EmoWeave: Unified Affective Image Manipulation via Emotion-Aware Understanding and Generation

Affective Image Manipulation (AIM) aims to modify images according to emotional intents, enabling them to evoke desired affective responses. Existing methods typically focus on either appearance or semantic manipulation, require predefined target emotions, and lack explicit mechanisms for coordinating content and appearance edits toward a shared emotional goal. We introduce Unified Affective Image Manipulation (U-AIM), a task formulation that connects emotion intent inference, editing planning, and image editing. Given a source image with an optional target emotion, U-AIM supports both emotion enhancement and emotion migration by generating and executing coordinated content and appearance editing instructions. To provide supervision for this process, we propose Emotion-Grounded Retrieval Augmentation (EGRA), which retrieves emotion-relevant evidence conditioned on the source-image state and target emotion to construct grounded editing instructions. Combining affective image data with retouching data, we build UAIM-14K, linking source images, emotional intents, coordinated instructions, and edited results. We further introduce UAIMBench to evaluate both planning and end-to-end editing under user-specified and model-inferred emotional intents. Based on this supervision, we develop EmoWeave, a unified multimodal model that integrates emotional reasoning, instruction generation, and instruction-conditioned image editing within a single framework. Experiments on UAIMBench demonstrate that EmoWeave outperforms evaluated unified multimodal baselines and applicable AIM methods in both planning quality and end-to-end editing performance.