Text to 3D Model of Chinese Ancient Architecture

Abstract

Three-dimensional (3D) modeling is currently a creative task that requires modelers with strong professional skills and background knowledge, especially in the field of 3D modeling of Chinese ancient architecture (CAA). At present, most of the studies on 3D CAA modeling are based on hard-coded constructive rules, which need completed, complex and formalized descriptions. We present a generative system bridging the gap between the Chinese text and 3D models that allows users to generate 3D models by natural language. First, a Bayesian network is learned from existing CAA data to provide relationships of different structural components. Second, by parsing the Chinese text inputted by the user, key components of the CAA will be determined; and other matched structural components will be calculated by inferencing the trained Bayesian network. Third, the synthesis of all components is achieved by a proposed placement optimizing algorithm. Finally, we evaluate the effectiveness of the trained Bayesian network and demonstrate the application to generate 3D CAA model rapidly from the Chinese text.