  Affective Image Manipulation (AIM) seeks to modify user-provided images to
evoke specific emotional responses. This task is inherently complex due to its
twofold objective: significantly evoking the intended emotion, while preserving
the original image composition. Existing AIM methods primarily adjust color and
style, often failing to elicit precise and profound emotional shifts. Drawing
on psychological insights, we extend AIM by incorporating content modifications
to enhance emotional impact. We introduce EmoEdit, a novel two-stage framework
comprising emotion attribution and image editing. In the emotion attribution
stage, we leverage a Vision-Language Model (VLM) to create hierarchies of
semantic factors that represent abstract emotions. In the image editing stage,
the VLM identifies the most relevant factors for the provided image, and guides
a generative editing model to perform affective modifications. A ranking
technique that we developed selects the best edit, balancing between emotion
fidelity and structure integrity. To validate EmoEdit, we assembled a dataset
of 416 images, categorized into positive, negative, and neutral classes. Our
method is evaluated both qualitatively and quantitatively, demonstrating
superior performance compared to existing state-of-the-art techniques.
Additionally, we showcase EmoEdit's potential in various manipulation tasks,
including emotion-oriented and semantics-oriented editing.
