In this paper, we propose an algorithm for fully automatic neural face swapping in images and videos. To the best of our knowledge, this is the first method capable of rendering photo-realistic and temporally coherent ...

We propose a novel approach for denoising Monte Carlo path traced images, which uses data from individual samples rather than relying on pixel aggregates. Samples are partitioned into layers, which are filtered separately, ...

Recently, deep learning-based denoising approaches have led to dramatic improvements in low sample-count Monte Carlo rendering. These approaches are aimed at path tracing, which is not ideal for simulating challenging light ...

Several fast global illumination algorithms rely on the Virtual Point Lights framework. This framework separates illumination into two steps: first, propagate radiance in the scene and store it in virtual lights, then ...

In this paper, we provide a comprehensive theory of anti-aliasing sampling patterns that explains and revises known results, and introduce a variational optimization framework to generate point patterns with any desired ...

We propose a novel image-driven fitting strategy for isotropic BRDFs. Whereas existing BRDF fitting methods minimize a cost function directly on the error between the fitted analytical BRDF and the measured isotropic BRDF ...

We introduce a novel semi-procedural approach that avoids drawbacks of procedural textures and leverages advantages of datadriven texture synthesis. We split synthesis in two parts: 1) structure synthesis, based on a ...