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MooD framework enables efficient, fine-grained affective image editing

Researchers have introduced MooD, a novel framework for affective image editing that utilizes continuous Valence-Arousal (VA) values for more nuanced emotional control. This approach addresses limitations in existing methods that rely on discrete emotion representations and often lack efficiency. MooD integrates a VA-Aware retrieval strategy and combines visual transfer with semantic guidance to achieve controllable and efficient image editing, supported by a new VA-annotated dataset called AffectSet. AI

Summary written by gemini-2.5-flash-lite from 3 sources. How we write summaries →

IMPACT Introduces a new method for fine-grained emotional control in image editing, potentially improving creative tools and user experience.

RANK_REASON The cluster contains an academic paper detailing a new framework for image editing.

Read on arXiv cs.CV →

COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 ·

    MooD: An Efficient VA-Driven Affective Image Editing Framework via Fine-Grained Semantic Control

    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 c…

  2. arXiv cs.CV TIER_1 · Xinyi Yin, Yiduo Wang, Tingqi Hu, Meicong Si, Yunyun Shi, Shi Chen, Hao Wang, Junxiao Xue, Xuecheng Wu ·

    MooD: An Efficient VA-Driven Affective Image Editing Framework via Fine-Grained Semantic Control

    arXiv:2605.02521v1 Announce Type: new Abstract: 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 lim…

  3. arXiv cs.CV TIER_1 · Xuecheng Wu ·

    MooD: An Efficient VA-Driven Affective Image Editing Framework via Fine-Grained Semantic Control

    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 c…