GMO-E$^2$DIT: Grounded Multi-Operation Editing for E-Commerce Images

• URL: http://arxiv.org/abs/2607.00920v2
• Date: Thu, 02 Jul 2026 02:41:37 GMT
• Title: GMO-E$^2$DIT: Grounded Multi-Operation Editing for E-Commerce Images
• Abstract summary: Real-world e-commerce image editing often requires multiple, localized, and auditable operations rather than global restyling.<n>Existing one-shot editors conflate intent resolution, spatial grounding, and synthesis into a single step.<n>We introduce GMO-E$2$DIT, an agentic editing framework that couples a Vision-Language Model (VLM) with a mask-conditioned image editor to tackle structured multi-turn task completion.
• Score: 16.213122519512932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world e-commerce image editing often requires multiple, localized, and auditable operations rather than global restyling. This compositional nature poses a dual challenge: models must precisely apply all requested edits to the correct regions while preserving unmodified content, even under ambiguous instructions. Existing one-shot editors conflate intent resolution, spatial grounding, and synthesis into a single step, frequently resulting in partial execution failures, which is unacceptable for commercial scenarios. To address this, we introduce GMO-E$^2$DIT, an agentic editing framework that couples a Vision-Language Model (VLM) with a mask-conditioned image editor to tackle structured multi-turn task completion. Given an underspecified instruction, the VLM agent constructs a region-grounded edit agenda, effectively decoupling cognitive reasoning from generative rendering. The framework then executes sub-programs via operation-aware masks and references, utilizing a reflection-driven loop to inspect intermediate results and determine the subsequent state. This iterative mechanism reliably preserves safe partial progress, retries unfinished operations, and recovers from errors. Furthermore, we develop a unified data pipeline providing aligned supervision for planning, execution, and reflection, alongside EComEditBench, a comprehensive benchmark for instruction-driven evaluation. Extensive experiments demonstrate that GMO-E$^2$DIT achieves competitive performance compared to strong closed-source models, yielding superior instruction accuracy and edit fidelity over existing baselines.

Related papers

• CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences [51.86576932800981]
  We introduce CV-Arena, an open benchmark designed to evaluate this capability at professional scales.<n>CV-Arena contains 12K high-resolution real-image instruction pairs spanning 16 instruction-based visual task types.<n>We develop CV-Agent, a lightweight agentic model that combines planning, editing, and verification.
  arXiv  Detail & Related papers  (2026-05-30T23:37:55Z)
• SWE-Edit: Rethinking Code Editing for Efficient SWE-Agent [17.74253780030394]
  SWE-Edit decomposes code editing into two specialized subagents: a Viewer that extracts task-relevant code on demand, and an Editor that executes modifications from high-level plans.<n>On SWE-bench Verified, SWE-Edit improves resolved rate by 2.1% while reducing inference cost by 17.9%.
  arXiv  Detail & Related papers  (2026-04-28T20:35:09Z)
• Making Image Editing Easier via Adaptive Task Reformulation with Agentic Executions [27.891822809255682]
  We propose an adaptive task reformulation framework that improves editing performance without modifying the underlying model.<n>Our key idea is to transform the original image-instruction pair into a sequence of operations that are dynamically determined and executed by a MLLM agent.
  arXiv  Detail & Related papers  (2026-04-17T10:17:22Z)
• CAMEO: A Conditional and Quality-Aware Multi-Agent Image Editing Orchestrator [8.901958956012072]
  Conditional image editing is crucial in scenarios requiring strict structural control.<n>Most approaches rely on single-step generation.<n>We propose textbfCAMEO, a structured multi-agent framework that reformulates conditional editing as a quality-aware, feedback-driven process.
  arXiv  Detail & Related papers  (2026-04-03T16:27:02Z)
• IMAGAgent: Orchestrating Multi-Turn Image Editing via Constraint-Aware Planning and Reflection [40.21337735524356]
  IMAGAgent is a multi-turn image editing agent framework based on a "plan-execute-reflect" closed-loop mechanism.<n>It achieves deep synergy among instruction parsing, tool scheduling, and adaptive correction within a unified pipeline.<n>Experiments on our constructed textbfMTEditBench and the MagicBrush dataset demonstrate that IMAGAgent achieves performance significantly superior to existing methods.
  arXiv  Detail & Related papers  (2026-02-12T02:37:38Z)
• CoEditor++: Instruction-based Visual Editing via Cognitive Reasoning [98.98349220451216]
  CoEditor++ is a training-free framework that decomposes editing into "what to edit" and "how to edit"<n>We show that CoEditor++ achieves state-of-the-art performance in both general editing and responsible editing tasks.<n>Our findings suggest the potential toward cognitive-centric instruction-based image editing.
  arXiv  Detail & Related papers  (2026-01-31T12:20:46Z)
• UniREditBench: A Unified Reasoning-based Image Editing Benchmark [52.54256348710893]
  This work proposes UniREditBench, a unified benchmark for reasoning-based image editing evaluation.<n>It comprises 2,700 meticulously curated samples, covering both real- and game-world scenarios across 8 primary dimensions and 18 sub-dimensions.<n>We fine-tune Bagel on this dataset and develop UniREdit-Bagel, demonstrating substantial improvements in both in-domain and out-of-distribution settings.
  arXiv  Detail & Related papers  (2025-11-03T07:24:57Z)
• EdiVal-Agent: An Object-Centric Framework for Automated, Fine-Grained Evaluation of Multi-Turn Editing [170.71134330650796]
  EdiVal-Agent is an object-centric evaluation framework for instruction-based image editing.<n>It is designed to assess not only standard single-turn but also multi-turn instruction-based editing with precision.<n>We build EdiVal-Bench, a benchmark covering 9 instruction types and 13 state-of-the-art editing models spanning in-context, flow-matching, and diffusion paradigms.
  arXiv  Detail & Related papers  (2025-09-16T17:45:39Z)
• An LLM-LVLM Driven Agent for Iterative and Fine-Grained Image Editing [5.192553173010677]
  RefineEdit-Agent is a novel, training-free intelligent agent framework for complex, iterative, and context-aware image editing.<n>Our framework comprises an LVI-driven instruction and scene understanding module, a multi-level editing planner, an iterative image editing module, and a crucial LVLM-driven feedback and evaluation loop.
  arXiv  Detail & Related papers  (2025-08-24T16:28:18Z)
• Image Editing As Programs with Diffusion Models [69.05164729625052]
  We introduce Image Editing As Programs (IEAP), a unified image editing framework built upon the Diffusion Transformer (DiT) architecture.<n>IEAP approaches instructional editing through a reductionist lens, decomposing complex editing instructions into sequences of atomic operations.<n>Our framework delivers superior accuracy and semantic fidelity, particularly for complex, multi-step instructions.
  arXiv  Detail & Related papers  (2025-06-04T16:57:24Z)
• What Changed? Detecting and Evaluating Instruction-Guided Image Edits with Multimodal Large Language Models [88.398085358514]
  DICE is a model designed to detect localized differences between the original and the edited image.<n>It is trained using a strategy that leverages self-supervision, distillation from inpainting networks, and full supervision.<n>We demonstrate that DICE effectively identifies coherent edits, effectively evaluating images generated by different editing models with a strong correlation with human judgment.
  arXiv  Detail & Related papers  (2025-05-26T18:00:10Z)
• Task-Oriented Diffusion Inversion for High-Fidelity Text-based Editing [60.730661748555214]
  We introduce textbfTask-textbfOriented textbfDiffusion textbfInversion (textbfTODInv), a novel framework that inverts and edits real images tailored to specific editing tasks. ToDInv seamlessly integrates inversion and editing through reciprocal optimization, ensuring both high fidelity and precise editability.
  arXiv  Detail & Related papers  (2024-08-23T22:16:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.