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

Zipeng Guo; Xiaoan Liu; Lichen Ma; Cheng Wang; Yu He; Xiaolong Fu; Jingling Fu; Xinyuan Shan; Shaojie Guo; Luohang Liu; Junshi Huang; Yan Li

arXiv · 2607.00920

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

Zipeng Guo·Xiaoan Liu·Lichen Ma·Cheng Wang·Yu He·Xiaolong Fu·Jingling Fu·Xinyuan Shan·Shaojie Guo·Luohang Liu·Junshi Huang·Yan Li

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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.

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BibTeX RIS

Zipeng Guo, Xiaoan Liu, Lichen Ma, Cheng Wang, Yu He, Xiaolong Fu, Jingling Fu, Xinyuan Shan, Shaojie Guo, Luohang Liu, Junshi Huang, Yan Li. 2026-07-02. GMO-E$^2$DIT: Grounded Multi-Operation Editing for E-Commerce Images. https://arxiv.org/abs/2607.00920

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