Research on Emotion-Based Inspiration Mechanism in Art Creation by Generative AI

Yuan-Chih Yu ()
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Yuan-Chih Yu: Department of Information Management, Chinese Culture University, Taipei 11114, Taiwan

Mathematics, 2025, vol. 13, issue 16, 1-22

Abstract: This research presents a generative AI mechanism designed to assist artists in finding inspiration and developing ideas during their creative process by leveraging their emotions as a driving force. The proposed iterative inspiration cycle, complete with feedback loops, helps artists digitally capture their creative emotions and use them as a guiding “vision” for creating artwork. Within the mechanism, the “Emotion Vision” images, generated from sketch line drawings and creative emotion prompts, are a medium designed to inspire artists. Experimental results demonstrate a positive inspirational effect, particularly in the creation of ‘Abstract Expressionism’ and ‘Impressionism’ artworks. In addition, we introduce the Emotion Vision Score metric, which quantifies the effectiveness of emotional inspiration. This metric evaluates how well “Emotion Vision” images inspire artists by balancing sketch intentions, creative emotions, and inspirational diversity, thus identifying the most effective images for inspiration. This novel mechanism integrates emotional intelligence into AI for art creation, allowing it to understand and replicate human emotion in its outputs. By enhancing emotional depth and ensuring consistency in generative AI, this research aims to advance digital art creation and contribute to the evolution of artistic expression through generative AI.

Keywords: art creation; inspiration; creative emotion; generative AI; artwork (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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