Suchergebnis: Katalogdaten im Frühjahrssemester 2023

Informatik Master Information
Vertiefungen
Vertiefung in Visual and Interactive Computing
Kernfächer
NummerTitelTypECTSUmfangDozierende
263-5806-00LDigital Humans Information
Previously Computational Models of Motion and Virtual Humans
W8 KP3V + 2U + 2AS. Coros, S. Tang
KurzbeschreibungThis course covers the core technologies required to model and simulate motions for digital humans and robotic characters. Topics include kinematic modeling, physics-based simulation, trajectory optimization, reinforcement learning, feedback control for motor skills, motion capture, data-driven motion synthesis, and ML-based generative models. They will be richly illustrated with examples.
LernzielStudents will learn how to estimate human pose, shape, and motion from videos and create basic human avatars from various visual inputs. Students will also learn how to represent and algorithmically generate motions for digital characters and their real-life robotic counterparts. The lectures are accompanied by four programming assignments (written in python or C++) and a capstone project.

The deadline to cancel/deregister from the course is May 1st. Deregistration after the deadline will lead to fail.
Inhalt- Basic concept of 3D representations
- Human body/hand models
- Human motion capture;
- Non-​rigid surface tracking and reconstruction
- Neural rendering
- Optimal control and trajectory optimization
- Physics-based modeling for multibody systems
- Forward and inverse kinematics
- Rigging and keyframing
- Reinforcement learning for locomotion
Voraussetzungen / BesonderesExperience with python and C++ programming, numerical linear algebra, multivariate calculus and probability theory. Some background in deep learning, computer vision, physics-based modeling, kinematics, and dynamics is preferred.
KompetenzenKompetenzen
Fachspezifische KompetenzenVerfahren und Technologiengeprüft
Wahlfächer
NummerTitelTypECTSUmfangDozierende
252-0312-00LMobile Health and Activity Monitoring Information W6 KP2V + 3AC. Holz
KurzbeschreibungHealth and activity monitoring has become a key purpose of mobile & wearable devices, e.g., phones, watches, and rings. We will cover the phenomena they capture, i.e., user behavior, actions, and human physiology, as well as the sensors, signals, and methods for processing and analysis.

For the exercise, students will receive a wristband to stream and analyze activity and health signals.
LernzielThe course will combine high-level concepts with low-level technical methods needed to sense, detect, and understand them.

High-level:
– sensing modalities for interactive systems
– "activities" and "events" (exercises and other mechanical activities such as movements and resulting vibrations)
– health monitoring (basic cardiovascular physiology)
– affective computing (emotions, mood, personality)

Lower-level:
– sampling and filtering, time and frequency domains
– cross-modal sensor systems, signal synchronization and correlation
– event detection, classification, prediction using basic signal processing as well as learning-based methods
– sensor types: optical, mechanical/acoustic, electromagnetic
InhaltHealth and activity monitoring has become a key purpose of mobile and wearable devices, including phones, (smart) watches, (smart) rings, (smart) belts, and other trackers (e.g., shoe clips, pendants). In this course, we will cover the fundamental aspects that these devices observe, i.e., user behavior, actions, and physiological dynamics of the human body, as well as the sensors, signals, and methods to capture, process, and analyze them. We will then cover methods for pattern extraction and classification on such data. The course will therefore touch on aspects of human activities, cardiovascular and pulmonary physiology, affective computing (recognizing, interpreting, and processing emotions), corresponding lower-level sensing systems (e.g., inertial sensing, optical sensing, photoplethysmography, eletrodermal activity, electrocardiograms) and higher-level computer vision-based sensing (facial expressions, motions, gestures), as well as processing methods for these types of data.

The course will be accompanied by a group exercise project, in which students will apply the concepts and methods taught in class. Students will receive a wearable wristband device that streams IMU data to a mobile phone (code will be provided for receiving, storing, visualizing on the phone). Throughout the course and exercises, we will collect data of various human activities from the band, annotate them, analyze, classify, and interpret them. For this, existing and novel processing methods will be developed (plenty of related work exists), based on the collected data as well as existing datasets. We will also combine the band with signals obtained from the mobile phone to holistically capture and analyze health and activity data.

Full details: https://teaching.siplab.org/mobile_health_activity_monitoring/2023/

Note: All lectures will be streamed live and recorded for later replay. Hybrid participation will be possible.
SkriptCopies of slides will be made available
Lectures will be streamed live as well as recorded and made available online.

More information on the course site: https://teaching.siplab.org/mobile_health_activity_monitoring/2023/

Note: All lectures will be streamed live and recorded for later replay. Hybrid participation will be possible.
LiteraturWill be provided in the lecture
KompetenzenKompetenzen
Fachspezifische KompetenzenKonzepte und Theoriengeprüft
Verfahren und Technologiengeprüft
Methodenspezifische KompetenzenAnalytische Kompetenzengeprüft
Entscheidungsfindunggeprüft
Medien und digitale Technologiengeprüft
Problemlösunggeprüft
Soziale KompetenzenKooperation und Teamarbeitgeprüft
Sensibilität für Vielfalt geprüft
Persönliche KompetenzenAnpassung und Flexibilitätgeprüft
Kreatives Denkengeprüft
Kritisches Denkengeprüft
252-0579-00L3D Vision Information W5 KP3G + 1AM. Pollefeys, D. B. Baráth
KurzbeschreibungThe course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual odometry (VO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization.
LernzielAfter attending this course, students will:
1. understand the core concepts for recovering 3D shape of objects and scenes from images and video.
2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications.
3. have a good overview over the current state-of-the art in 3D vision.
4. be able to critically analyze and asses current research in this area.
InhaltThe goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications.
252-5706-00LMathematical Foundations of Computer Graphics and Vision Information W5 KP2V + 1U + 1AT. Aydin, A. Djelouah
KurzbeschreibungThis course presents the fundamental mathematical tools and concepts used in computer graphics and vision. Each theoretical topic is introduced in the context of practical vision or graphic problems, showcasing its importance in real-world applications.
LernzielThe main goal is to equip the students with the key mathematical tools necessary to understand state-of-the-art algorithms in vision and graphics. In addition to the theoretical part, the students will learn how to use these mathematical tools to solve a wide range of practical problems in visual computing. After successfully completing this course, the students will be able to apply these mathematical concepts and tools to practical industrial and academic projects in visual computing.
InhaltThe theory behind various mathematical concepts and tools will be introduced, and their practical utility will be showcased in diverse applications in computer graphics and vision. The course will cover topics in sampling, reconstruction, approximation, optimization, robust fitting, differentiation, quadrature and spectral methods. Applications will include 3D surface reconstruction, camera pose estimation, image editing, data projection, character animation, structure-aware geometry processing, and rendering.
263-5052-00LInteractive Machine Learning: Visualization & Explainability Information Belegung eingeschränkt - Details anzeigen W5 KP3G + 1AM. El-Assady
KurzbeschreibungVisual Analytics supports the design of human-in-the-loop interfaces that enable human-machine collaboration. In this course, will go through the fundamentals of designing interactive visualizations, later applying them to explain and interact with machine leaning models.
LernzielThe goal of the course is to introduce techniques for interactive information visualization and to apply these on understanding, diagnosing, and refining machine learning models.
InhaltInteractive, mixed-initiative machine learning promises to combine the efficiency of automation with the effectiveness of humans for a collaborative decision-making and problem-solving process. This can be facilitated through co-adaptive visual interfaces.

This course will first introduce the foundations of information visualization design based on data charecteristics, e.g., high-dimensional, geo-spatial, relational, temporal, and textual data.

Second, we will discuss interaction techniques and explanation strategies to enable explainable machine learning with the tasks of understanding, diagnosing, and refining machine learning models.

Tentative list of topics:
1. Visualization and Perception
2. Interaction and Explanation
3. Systems Overview
SkriptCourse material will be provided in form of slides.
LiteraturWill be provided during the course.
Voraussetzungen / BesonderesBasic understanding of machine learning as taught at the Bachelor's level.
263-5701-00LScientific Visualization Information W5 KP2V + 1U + 1AM. Gross, T. Günther
KurzbeschreibungThis lecture provides an introduction into visualization of scientific and abstract data.
LernzielThis lecture provides an introduction into the visualization of scientific and abstract data. The lecture introduces into the two main branches of visualization: scientific visualization and information visualization. The focus is set onto scientific data, demonstrating the usefulness and necessity of computer graphics in other fields than the entertainment industry. The exercises contain theoretical tasks on the mathematical foundations such as numerical integration, differential vector calculus, and flow field analysis, while programming exercises familiarize with the Visualization Tool Kit (VTK). In a course project, the learned methods are applied to visualize one real scientific data set. The provided data sets contain measurements of volcanic eruptions, galaxy simulations, fluid simulations, meteorological cloud simulations and asteroid impact simulations.
InhaltThis lecture opens with human cognition basics, and scalar and vector calculus. Afterwards, this is applied to the visualization of air and fluid flows, including geometry-based, topology-based and feature-based methods. Further, the direct and indirect visualization of volume data is discussed. The lecture ends on the viualization of abstract, non-spatial and multi-dimensional data by means of information visualization.
Voraussetzungen / BesonderesFundamentals of differential calculus. Knowledge on numerical mathematics, computer algebra systems, as well as ordinary and partial differential equations is an asset, but not required.
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