Search Results for author:
Found 12 papers, 8 papers with code
Learning Neural Light Transport
In recent years, deep generative models have gained significance due to their ability to synthesize natural-looking images with applications ranging from virtual reality to data augmentation for training computer vision models.
Learning Implicit Surface Light Fields
In this work, we propose a novel implicit representation for capturing the visual appearance of an object in terms of its surface light field.
Convolutional Occupancy Networks
Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction.
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
In this work, we propose a differentiable rendering formulation for implicit shape and texture representations.
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain.
Texture Fields: Learning Texture Representations in Function Space
A major reason for these limitations is that common representations of texture are inefficient or hard to interface for modern deep learning techniques.
Occupancy Networks: Learning 3D Reconstruction in Function Space
With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity.
Which Training Methods for GANs do actually Converge?
In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent.
Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes
Further, we demonstrate the utility of our approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenes.
The Numerics of GANs
In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs).
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
We show that in the nonparametric limit our method yields an exact maximum-likelihood assignment for the parameters of the generative model, as well as the exact posterior distribution over the latent variables given an observation.
Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring
We present a new notion of probabilistic duality for random variables involving mixture distributions.
