3D Generative Adversarial Network

3D Generative Adversarial Networks (GANs) aim to generate realistic three-dimensional objects and scenes from data, often 2D images, by leveraging adversarial training between a generator and discriminator network. Current research focuses on improving efficiency and realism, exploring architectures like Gaussian splatting for faster rendering and incorporating multi-modal conditioning (e.g., text, images) for greater control over generation and editing. These advancements are significant for applications ranging from creating realistic avatars and 3D models to synthesizing medical images for research and augmenting datasets in areas with limited data availability.