SeCo: Semantic-Guided Multimodal Color Splash Effects

Color splash is a widely used image editing effect that highlights selected regions by retaining color while rendering the rest of the image in grayscale. However, existing tools often struggle with achieving high precision, efficiency, and user flexibility in controlling the effect. In this article, we propose Semantic-Guided Multimodal Color Splash Effects (SeCo), a novel framework for generating stylized and customizable color splash effects from natural language instructions and color palettes. SeCo decomposes the task into two key components: Semantic-Guided Object Isolation (SGOI) and Palette-Driven Color Adjustment (PDCA). SGOI accurately identifies and isolates user-referred objects with fine-grained transparency, while the PDCA module recolors the isolated regions under user-specified palette guidance. Our approach supports arbitrary object selection, handles transparency, and enables diverse stylization patterns. Experimental results on both synthetic and real-world datasets demonstrate that SeCo outperforms existing methods in precision and controllability, offering a practical and expressive solution for visual editing and content creation.