IEA: Amateur-Friendly Conversational Image Editing Agent via Three Stages of Multitask Alignment
arXiv:2606.08016v1 Announce Type: cross Abstract: Current image editing software often hinges on fixed filters or expert tuning, leaving a gap between amateur users' intent and outcomes. Creations by generative models may contain artifacts, implausible details, or stylistic drift away from photorealism and offer little insight into why an edit was made. We propose IEA, a conversational Image Editing Agent that learns to operate parameterized tools in an explicit, interpretable action space. IEA is trained via a three-stage multitask pipeline: (1) SFT on distilled expert edits, (2) GRPO with re
The proliferation of generative AI models for image creation has exposed the need for more user-friendly and interpretable editing tools, especially for non-experts.
This breakthrough addresses a significant usability gap, making advanced image editing accessible to a broader audience without requiring specialized skills, democratizing content creation.
Image editing moves beyond fixed filters and expert tuning towards more intuitive, conversational interfaces that allow users to express intent in natural language and understand the underlying actions.
- · AI software developers
- · Creative professionals (non-experts)
- · Generative AI platforms
- · Consumer electronics manufacturers
- · Traditional image editing software requiring deep expertise
- · Providers of fixed filter-based editing solutions
Increased amateur engagement in complex image editing tasks, leading to a surge in user-generated content.
Development of an ecosystem of 'skill sets' or 'tool packs' for conversational AI agents specific to various creative tasks, similar to app stores.
The blurring of lines between content creation and content editing, as highly capable agents handle iterative refinement based on high-level user directives.
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Read at arXiv cs.AI