Stelian Coros: Katalogdaten im Frühjahrssemester 2023 |
| Name | Herr Prof. Dr. Stelian Coros |
| Lehrgebiet | Computergestützte Robotik |
| Adresse | Computergestützte Robotik ETH Zürich, WWA H 22 Wasserwerkstrasse 10 / 12 8092 Zürich SWITZERLAND |
| stelian.coros@inf.ethz.ch | |
| URL | http://crl.ethz.ch/index.html |
| Departement | Informatik |
| Beziehung | Ausserordentlicher Professor |
| Nummer | Titel | ECTS | Umfang | Dozierende | |||||
|---|---|---|---|---|---|---|---|---|---|
| 263-5806-00L | Digital Humans  Previously Computational Models of Motion and Virtual Humans | 8 KP | 3V + 2U + 2A | S. Coros, S. Tang | |||||
| Kurzbeschreibung | This 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. | ||||||||
| Lernziel | Students 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 / Besonderes | Experience 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. | ||||||||
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