Module Number

ML-4530 		Module Title

Deep Learning for Vision and Graphics 		Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Workload:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral Test
Content

The fields of 3D computer vision and graphics have been revolutionized by deep learning. For example, it is now possible to obtain detailed 3D reconstructions of humans and objects from single images, generate photo-realistic renderings of 3D scenes with neural networks, or manipulate and edit videos and images. In this seminar, we will cover the most recent publications and advances in the fields of neural rendering, 3D computer vision, 3D shape reconstruction, and representation learning for 3D shapes.
Objectives

Students are able to read and reflect upon current research papers in this research area. They can critically assess the contributions of such a paper. They can present current research results to other students and researchers and can lead research discussions.
Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Seminar
S
o
2
3.0
op
30
g
100
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Pons-Moll
Literature

Will be announced in the first meeting / Programming skills, knowledge of linear algebra and calculus, numerical optimization, probability theory.
Prior participation in one of: Deep Learning, Probabilistic ML, Mathematics for ML, Statistical ML is required.
Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV