Art Style

Art style analysis and generation are active research areas focusing on understanding and replicating artistic expression through computational methods. Current research employs diverse techniques, including diffusion models, variational autoencoders, and generative adversarial networks, often coupled with large language models for text-based control and style manipulation. This work aims to quantify and classify styles, generate novel styles from existing data, and improve the realism and controllability of AI-generated art, impacting fields like digital art creation, art history analysis, and the development of more sophisticated multimodal AI systems.