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CREA: A Collaborative Multi-Agent Framework for Creative Image Editing and Generation

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Creativity in AI imagery remains a fundamental challenge, requiring not only the generation of visually compelling content but also the capacity to add novel, expressive, and artistically rich transformations to images. Unlike conventional editing tasks that rely on direct prompt-based modifications, creative image editing requires an autonomous, iterative approach that balances originality, coherence, and artistic intent. To address this, we introduce CREA, a novel multi-agent collaborative framework that mimics the human creative process. Our framework leverages a team of specialized AI agents who dynamically collaborate to conceptualize, generate, critique, and enhance images. Through extensive qualitative and quantitative evaluations, we demonstrate that CREA significantly outperforms state-of-the-art methods in diversity, semantic alignment, and creative transformation. To the best of our knowledge, this is the first work to introduce the task of creative editing.

Kavana Venkatesh, Connor Dunlop, Pinar Yanardag• 2025

Related benchmarks

TaskDatasetResultRank
Creative GenerationArchitecture Design
VLM Judge Score7.82
3
Creative Image GenerationArchitecture Design
Average Rank (User Study)2.19
3
Creative Image GenerationFurniture Design
User Study Average Rank2.27
3
Creative GenerationFurniture Design
VLM-as-a-Judge Score7.64
3
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