A 3D-GAN is a GAN architecture for 3D shape generation. 3D shape generation is typically a complex problem, due to the complexities involved in processing 3D images. A 3D-GAN is a solution that can generate realistic and varied 3D shapes and was introduced by Jiajun Wu, Chengkai Zhang, Tianfan Xue, and others in the paper titled Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. This paper is available at http://3dgan.csail.mit.edu/papers/3dgan_nips.pdf. In this chapter, we will implement a 3D-GAN using the Keras framework.
We will cover the following topics:
- Introduction to the basics of 3D-GANs
- Setting up the project
- Preparing the data
- A Keras implementation of a 3D-GAN
- Training the 3D-GAN
- Hyperparameter optimization
- Practical applications of 3D-GANs
