Controllable Chinese landscape art creation using adversarially regularized autoencoder

Chinese landscape painting represents a rich artistic tradition that spans over a millennium, reflecting the complex interplay of philosophy, nature, and cultural expression distinctive to China. However, as fewer contemporary artists master these traditional techniques, there is growing concern about preserving and transmitting this cultural knowledge to future generations. Recent advancements in artificial intelligence present new opportunities for addressing this challenge through the preservation and creative evolution of this art form. In this article, we propose a deep learning framework for generating Chinese landscape artworks, leveraging a curated image-text dataset of high-quality traditional works. Our architecture is designed to combine the advantages of both generative adversarial networks and variational autoencoders, achieving improved stylistic control and visual fidelity. We introduce a guided image generation approach that enables fine-grained manipulation of composition and style through embedding blending mechanisms. Experimental evaluations show that our model outperforms state-of-the-art baselines including StyleGAN2, SAPGAN, and CycleGAN, achieving superior Fréchet Inception Distance (FID) scores. The proposed approach not only demonstrates technical advances in generative modeling but also opens new avenues for digital preservation, aesthetic education, and contemporary reinterpretation of Chinese landscape art.