Landscape Painting

Research on landscape painting generation leverages deep learning, particularly generative adversarial networks (GANs) and diffusion models like Stable Diffusion, to create realistic and stylistically consistent images, often focusing on specific cultural styles such as Chinese landscape painting. Current efforts concentrate on improving controllability, allowing for generation based on textual descriptions or reference images, and enhancing the realism and artistic merit of the output. This work has implications for art restoration, accessibility (e.g., creating tactile representations for the visually impaired), and a deeper understanding of cross-cultural aesthetic perception through multimodal data analysis.