Summer Term 2019
Here you find the courses offered by the Chair for Computer Graphics.
Motion Synthesis for Virtual Characters
The focus of this seminar is on motion synthesis for virtual characters acting in virtual 3D scenes. In this context, the seminar focusses on data driven methods, which use motion capture data taken from human volunteers. The full range of relevant aspects of such approaches is offered to interested participants for overview presentations or for presentations on specialized topics.
Programmieren für Ingenieure
In dieser Vorlesung geht es darum mithilfe programmierbarer Arduino Mega Boards in die Programmierung einzusteigen. Der Fokus liegt dabei auf der Vermittlung grundlegender Konzepte der Programmierung und dem Realisieren von Projekten zum Erwerb von erster Programmierpraxis.
Realistic Image Synthesis
This advanced lecture discusses the mathematical concepts and algorithms that are used to simulate the propagation of light in a virtual scene. The topics include Monte Carlo sampling, various Global Illumination algorithms (from the basic Path Tracing algorithm to more advanced algorithms like Vertex Connection and Merging), and HDR imaging. In the practical exercises, the students implement some of the algorithms discussed in the lecture in a lightweight rendering framework.
In this seminar we will follow up on some rendering techniques related to rasterization and hardware-accelerated rendering. In CG1 we already touched the inner workings of today’s graphics APIs and hardware briefly. In this seminar students will further investigate specific parts of the rendering pipeline using a rasterization framework in order to implement respective rendering techniques.
Semantic Deep Learning
This seminar is concerned with selected methods and systems of semantic deep learning. Research on semantic deep learning mainly focuses on the task-oriented combination of deep learning with symbolic knowledge representation and reasoning in general, and semantic web technologies in particular. In addition, the rationalization and semantic explanation of deep learning models and results to the human user is of interest (explainable AI).