Sampling from known probability distributions is a ubiquitous task in computational science,
underlying calculations in domains from linguistics to biology and physics. Generative …

The success of machine learning (ML) techniques in the formerly difficult areas of data
analysis and pattern extraction has led to their widespread incorporation into various …

Neural graphics primitives, parameterized by fully connected neural networks, can be costly
to train and evaluate. We reduce this cost with a versatile new input encoding that permits …

Most existing geometry processing algorithms use meshes as the default shape
representation. Manipulating meshes, however, requires one to maintain high quality in the …

We present a real-time neural radiance caching method for path-traced global illumination.
Our system is designed to handle fully dynamic scenes, and makes no assumptions about …

We present a method for reducing errors---variance and bias---in physically based
differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as …

We present a complete system for real-time rendering of scenes with complex appearance
previously reserved for offline use. This is achieved with a combination of algorithmic and …

The world is full of materials with interesting small-scale structure: a green pasture
consisting of millions of individual blades of grass, a scratched and partially rusted metallic …

We investigate the use of neural fields for modelling diverse mesoscale structures, such as
fur, fabric and grass. Instead of using classical graphics primitives to model the structure, we …

We introduce Neural Radiosity, an algorithm to solve the rendering equation by minimizing
the norm of its residual, similar as in classical radiosity techniques. Traditional basis …