Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

The Generative Models have gained considerable attention in unsupervised learning via a
new and practical framework called Generative Adversarial Networks (GAN) due to their …

Volumetric neural rendering methods, such as neural ra-diance fields (NeRFs), have
enabled photo-realistic novel view synthesis. However, in their standard form, NeRFs do not …

Modeling and rendering of dynamic scenes is challenging, as natural scenes often contain
complex phenomena such as thin structures, evolving topology, translucency, scattering …

Several recent works have shown how highly realistic human head images can be obtained
by training convolutional neural networks to generate them. In order to create a personalized …

We propose a method to learn 3D deformable object categories from raw single-view
images, without external supervision. The method is based on an autoencoder that factors …

Talking head generation is to generate video based on a given source identity and target
motion. However, current methods face several challenges that limit the quality and …

Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

We present BlockGAN, an image generative model that learns object-aware 3D scene
representations directly from unlabelled 2D images. Current work on scene representation …

We present a learning framework that learns to recover the 3D shape, pose and texture from
a single image, trained on an image collection without any ground truth 3D shape, multi …