DIGAN: distillation model for generating 3D-aware Terracotta Warrior faces

Abstract Utilizing Generative Adversarial Networks (GANs) to generate 3D representations of the Terracotta Warriors offers a novel approach for the preservation and restoration of cultural heritage. Through GAN technology, we can produce complete 3D models of the Terracotta Warriors’ faces, aiding in the repair of damaged or partially destroyed figures. This paper proposes a distillation model, DIGAN, for generating 3D Terracotta Warrior faces. By extracting knowledge from StyleGAN2, we train an innovative 3D generative network. G2D, the primary component of the generative network, produces detailed and realistic 2D images. The 3D generator modularly decomposes the generation process, covering texture, shape, lighting, and pose, ultimately rendering 2D images of the Terracotta Warriors’ faces. The model enhances the learning of 3D shapes through symmetry constraints and multi-view data, resulting in high-quality 2D images that closely resemble real faces. Experimental results demonstrate that our method outperforms existing GAN-based generation methods.