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Data-driven intelligent sci-fi color design: clustering to generative validation
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DOI：10.1186/s13662-025-04041-4.png)
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Abstract
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This study applies deep learning to Sci-Fi color scheme design. Specifically, we first integrate multi-source Sci-Fi visuals. Sources include online platforms, original works, and Midjourney. On this basis, we build an HSV color dataset via K-means clustering. The dataset has 108 discrete categories. We then analyze core characteristics. Key findings show cool-color dominance and monochromatic preference. Based on these identified features, we train a VAE model. It generates characteristic-aligned color schemes. Subsequently, we validate schemes through Midjourney. Implement palette-to-rendering control. This breaks traditional experience-driven design limits. It establishes a scientifically reusable cross-modal methodology. This methodology serves visual computational aesthetics. The framework delivers efficient intelligent color solutions. Solutions target film and gaming industries.
Keywords:
Sci-Fi
Color design
K-means
Variational Autoencoder
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