3D Mesh Generation by Introducing Extended Attentive Normalization

In recent years, research on conditional image generation using GANs of the type where conditions are given by class labels or texts has been successful. On the other hand, the generation of conditional 3D models consisting of 3D meshes is still in its infancy. In this research, we add global information based on Attentive Normalization to local information using CNN to improve 3D mesh generation. Specifically, we propose Conditional Attentive Normalization, which is an extension of Attentive Normalization and can add conditional information. Comparative experiments conditioned by class labels and texts have been carried out by using Caltech-UCSD Birds-200-201. It turns out that our proposed method outperforms the conventional methods.