3D Generative Network

The area of 3D generative network has been developing rapidly due in part to the progresses in generative models and 3D sensing technology. It has a wide range of applications in film and animation, video games, virtual reality, etc. Although many popular 3D generative networks are inspired from the 2D ones, they are significantly different. This is essentially because the representations of 3D data, particularly the non-Euclidean ones, cannot be directly processed by 2D generative networks. In this chapter, we first present an overview of 3D generative networks. Then, we introduce the common representations of 3D data, including Euclidean and non-Euclidean ones. Next, we present the mainstream methods for 3D generative networks categorised subject to the same taxonomy as the 2D generative models. Finally, we discuss the limitations of 3D generative networks and potential future work in this field.