3DTGC: Text generation of Chinese painting elements based on 3D reconstruction

Given the limited availability of multi-view information, research on 3D data and 3D reconstruction of Chinese paintings is scarce. Therefore, this paper proposes an innovative method called 3DTGC for the 3D reconstruction of Chinese painting elements generated from text. First, a text-to-image module generates images of Chinese painting elements from text. Then, a multi-view generation module handles multi-view synthesis. Through a series of preprocessing steps, this module creates six fixed-view images from a single image, which are used to generate a local light field fusion dataset. Next, the generated local light field fusion dataset is input into a neural radiation field synthesis module for further optimization of 3D information representation. Finally, a detailed mesh structure is created in the mesh generation module based on the results of the first two steps. Compared with several state-of-the-art 3D reconstruction methods, this framework demonstrates significant advantages in both visualization and technical performance. Furthermore, 3DTGC effectively addresses the multifaceted problem commonly encountered by other algorithms when processing traditional Chinese painting data, thereby improving reconstruction quality and robustness.