3d Gan

3D Generative Adversarial Networks (GANs) aim to generate realistic three-dimensional objects and scenes from two-dimensional image data, overcoming limitations of traditional 3D modeling. Current research focuses on improving the efficiency and quality of 3D generation, exploring architectures like Gaussian splatting, neural radiance fields, and layered surface volumes to achieve high-resolution, multi-view consistent outputs. These advancements are impacting diverse fields, including medical imaging (e.g., super-resolution and synthetic data generation), avatar creation, and novel view synthesis, offering powerful tools for data augmentation and content creation.