Point patterns are characterized by their density and correlation. While...
Gradient-based optimization is now ubiquitous across graphics, but
unfor...
Neural fields are evolving towards a general-purpose continuous
represen...
Current differentiable renderers provide light transport gradients with
...
We introduce 3inGAN, an unconditional 3D generative model trained from 2...
Differentiable rasterization changes the common formulation of primitive...
We propose a method to accelerate the joint process of physically acquir...
In many recent works, multi-layer perceptions (MLPs) have been shown to ...
We propose a relighting method for outdoor images. Our method mainly foc...
There currently are two main approaches to reproducing visual appearance...
Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of ...
We tackle the problem of generating novel-view images from collections o...
Appropriate weight initialization has been of key importance to successf...
Computer-Generated Holography (CGH) offers the potential for genuine,
hi...
Density estimation plays a crucial role in many data analysis tasks, as ...
Our goal is to learn a deep network that, given a small number of images...
We learn a latent space for easy capture, semantic editing, consistent
i...
Controlled capture of real-world material appearance yields tabulated se...
We propose Blue Noise Plots, two-dimensional dot plots that depict data
...
We seek to reconstruct sharp and noise-free high-dynamic range (HDR) vid...
Previous work has demonstrated learning isolated 3D objects (voxel grids...
The motion of picking up and placing an object in 3D space is full of su...
We suggest to represent an X-Field -a set of 2D images taken across diff...
Proteins perform a large variety of functions in living organisms, thus
...
Massive semantic labeling is readily available for 2D images, but much h...
We propose a generative model of 2D and 3D natural textures with diversi...
We suggest representing light field (LF) videos as "one-off" neural netw...
We suggest representing light field (LF) videos as "one-off" neural netw...
We show that denoising of 3D point clouds can be learned unsupervised,
d...
Image metrics predict the perceived per-pixel difference between a refer...
We develop PlatonicGAN to discover 3D structure of an object class from ...
We suggest a method to directly deep-learn light transport, i. e., the
m...
Sample patterns have many uses in Computer Graphics, ranging from proced...
We suggest a rasterization pipeline tailored towards the need of head-mo...
We propose an efficient and effective method to learn convolutions for
n...
Convolutional neural networks (CNNs) handle the case where filters exten...
We propose a deep representation of appearance, i. e. the relation of co...
Faithful manipulation of shape, material, and illumination in 2D Interne...
As many different 3D volumes could produce the same 2D x-ray image, inve...
Photographers routinely compose multiple manipulated photos of the same ...
How much does a single image reveal about the environment it was taken i...
In this paper we are extracting surface reflectance and natural environm...
In computer vision, convolutional neural networks (CNNs) have recently
a...
Taking an image of an object is at its core a lossy process. The rich
in...
Undoing the image formation process and therefore decomposing appearance...
Object class detection has been a synonym for 2D bounding box localizati...