Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics. In this paper, we focus on 3D deformable shapes that share a common topological structure, such as human faces and bodies. Morphable Models and their variants, despite their linear formulation, have been widely used for shape representation, while most of the recently proposed nonlinear approaches resort to intermediate representations, such as 3D voxel grids or 2D views. In this work, we introduce a novel graph convolutional operator, acting directly on the 3D mesh, that explicitly models the inductive bias of the fixed underlying graph. This is achieved by enforcing consistent local orderings of the vertices of the graph, throu...
With the rise of deep neural networks a number of approaches for learning over 3D data have gained p...
Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep lear...
Deep generative models have de facto emerged as state of the art when it comes to density estimation...
In recent years, there has been a surge in the availability of 3D sensors, leading to an exponential...
We propose Geometric Neural Parametric Models (GNPM), a learned parametric model that takes into acc...
International audienceConvolutional neural networks (CNNs) have massively impacted visual recogniti...
The recent trend in deep learning methods for 3D point cloud understanding is to propose increasingl...
While deep learning has been successfully applied to many tasks in computer graphics and vision, sta...
Convolutional neural networks have achieved extraordinary results in many computer vision and patter...
Deep learning has achieved tremendous progress and success in processing images and natural language...
th the rise of deep neural networks a number of approaches for learning over 3D data have gained pop...
Convolutional Neural Networks have revolutionized vision applications. There are image domains and r...
Reconstructing 3D objects from 2D images is both challenging for our brains and machine learning alg...
Solving geometric tasks involving point clouds by using machine learning is a challenging problem. S...
Graduation date:2017Reasoning about 3D shape of objects is important for successful computer vision\...
With the rise of deep neural networks a number of approaches for learning over 3D data have gained p...
Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep lear...
Deep generative models have de facto emerged as state of the art when it comes to density estimation...
In recent years, there has been a surge in the availability of 3D sensors, leading to an exponential...
We propose Geometric Neural Parametric Models (GNPM), a learned parametric model that takes into acc...
International audienceConvolutional neural networks (CNNs) have massively impacted visual recogniti...
The recent trend in deep learning methods for 3D point cloud understanding is to propose increasingl...
While deep learning has been successfully applied to many tasks in computer graphics and vision, sta...
Convolutional neural networks have achieved extraordinary results in many computer vision and patter...
Deep learning has achieved tremendous progress and success in processing images and natural language...
th the rise of deep neural networks a number of approaches for learning over 3D data have gained pop...
Convolutional Neural Networks have revolutionized vision applications. There are image domains and r...
Reconstructing 3D objects from 2D images is both challenging for our brains and machine learning alg...
Solving geometric tasks involving point clouds by using machine learning is a challenging problem. S...
Graduation date:2017Reasoning about 3D shape of objects is important for successful computer vision\...
With the rise of deep neural networks a number of approaches for learning over 3D data have gained p...
Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep lear...
Deep generative models have de facto emerged as state of the art when it comes to density estimation...