3D Aware GAN

3D-aware Generative Adversarial Networks (GANs) aim to generate realistic images with inherent 3D consistency, enabling multi-view rendering and manipulation. Current research focuses on improving the quality and detail of generated 3D assets, often using neural radiance fields (NeRFs) or generative radiance manifolds, while minimizing reliance on extensive multi-view datasets and addressing challenges like view consistency and object-level editing. These advancements are significant for applications such as creating realistic avatars, enhancing image editing capabilities, and improving 3D model generation from limited data, impacting fields ranging from computer graphics to medical imaging.