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EmoAgent: A Multi-Agent Framework for Diverse Affective Image Manipulation
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AI
DOI：10.1109/taffc.2026.3672501.png)
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Abstract
En 中文
Affective Image Manipulation (AIM) aims to alter visual elements within an image to evoke specific emotional responses from viewers. However, existing AIM approaches rely on rigid one-to-one mappings between emotions and visual cues, making them ill-suited for the inherently subjective and diverse ways in which humans perceive and express emotion. To address this, we introduce a novel task setting termed Diverse AIM (D-AIM), aiming to generate multiple visually distinct yet emotionally consistent image edits from a single source image and target emotion. We propose EmoAgent, the first multi-agent framework tailored specifically for D-AIM. EmoAgent explicitly decomposes the manipulation process into three specialized phases executed by collaborative agents: a Planning Agent that generates diverse emotional editing strategies, an Editing Agent that precisely executes these strategies, and a Critic Agent that iteratively refines the results to ensure emotional accuracy. This collaborative design empowers EmoAgent to model one-to-many emotion-to-visual mappings, enabling semantically diverse and emotionally faithful edits. Extensive quantitative and qualitative evaluations demonstrate that EmoAgent substantially outperforms state-of-the-art approaches in both emotional fidelity and semantic diversity, effectively generating multiple distinct visual edits that convey the same target emotion.
Keywords:
Affective image manipulation
multi-agent collaboration
affective computing
visual diversity
emotionaware editing
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