This course covers fundamentals of physics-based modelling and numerical optimization from the perspective of character animation and robotics applications. The methods discussed in class derive their theoretical underpinnings from applied mathematics, control theory and computational mechanics, and they will be richly illustrated with examples.

This course covers the core technologies required to model and simulate motions for digital humans. The curriculum includes human body modeling, human motion capture, data-driven human motion synthesis, and ML-based generative models. Each topic will be extensively illustrated with examples to provide a comprehensive understanding of the subject matter.

Recent developments in neural networks have drastically advanced the performance of machine perception systems in a variety of areas including computer vision, robotics, and human shape modeling.This course is a deep dive into deep learning algorithms and architectures with applications to a variety of perceptual and generative tasks.

This lecture provides an introduction to physically-based animation in computer graphics and gives an overview of fundamental methods and algorithms. The practical exercises include three assignments which are to be solved in small groups. In an addtional course project, topics from the lecture will be implemented into a 3D game or a comparable application.

This course acquaints students with core knowledge in computer graphics, image processing, multimedia and computer vision. Topics include: Graphics pipeline, perception and camera models, transformation, shading, global illumination, texturing, sampling, filtering, image representations, image and video compression, edge detection and optical flow.