State-of-the-art models can perform well in controlled environments, but...
Self-supervised methods have been proven effective for learning deep
rep...
NeRF is a popular model that efficiently represents 3D objects from 2D
i...
Traditional 3D face models are based on mesh representations with textur...
Recently, generative models for 3D objects are gaining much popularity i...
Talking face generation has historically struggled to produce head movem...
Iris-based identification systems are among the most popular approaches ...
The tree-based ensembles are known for their outstanding performance for...
Contemporary deep neural networks offer state-of-the-art results when ap...
Few-shot models aim at making predictions using a minimal number of labe...
Gaussian Processes (GPs) have been widely used in machine learning to mo...
Generative models have gained many researchers' attention in the last ye...
Modern generative models achieve excellent quality in a variety of tasks...
Recently proposed 3D object reconstruction methods represent a mesh with...
Predicting future states or actions of a given system remains a fundamen...
Signed distance field (SDF) is a prominent implicit representation of 3D...
In this work, we present HyperFlow - a novel generative model that lever...
This paper focuses on a novel generative approach for 3D point clouds th...
In this work we introduce a novel approach to train Bidirectional Genera...
Deep generative architectures provide a way to model not only images, bu...
In this paper, we propose a novel regularization method for Generative
A...
Triplet networks are widely used models that are characterized by good
p...