The paper "Efficient Geometry-aware 3D Generative Adversarial Networks" addresses the challenge of unsupervised generation of high-quality, multi-view-consistent images and 3D shapes from single-view 2D photographs. Existing 3D GANs either require excessive computational resources or make approximations that compromise multi-view consistency and shape quality. The authors introduce a hybrid explicit-implicit network architecture that improves computational efficiency and image quality without relying heavily on these approximations. By decoupling feature generation and neural rendering, the framework leverages state-of-the-art 2D CNN generators like StyleGAN2, inheriting their efficiency and expressiveness. The proposed method achieves state-of-the-art results in 3D-aware synthesis on datasets such as FFHQ and AFHQ Cats, generating high-quality 3D shapes and multi-view-consistent images. Key contributions include a tri-plane-based 3D GAN framework, a training strategy promoting multi-view consistency through dual discrimination and pose conditioning, and superior performance in image quality, view consistency, and geometry quality. The approach is efficient, expressive, and capable of handling complex scenes with high resolution and detail.The paper "Efficient Geometry-aware 3D Generative Adversarial Networks" addresses the challenge of unsupervised generation of high-quality, multi-view-consistent images and 3D shapes from single-view 2D photographs. Existing 3D GANs either require excessive computational resources or make approximations that compromise multi-view consistency and shape quality. The authors introduce a hybrid explicit-implicit network architecture that improves computational efficiency and image quality without relying heavily on these approximations. By decoupling feature generation and neural rendering, the framework leverages state-of-the-art 2D CNN generators like StyleGAN2, inheriting their efficiency and expressiveness. The proposed method achieves state-of-the-art results in 3D-aware synthesis on datasets such as FFHQ and AFHQ Cats, generating high-quality 3D shapes and multi-view-consistent images. Key contributions include a tri-plane-based 3D GAN framework, a training strategy promoting multi-view consistency through dual discrimination and pose conditioning, and superior performance in image quality, view consistency, and geometry quality. The approach is efficient, expressive, and capable of handling complex scenes with high resolution and detail.