Diffusion Probabilistic Model Assisted 3D Form Finding and Design Latent Space Exploration: A Case Study for Taihu Stone Spacial Transformation

Abstract

Taihu stone is an important landscape element in Chinese Private Garden on Southern Yangtze river, which is known for its profound cultural and aesthetic significance. In this paper, we intend to introduce the 3D spacial character of Taihu stone into architectural forms and spaces using machine learning, trying to explore the possibilities of AI-assisted 3D form finding and design latent space exploration. Existing spacial transformation of Taihu stone is mainly demonstrated by continuous section slicing, which cannot directly analyze and generate 3D space, thus cannot learn the most characteristic internal space of Taihu stone. This paper used the latest 3D point cloud probabilistic diffusion model to achieve 3D form generation and interpolation of Taihu stone and architectural massing through latent space exploration. Experiments show that a sufficiently trained diffusion model can generate 3D point clouds of Taihu stone and building massing, as well as generate interpolations between them. The latent vector can be manipulated to generate outputs that are more oriented towards the Taihu stone or the building massing, to meet the different needs of designers. Generated point clouds can be reconstructed into triangle meshes or voxelized, as a morphological prototype for further design implementation. Generated forms are capable to provide inspiration and reference for the designers to create free forms, showing the potential of the diffusion model to assist architecture design in conceptual phases.