The document discusses recovering 3D shape from 2D images using generative adversarial networks (GANs). It describes existing methods that use GANs to model 2D image manifolds and reconstruct 3D shapes. The proposed method uses a pre-trained 2D GAN and employs an ellipsoid shape prior to generate "pseudo samples" with different viewpoints and lighting. It then projects these into the GAN's image manifold to guide exploration of the object's 3D shape in an unsupervised manner, iteratively refining the shape estimate. Evaluation shows this allows recovering 3D shapes of objects like faces and cars directly from a single 2D image without using external 3D models.