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 …

Learning deformable 3D objects from 2D images is often an ill-posed problem. Existing
methods rely on explicit supervision to establish multi-view correspondences, such as …

The matching of 3D shapes has been extensively studied for shapes represented as surface
meshes, as well as for shapes represented as point clouds. While point clouds are a …

Traditional approaches for learning 3D object categories use either synthetic data or manual
supervision. In this paper, we propose a method which does not require manual annotations …

A major endeavor of computer vision is to represent, understand and extract structure from
3D data. Towards this goal, unsupervised learning is a powerful and necessary tool. Most …

We propose a novel generative adversarial network (GAN) for the task of unsupervised
learning of 3D representations from natural images. Most generative models rely on 2D …

Dense 3D object reconstruction from a single image has recently witnessed remarkable
advances, but supervising neural networks with ground-truth 3D shapes is impractical due to …

We present a unified framework tackling two problems: class-specific 3D reconstruction from
a single image, and generation of new 3D shape samples. These tasks have received …

We present a new framework to learn dense 3D reconstruction and correspondence from a
single 2D image. The shape is represented implicitly as deformation over a category-level …

We study the problem of 3D object generation. We propose a novel framework, namely 3D
Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic …