Space Narrative: Generating Images and 3D Scenes of Chinese Garden from Text Using Deep Learning

The consistent mapping from poems to paintings is essential for the research and restoration of traditional Chinese gardens. But the lack of firsthand material is a great challenge to the reconstruction work. In this paper, we propose a method to generate garden paintings based on text descriptions using deep learning method. Our image-text pair dataset consists of more than one thousand Ming Dynasty Garden paintings and their inscriptions and postscripts. A latent text-to-image diffusion model learns the mapping from descriptive texts to garden paintings of the Ming Dynasty, and then the text description of Jichang Garden guides the model to generate new garden paintings. The cosine similarity between the guide text and the generated image is the evaluation criterion for the generated images. Our dataset is used to fine-tune the pre-trained diffusion model using Low-Rank Adaptation of Large Language Models (LoRA). We also transformed the generated images into a panorama and created a free-roam scene in Unity 3D. Our post-trained model is capable of generating garden images in the style of Ming Dynasty landscape paintings based on textual descriptions. The generated images are compatible with three-dimensional presentation in Unity 3D.