Generating 3D Reconstructions Using Generative Models

As the capacity for visual representation continues to evolve, there is a growing need for techniques for realistic and efficient creation of three-dimensional objects. Generative models, particularly Generative Adversarial Networks, Variational Autoencoders and novel methods of Text-to-3D, utilize textual descriptions to generate 3D reconstructions with high-quality geometry and disentangled materials. In this chapter, we present an in-depth exploration of the application of generative models in 3D reconstruction. We begin by discussing the theoretical underpinnings of these models and their applicability to 3D reconstruction. This chapter studies how these models learn to generate new instances from a given distribution. We end with discussions of potential future directions and the broader impacts of these technologies in various industries.