Monte Carlo (MC) integration is used ubiquitously in realistic image synthesis because of its
flexibility and generality. However, the integration has to balance estimator bias and …

Research on neural radiance fields (NeRFs) for novel view generation is exploding with new
models and extensions. However, a question that remains unanswered is what happens in …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

This paper presents a novel generative model to synthesize fluid simulations from a set of
reduced parameters. A convolutional neural network is trained on a collection of discrete …

We propose a temporally coherent generative model addressing the super-resolution
problem for fluid flows. Our work represents a first approach to synthesize four-dimensional …

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 …

The wide adoption of path‐tracing algorithms in high‐end realistic rendering has stimulated
many diverse research initiatives. In this paper we present a coherent survey of methods that …

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 …

Clouds play an important role in enhancing the realism of outdoor scenes in computer
graphics (CG). Realistic cloud generation is a challenging task, which entails processes …

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 …