Technical University of Munich

Winter Semester 2023/24, TU München

Organisers: Dominik Schnaus, Tarun Yenamandra

Please direct questions to fmcv-ws23@cvpr.in.tum.de

Course Materials: Course Materials (password protected; please email us or look in the preliminary meeting slides for the password)

2023-07-11: Last date for registration via email and TUM Matching: 25.07.2023

2023-07-11: [preliminary meeting] will take place at 11:00 - 12:00 on 11.07.2023 online via zoom. The slides will be published in a protected page linked from here. Please attend the meeting to learn more about the course or ask questions. The zoom link can be found on TUMonline.

Large-scale machine learning models, also called foundational models, have proven helpful in many applications. As they are learned from large amounts of data, they can be used as priors in many applications. Different machine learning paradigms, such as transformers, and diffusion models, have been used to learn such foundational models. While the major focus has been on 2D foundational models, some methods have also been proposed to extend the 2D foundational models to 3D using differentiable rendering. In this seminar, we will discuss some of the important foundational models in recent years and their applications.

All participants should have a solid working knowledge of linear algebra and calculus. In addition, it is useful (but not required), that students have a background in one of the following topics: computer vision, image processing, computer graphics, 3D geometry, and deep learning.