Siyu Tang: Catalogue data in Autumn Semester 2022

Name	Prof. Dr. Siyu Tang
Field	Computer Vision
Address	Professur für Computer Vision ETH Zürich, CNB G 104 Universitätstrasse 6 8092 Zürich SWITZERLAND
E-mail	siyu.tang@inf.ethz.ch
URL	https://vlg.inf.ethz.ch
Department	Computer Science
Relationship	Assistant Professor (Tenure Track)

Number	Title	ECTS	Hours	Lecturers
252-5701-00L	Seminar in Advanced Topics in Vision Number of participants limited to 24. The deadline for deregistering expires at the end of the third week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.	2 credits	2S	M. Pollefeys, S. Tang
Abstract	This seminar covers advanced topics in computer vision, such as 3D reconstruction, image understanding, object detection, people tracking, motion prediction, and other related topics. Each time the course is offered, a collection of research papers is selected and each student presents one paper to the class and leads a discussion about the paper and related topics.
Learning objective	The goal is to get an in-depth understanding of actual problems and research topics in the field of computer vision as well as improve presentations and critical analysis skills.
Content	This seminar covers advanced topics in computer vision by reading and presenting classic and state-of-the-art papers. Each time the course is offered, a collection of research papers are selected covering topics such as 3D reconstruction, image understanding, object detection, people tracking, motion prediction and others. Each student presents one paper to the class and leads a discussion about the paper and related topics. All students read the papers and participate in the discussion.
Lecture notes	no script
Literature	Individual research papers are selected each term.
263-5702-00L	Seminar on Digital Humans Number of participants limited to 24. The deadline for deregistering expires at the end of the third week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.	2 credits	2S	M. Gross, B. Solenthaler, S. Tang, R. Wampfler
Abstract	This seminar covers advanced topic in digital humans with a focus on the latest research results. Topics include estimating human pose and motion from images, human motion synthesis, learning-based human avatar creation, learning neural implicit representations for humans, modeling, animations, artificial intelligence for digital characters, and others. A collection of research papers is selected.
Learning objective	The goal is to get an overview of actual research topics in the field of digital humans and to improve presentation and critical analysis skills.
Content	This seminar covers advanced topics in digital humans including both seminal research papers as well as the latest research results. A collection of research papers are selected covering topics such as estimating human pose and motion from images, human motion synthesis, learning-based human avatar creation, learning neural implicit representations for humans, modeling, animations, artificial intelligence for digital characters, and others. Each student presents one paper to the class and leads a discussion about the paper. All students read the papers and participate in the discussion.
Literature	Individual research papers are selected each term. See https://vlg.inf.ethz.ch/ and http://graphics.ethz.ch/ for example papers.
263-5902-00L	Computer Vision	8 credits	3V + 1U + 3A	M. Pollefeys, S. Tang, F. Yu
Abstract	The goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises.
Learning objective	The objectives of this course are: 1. To introduce the fundamental problems of computer vision. 2. To introduce the main concepts and techniques used to solve those. 3. To enable participants to implement solutions for reasonably complex problems. 4. To enable participants to make sense of the computer vision literature.
Content	Camera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition
Prerequisites / Notice	It is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course.
264-5800-20L	Doctoral Seminar in Visual Computing (HS22)	1 credit	1S	D. B. Baráth, M. Gross, M. Pollefeys, B. Solenthaler, O. Sorkine Hornung, S. Tang
Abstract	In this doctoral seminar, current research at the Institute for Visual Computing will be presented and discussed. The goal is to learn about current research projects at our institute, to strengthen our expertise in the field, to provide a platform where research challenges caThis graduate seminar provides doctoral students in computer science a chance to read and discuss current research papers.
Learning objective	In this doctoral seminar, current research at the Institute for Visual Computing will be presented and discussed. The goal is to learn about current research projects at our institute, to strengthen our expertise in the field, to provide a platform where research challenges can be discussed, and also to practice scientific presentations.
Content	Current research at the IVC will be presented and discussed.
Prerequisites / Notice	This course requires solid knowledge in the area of Computer Graphics and Computer Vision as well as state-of-the-art research.