A Jabbar, X Li, B Omar - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
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 …
TH Nguyen-Phuoc, C Richardt, L Mai… - Advances in neural …, 2020 - proceedings.neurips.cc
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 …
Y Gao, Y Zhou, J Wang, X Li… - Proceedings of the …, 2023 - openaccess.thecvf.com
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 …
We present SfSNet, an end-to-end learning framework for producing an accurate decomposition of an unconstrained human face image into shape, reflectance and …