useful systems should offer meaningfully different solutions without drifting from the source image or request. Its results indicate that structuring permissible variation can improve the balance between grounding and exploration.

  • Open-ended image editing should expose multiple meaningfully different solutions without drifting from the source image or user request.Source: §5 · p. 4
  • Generic diversity prompting is not sufficient, while structuring permissible variation can improve the balance between grounding and exploration.Source: §5 · p. 4
  • Open-ended multimodal tasks may benefit from strategies that preserve and expose multiple valid modes rather than optimize toward one preferred realization.Source: §5 · p. 4

A Additional Qualitative Results

Figure 2 compares Gemini, prompt diversity, and region-level exploration. The described outputs contrast scene-geometry preservation with structural or viewpoint changes.

B Condensed Generation Prompts

The condensed prompts specify how to generate sets of executable edits from an image, request, and optional scaffold, while preserving source constraints and requiring substantive variation.

B Condensed Generation Prompts

B.1 Realization Generation

  • Realization generation takes the image, request, and optional Stage-1 scaffold as input and produces K concrete, executable edit instructions.Source: §B.1 · p. 6
  • Each instruction must plausibly satisfy the request, identify the visible content to change and how, and use an appropriate edit extent.Source: §B.1 · p. 6
  • Instructions must preserve source geometry, spatial relationships, unrelated content, and explicit constraints.Source: §B.1 · p. 6
  • The instruction set should vary in dominant editing direction or composition, not merely in wording or superficial attributes.Source: §B.1 · p. 6
  • The prompt requires returning only a JSON array containing K instruction strings.Source: §B.1 · p. 6

C Condensed Evaluation Prompts

The condensed evaluation prompts use structured judge calls to assess candidates against the source image and request, then return ratings and validity decisions for each candidate.

C Condensed Evaluation Prompts

  • Evaluation uses three structured GPT-5.5 judge calls, with repeated image-indexing instructions and diagnostics omitted from the condensed prompts.Source: C Condensed Evaluation Prompts · p. 6

C.1 Validity and Set-Level Ratings

  • Each candidate is evaluated against the source image and original user request.Source: §C.1 · p. 6
  • A candidate is valid only if it satisfies the requested intent and scope, remains an edit of the same scene, and preserves viewpoint, fixed structure, geometry, spatial relationships, and unrelated content unless change is required.Source: §C.1 · p. 6
  • Unsupported reconstruction, structural or layout changes, unrelated edits, wrong targets, and required-edit no-ops are invalid.Source: §C.1 · p. 6
  • Set-level instruction-following ratings use a 1–5 scale, with 5 indicating that almost all outputs satisfy the request and 1 that most fail.Source: §C.1 · pp. 6–7
  • Set-level groundedness ratings use a 1–5 scale, from source structure preserved almost throughout to most outputs departing from the source.Source: §C.1 · p. 7
  • The returned structured JSON contains both set-level ratings and a validity decision for every candidate.Source: §C.1 · p. 7
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