AI RESEARCH
MooD: An Efficient VA-Driven Affective Image Editing Framework via Fine-Grained Semantic Control
arXiv CS.CV
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ArXi:2605.02521v1 Announce Type: new Affective image editing (AIE) aims to edit visual content to evoke target emotions. However, existing methods often overlook inference efficiency and predominantly depend on discrete emotion representations, which to some extent limits their practical applicability and makes it challenging to capture complex and subtle human emotions. To tackle these gaps, we propose MooD, the first framework that directly leverages continuous Valence-Arousal (VA) values for fine-grained and efficient AIE. Specifically, we first