3D GANs and Latent Space: A comprehensive survey
It provides a comprehensive overview for researchers in 3D graphics and generative modeling, but it is incremental as it synthesizes existing knowledge without novel findings.
This survey explores the latent space and 3D GANs, examining GAN variants and training methods to improve 3D GAN training and suggesting future research directions, but it does not present new experimental results or concrete numbers.
Generative Adversarial Networks (GANs) have emerged as a significant player in generative modeling by mapping lower-dimensional random noise to higher-dimensional spaces. These networks have been used to generate high-resolution images and 3D objects. The efficient modeling of 3D objects and human faces is crucial in the development process of 3D graphical environments such as games or simulations. 3D GANs are a new type of generative model used for 3D reconstruction, point cloud reconstruction, and 3D semantic scene completion. The choice of distribution for noise is critical as it represents the latent space. Understanding a GAN's latent space is essential for fine-tuning the generated samples, as demonstrated by the morphing of semantically meaningful parts of images. In this work, we explore the latent space and 3D GANs, examine several GAN variants and training methods to gain insights into improving 3D GAN training, and suggest potential future directions for further research.