MooD: Perception-Enhanced Efficient Affective Image Editing via Continuous Valence-Arousal Modeling

TL;DR

MooD is a novel framework for affective image editing that uses continuous Valence-Arousal modeling to enable fine-grained, efficient, and controllable modifications across diverse scenarios.

Contribution

It introduces a VA-aware retrieval strategy, visual transfer, and semantic guidance, along with a new VA-annotated dataset, AffectSet, for improved emotion-driven image editing.

Findings

MooD outperforms existing methods in affective controllability and visual fidelity.

The framework maintains high efficiency in image editing tasks.

Ablation studies highlight key factors influencing performance.

Abstract

Affective Image Editing (AIE) aims to modify visual content to evoke targeted emotions. Although current approaches achieve impressive editing quality, they often overlook inference efficiency, which limits their applicability in computational social scenarios. Moreover, most methods depend on discrete emotion representations, which hinder the continuous modeling of complex human emotions and constrain expressive capabilities in interactive scenarios. To tackle these gaps, we propose MooD, the first framework that directly leverages continuous Valence-Arousal (VA) values as editing instruction for fine-grained and efficient AIE in computational social systems. Specifically, we first introduce a VA-Aware retrieval strategy to bridge vague affective values and detailed visual semantics. Building upon this, MooD integrates visual transfer and perception-enhanced semantic guidance to achieve controllable AIE. Furthermore, considering that existing VA-annotated datasets mainly focus on social scenarios and largely overlook natural scenes, we therefore construct AffectSet, a comprehensive VA-annotated dataset covering diverse scenarios, to support model optimization and evaluation. Extensive qualitative and quantitative experimental results demonstrate that our MooD achieves superior performance in both affective controllability and visual fidelity while maintaining high efficiency. A series of ablation studies further reveal the crucial factors of our design.