computer vision

Unsupervised Learning of 3D Object Categories from Videos in the Wild

Our goal is to learn a deep network that, given a small number of images of an object of a given category, reconstructs it in 3D. While several recent works have obtained analogous results using synthetic data or assuming the availability of 2D …

Generative Modelling of BRDF Textures from Flash Images

We learn a latent space for easy capture, semantic editing, consistent interpolation, and efficient reproduction of visual material appearance. When users provide a photo of a stationary natural material captured under flash light illumination, it is …

Learning a Neural 3D Texture Space from 2D Exemplars

We propose a generative model of 2D and 3D natural textures with diversity, visual fidelity and at high computational efficiency. This is enabled by a family of methods that extend ideas from classic stochastic procedural texturing (Perlin noise) to …

Escaping Plato's Cave: 3D Shape From Adversarial Rendering

We introduce PlatonicGAN to discover the 3D structure of an object class from an unstructured collection of 2D images, i.e., where no relation between photos is known, except that they are showing instances of the same category}. The key idea is to …