Recent advancements in crowd simulation unravel a wide range of functionalities for virtual agents, delivering highly-realistic, natural virtual crowds. Such systems are of particular importance to a variety of applications ...

Additive manufacturing requires support structures to fabricate parts with overhangs. In this paper, we revisit a known support structure based on bridges-and-pillars (see Figure 1). The support structures are made of ...

Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to ...

Throughout the past decades, the graphics community has spent major resources on the research and development of physics simulators on the mission to computer-generate behaviors achieving outstanding visual effects or to ...

We observe that a Loop refinement step invariably splits halfedges into four new ones. We leverage this observation to formulate a breadth-first uniform Loop subdivision algorithm: Our algorithm iterates over halfedges to ...

Virtual reality can be used to support computer graphics teaching, e.g. by offering the chance to illustrate 3D processes that are difficult to convey. This paper describes the development and first evaluations of RePiX ...

Human body 3D reconstruction has a wide range of applications including 3D-printing, art, games, and even technical sport analysis. This is a challenging problem due to 2D ambiguity, diversity of human poses, and costs in ...

Path-Guiding algorithms for sampling scattering directions can drastically decrease the variance of Monte Carlo estimators of Light Transport Equation, but their production usage was limited to offline rendering because ...

Procedural modeling enables building complex geometric objects and scenes in a wide panel of applications. The traditional approach relies on the sequential application of a reduced set of construction rules. We offer to ...

Reinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environment. While learning, they repeatedly take ...