GMO-E$^2$DIT：面向电商图像的接地多操作编辑
GMO-E$^2$DIT: Grounded Multi-Operation Editing for E-Commerce Images
AI总结：
提出GMO-E$^2$DIT框架，通过VLM代理构建区域接地编辑议程，结合掩码条件图像编辑器和反思循环，实现多操作、可审计的电商图像编辑，在指令准确性和编辑保真度上超越现有基线。
摘要：
现实世界的电商图像编辑通常需要多个、局部化且可审计的操作，而非全局风格化。这种组合性质带来了双重挑战：模型必须精确地将所有请求的编辑应用到正确的区域，同时保留未修改的内容，即使在模糊指令下也是如此。现有的一次性编辑器将意图解析、空间接地和合成合并为单一步骤，经常导致部分执行失败，这在商业场景中是不可接受的。为了解决这个问题，我们引入了GMO-E$^2$DIT，一个代理编辑框架，它将视觉语言模型（VLM）与掩码条件图像编辑器相结合，以处理结构化的多轮任务完成。给定一个不明确的指令，VLM代理构建一个区域接地的编辑议程，有效地将认知推理与生成渲染解耦。然后，该框架通过操作感知掩码和参考执行子程序，利用反思驱动循环检查中间结果并确定后续状态。这种迭代机制可靠地保留安全的局部进展，重试未完成的操作，并从错误中恢复。此外，我们开发了一个统一的数据管道，为规划、执行和反思提供对齐的监督，以及EComEditBench，一个用于指令驱动评估的综合基准。大量实验表明，GMO-E$^2$DIT与强大的闭源模型相比取得了竞争性性能，在指令准确性和编辑保真度上优于现有基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
