Viktor Matanski

Generative models can generate art within a single modality with high fidelity. However, translating a work of art from one domain to another (e.g., painting to music or poem to painting) in a meaningful way remains a longstanding, interdisciplinary challenge. We propose a novel approach combining a multi-agent system (MAS) architecture with an ontology-guided semantic representation to achieve cross-domain art translation while preserving the original artwork’s meaning and emotional impact. In our concept, specialized agents decompose the task: a Perception Agent extracts symbolic descriptors from the source artwork, a Translation Agent maps these descriptors using shared knowledge base, a Generator Agent creates the target-modality artwork, and a Curator Agent evaluates and refines the output for coherence and style alignment. This modular design, inspired by human creative workflows, allows complex artistic concepts (themes, moods, motifs) to carry over across modalities in a consistent and interpretable way. We implemented a prototype supporting translations between painting and poetry, leveraging state-of-the-art generative models. Preliminary results indicate that our ontology-driven MAS produces cross-domain translations that preserve key semantic elements and affective tone of the input, offering a new path toward explainable and controllable creative AI. Finally, we discuss a case study and potential applications from educational tools to synesthetic VR experiences and outline future research directions for enhancing the realm of intelligent agents.