| Description | This hands-on lecture focuses on cutting-edge 3D reconstruction approaches, including volumetric fusion approaches on implicit functions, face tracking, hand tracking, and much more! We also cover essential optimization techniques such as Gauss-Newton and Levenberg-Marquardt. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner TAs: M.Sc. Artem Sevastopolsky, M.Sc. Lukas Höllein, M.Sc. Andrei Burov |
| Description | This practical offers a deep-dive into recent advances in 3D scanning and spatial machine learning. Projects span the whole semester and the objective can cover a variety of computer vision problems as large-scale 3D scanning, body tracking, face reconstruction and much more! |
|---|---|
| Contact | M.Sc. Jiapeng Tang, M.Sc. Peter Kocsis |
| Description | Advanced Deep Learning. This lecture focuses on cutting edge Deep Learning techniques for computer vision with a heavy focus on Neural Rendering, Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and many more topics related to the visual computing lab. Part of the lecture is a semester-long project with a deep dive on modern DL methods. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner TAs: M.Sc. Dave Zhenyu Chen, Dr. Yinyu Nie, M.Sc. Barbara Roessle, M.Sc. David Rozenberszki |
| Description | This practical offers a deep stint into recent advances in Deep Learning and AI. Over the course we will carefully look into recent research highlights and implement hands-on state-of-the-art algorithms through engaging group projects. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner |
| Description | Introduction to Deep Learning. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs). |
|---|---|
| Contact | Prof. Dr. Angela Dai TAs: M.Sc. Junwen Huang, M.Sc. Yujin Chen, M.Sc. Manuel Dahnert |
| Description | This is the research seminar of the Visual Computing Group. We discuss ongoing work in the direction of Neural Radiance Fields (NeRFs). |
|---|---|
| Contact | M.Sc. Guy Gafni, M.Sc. Yawar Siddiqui |
| Description | This colloquium invites prominent researchers to present new and exciting work across various fields in computer vision and artificial intelligence. |
|---|---|
| Contact | M.Eng. Dejan Azinović |
| Description | This is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning. |
|---|---|
| Contact | M.Sc. Norman Müller, M.Sc. Shivangi Aneja |
| Description | This hands-on lecture focuses on cutting-edge 3D reconstruction approaches, including volumetric fusion approaches on implicit functions, face tracking, hand tracking, and much more! We also cover essential optimization techniques such as Gauss-Newton and Levenberg-Marquardt. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner TAs: M.Sc. Artem Sevastopolsky, M.Sc. Lukas Höllein, M.Sc. Andrei Burov |
| Description | This practical offers a deep-dive into recent advances in 3D scanning and spatial machine learning. Projects span the whole semester and the objective can cover a variety of computer vision problems as large-scale 3D scanning, body tracking, face reconstruction and much more! |
|---|---|
| Contact | M.Sc. Peter Kocsis, M.Sc. Jiapeng Tang |
| Description | Advanced Deep Learning. This lecture focuses on cutting edge Deep Learning techniques for computer vision with a heavy focus on Statistical Background, Recurrent Neural Networks (RNNs), and Generative Models (GANs). Part of the lecture is a semester-long project with a deep dive on modern DL methods. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner TAs: M.Sc. Dave Zhenyu Chen, Dr. Yinyu Nie, M.Sc. Barbara Roessle |
| Description | This practical offers a deep stint into recent advances in Deep Learning and AI. Over the course we will carefully look into recent research highlights and implement hands-on state-of-the-art algorithms through engaging group projects. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner |
| Description | This colloquium invites prominent researchers to present new and exciting work across various fields in computer vision and artificial intelligence. |
|---|---|
| Contact | M.Eng. Dejan Azinović |
| Description | This is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning. |
|---|---|
| Contact | M.Sc. Norman Müller, M.Sc. Shivangi Aneja |
| Description | This practical offers a deep-dive into recent advances in 3D scanning and spatial machine learning. Projects span the whole semester and the objective can cover a variety of computer vision problems as large-scale 3D scanning, body tracking, face reconstruction and much more! |
|---|---|
| Contact | M.Sc. Aljaž Božič, M.Sc. Pablo Palafox |
| Description | Advanced Deep Learning. This lecture focuses on cutting edge Deep Learning techniques for computer vision with a heavy focus on Neural Rendering, Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and many more topics related to the visual computing lab. Part of the lecture is a semester-long project with a deep dive on modern DL methods. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner TAs: M.Sc. Dave Zhenyu Chen, M.Sc. Hao Yu, M.Sc. Barbara Roessle |
| Description | This practical offers a deep stint into recent advances in Deep Learning and AI. Over the course we will carefully look into recent research highlights and implement hands-on state-of-the-art algorithms through engaging group projects. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner |
| Description | Introduction to Deep Learning. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs). |
|---|---|
| Contact | Prof. Dr. Matthias Nießner |
| Description | This colloquium invites prominent researchers to present new and exciting work across various fields in computer vision and artificial intelligence. |
|---|---|
| Contact | M.Eng. Dejan Azinović |
| Description | This is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning. |
|---|---|
| Contact | M.Sc. Norman Müller, M.Sc. Shivangi Aneja |
| Description | This practical offers a deep-dive into recent advances in 3D scanning and spatial machine learning. Projects span the whole semester and the objective can cover a variety of computer vision problems as large-scale 3D scanning, body tracking, face reconstruction and much more! |
|---|---|
| Contact | M.Sc. Aljaž Božič, M.Sc. Pablo Palafox |
| Description | Advanced Deep Learning. This lecture focuses on cutting edge Deep Learning techniques for computer vision with a heavy focus on Statistical Background, Recurrent Neural Networks (RNNs), and Generative Models (GANs). Part of the lecture is a semester-long project with a deep dive on modern DL methods. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner TAs: M.Sc. Dave Zhenyu Chen, M.Sc. Hao Yu, M.Sc. Barbara Roessle |
| Description | This practical offers a deep stint into recent advances in Deep Learning and AI. Over the course we will carefully look into recent research highlights and implement hands-on state-of-the-art algorithms through engaging group projects. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner |
| Description | Introduction to Deep Learning. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs). |
|---|---|
| Contact | Prof. Dr. Matthias Nießner TAs: M.Sc. Guy Gafni, M.Sc. Manuel Dahnert |
| Description | This colloquium invites prominent researchers to present new and exciting work across various fields in computer vision and artificial intelligence. |
|---|---|
| Contact | M.Eng. Dejan Azinović |
| Description | This is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning. |
|---|---|
| Contact | M.Sc. Norman Müller, M.Sc. Shivangi Aneja |
| Description | This hands-on lecture focuses on cutting-edge 3D reconstruction approaches, including volumetric fusion approaches on implicit functions, face tracking, hand tracking, and much more! We also cover essential optimization techniques such as Gauss-Newton and Levenberg-Marquardt. |
|---|---|
| Contact | Dr. Justus Thies, Prof. Dr. Angela Dai TAs: M.Sc. Andrei Burov, M.Sc. Felix AltenBerger |
| Description | This practical offers a deep-dive into recent advances in 3D scanning and spatial machine learning. Projects span the whole semester and the objective can cover a variety of computer vision problems as large-scale 3D scanning, body tracking, face reconstruction and much more! |
|---|---|
| Contact | Dr. Justus Thies, M.Sc. Manuel Dahnert, M.Sc. Pablo Palafox |
| Description | Advanced Deep Learning. This lecture focuses on cutting edge Deep Learning techniques for computer vision with a heavy focus on Statistical Background, Recurrent Neural Networks (RNNs), and Generative Models (GANs). Part of the lecture is a semester-long project with a deep dive on modern DL methods. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner, Prof. Dr. Laura Leal-Taixé TAs: M.Sc. Yawar Siddiqui, M.Sc. Dave Zhenyu Chen, M.Sc. Guillem Braso, M.Sc. Ismail Elezi |
| Description | This practical offers a deep stint into recent advances in Deep Learning and AI. Over the course we will carefully look into recent research highlights and implement hands-on state-of-the-art algorithms through engaging group projects. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner |
| Description | Introduction to Deep Learning. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs). |
|---|---|
| Contact | Prof. Dr. Matthias Nießner, Prof. Dr. Laura Leal-Taixé TAs: M.Sc. Andreas Rössler, M.Sc. Franziska Gerken |
| Description | This is a virtual talk series on various topics in computer vision and artificial intelligence. Our invited speakers are experts in their field and have already received international recognition for their work. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner, M.Eng. Dejan Azinović |
| Description | This is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning. |
|---|---|
| Contact | M.Sc. Norman Müller, M.Sc. Christian Diller, Dr. Justus Thies |
| Description | This hands-on lecture focuses on cutting-edge 3D reconstruction approaches, including volumetric fusion approaches on implicit functions, face tracking, hand tracking, and much more! We also cover essential optimization techniques such as Gauss-Newton and Levenberg-Marquardt. |
|---|---|
| Contact | Dr. Justus Thies, Prof. Dr. Angela Dai TAs: M.Eng. Dejan Azinović, M.Sc. Manuel Dahnert |
| Description | This practical offers a deep-dive into recent advances in 3D scanning and spatial machine learning. Projects span the whole semester and the objective can cover a variety of computer vision problems as large-scale 3D scanning, body tracking, face reconstruction and much more! |
|---|---|
| Contact | Dr. Justus Thies, M.Sc. Andrei Burov, M.Sc. Pablo Palafox |
| Description | Advanced Deep Learning. This lecture focuses on cutting edge Deep Learning techniques for computer vision with a heavy focus on Statistical Background, Recurrent Neural Networks (RNNs), and Generative Models (GANs). Part of the lecture is a semester-long project with a deep dive on modern DL methods. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner, Prof. Dr. Laura Leal-Taixé TAs: M.Sc. Ji Hou, M.Sc. Dave Zhenyu Chen, M.Sc. Tim Meinhardt, M.Sc. Maxim Maximov |
| Description | This practical offers a deep stint into recent advances in Deep Learning and AI. Over the course we will carefully look into recent research highlights and implement hands-on state-of-the-art algorithms through engaging group projects. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner |
| Description | Introduction to Deep Learning. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs). |
|---|---|
| Contact | Prof. Dr. Matthias Nießner, Prof. Dr. Laura Leal-Taixé TAs: M.Sc. Andreas Rössler, M.Sc. Patrick Dendorfer |
| Description | This is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning. |
|---|---|
| Contact | M.Sc. Norman Müller, M.Sc. Christian Diller, Dr. Justus Thies |
| Description | This hands-on lecture focuses on cutting-edge 3D reconstruction approaches, including volumetric fusion approaches on implicit functions, face tracking, hand tracking, and much more! We also cover essential optimization techniques such as Gauss-Newton and Levenberg-Marquardt. |
|---|---|
| Contact | Dr. Justus Thies, Prof. Dr. Angela Dai TAs: M.Eng. Dejan Azinović, M.Sc. Manuel Dahnert |
| Description | This practical offers a deep-dive into recent advances in 3D scanning and spatial machine learning. Projects span the whole semester and the objective can cover a variety of computer vision problems as large-scale 3D scanning, body tracking, face reconstruction and much more! |
|---|---|
| Contact | Dr. Justus Thies, M.Eng. Armen Avetisyan, M.Sc. Aljaž Božič |
| Description | Advanced Deep Learning. This lecture focuses on cutting edge Deep Learning techniques for computer vision with a heavy focus on Statistical Background, Recurrent Neural Networks (RNNs), and Generative Models (GANs). Part of the lecture is a semester-long project with a deep dive on modern DL methods. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner, Prof. Dr. Laura Leal-Taixé TAs: M.Sc. Ji Hou, M.Sc. Dave Zhenyu Chen |
| Description | Introduction to Deep Learning. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs). |
|---|---|
| Contact | Prof. Dr. Matthias Nießner, Prof. Dr. Angela Dai TAs: M.Sc. Andreas Rössler, M.Sc. Yawar Siddiqui |
| Description | This is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning. |
|---|---|
| Contact | M.Sc. Norman Müller, M.Eng. Dejan Azinović, Dr. Justus Thies |
| Description | This hands-on lecture focuses on cutting-edge 3D reconstruction approaches, including volumetric fusion approaches on implicit functions, face tracking, hand tracking, and much more! We also cover essential optimization techniques such as Gauss-Newton and Levenberg-Marquardt. |
|---|---|
| Contact | Dr. Justus Thies, Prof. Dr. Angela Dai TAs: M.Eng. Dejan Azinović, M.Sc. Manuel Dahnert |
| Description | Advanced Deep Learning. This lecture focuses on cutting edge Deep Learning techniques for computer vision with a heavy focus on Statistical Background, Recurrent Neural Networks (RNNs), and Generative Models (GANs). Part of the lecture is a semester-long project with a deep dive on modern DL methods. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner, Prof. Dr. Laura Leal-Taixé TAs: M.Sc. Ji Hou, M.Sc. Andreas Rössler, M.Sc. Tim Meinhardt |
| Description | Introduction to Deep Learning. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs). |
|---|---|
| Contact | Prof. Dr. Matthias Nießner, Prof. Dr. Laura Leal-Taixé TAs: M.Sc. Andreas Rössler, M.Sc. Tim Meinhardt |
| Description | This is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning. |
|---|---|
| Contact | M.Eng. Armen Avetisyan, M.Sc. Aljaž Božič |
| Description | This hands-on lecture focuses on cutting-edge 3D reconstruction approaches, including volumetric fusion approaches on implicit functions, face tracking, hand tracking, and much more! We also cover essential optimization techniques such as Gauss-Newton and Levenberg-Marquardt. |
|---|---|
| Contact | Prof. Dr. Angela Dai, Dr. Justus Thies TAs: M.Eng. Dejan Azinović, M.Eng. Armen Avetisyan |
| Description | In this course, students will investigate recent research topics about 3D Scanning & Motion Capturing and present and summarize a chosen paper. |
|---|---|
| Contact | M.Sc. Aljaž Božič, M.Sc. Manuel Dahnert |
| Description | Advanced Deep Learning. This lecture focuses on cutting edge Deep Learning techniques for computer vision with a heavy focus on Statistical Background, Recurrent Neural Networks (RNNs), and Generative Models (GANs). Part of the lecture is a semester-long project with a deep dive on modern DL methods. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner, Prof. Dr. Laura Leal-Taixé TAs: M.Sc. Ji Hou, M.Sc. Andreas Rössler, M.Sc. Tim Meinhardt |
| Description | Introduction to Deep Learning. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs). |
|---|---|
| Contact | Prof. Dr. Laura Leal-Taixé, Prof. Dr. Matthias Nießner TAs: M.Sc. Ji Hou, M.Sc. Andreas Rössler, M.Sc. Tim Meinhardt |
| Description | This is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning. |
|---|---|
| Contact | Dr. Justus Thies, Prof. Dr. Matthias Nießner |
| Description | This hands-on lecture focuses on cutting-edge 3D reconstruction approaches, including volumetric fusion approaches on implicit functions, face tracking, hand tracking, and much more! We also cover essential optimization techniques such as Gauss-Newton and Levenberg-Marquardt. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner TAs: M.Sc. Aljaž Božič, M.Eng. Armen Avetisyan |
| Description | Introduction to Deep Learning for Computer Vision. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs). |
|---|---|
| Contact | Prof. Dr. Laura Leal-Taixé, Prof. Dr. Matthias Nießner TAs: M.Sc. Thomas Frerix, M.Sc. Tim Meinhardt |
| Description | Introduction to Deep Learning. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs). |
|---|---|
| Contact | Prof. Dr. Matthias Nießner, Prof. Dr. Laura Leal-Taixé TAs: M.Sc. Ji Hou, M.Sc. Andreas Rössler, M.Sc. Tim Meinhardt |
| Description | This is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning. |
|---|---|
| Contact | Dr. Justus Thies, Prof. Dr. Matthias Nießner |
| Description | This hands-on lecture focuses on cutting-edge 3D reconstruction approaches, including volumetric fusion approaches on implicit functions, face tracking, hand tracking, and much more! We also cover essential optimization techniques such as Gauss-Newton and Levenberg-Marquardt. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner TAs: Dr. Justus Thies, M.Sc. Aljaž Božič |
| Description | Introduction to Deep Learning for Computer Vision. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs). |
|---|---|
| Contact | Prof. Dr. Matthias Nießner, Prof. Dr. Laura Leal-Taixé TAs: M.Sc. Thomas Frerix, M.Sc. Ji Hou, M.Sc. Andreas Rössler, M.Sc. Tim Meinhardt |
| Description | This is the research seminar of the Visual Computing Group. We discuss hot trends and ongoing work in computer vision, graphics, and machine learning. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner |
| Description | Introduction to Deep Learning for Computer Vision. This lecture focuses on modern machine learning techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Models (GANs). |
|---|---|
| Contact | Prof. Dr. Laura Leal-Taixé, Prof. Dr. Matthias Nießner TAs: M.Sc. Thomas Frerix, M.Sc. Tim Meinhardt |
| Description | In this course, students will investigate recent research about machine learning techniques in the computer graphics area. Independent investigation for further reading, critical analysis, and evaluation of the topic are required. |
|---|---|
| Contact | Prof. Dr. Nils Thuerey, Prof. Dr. Rüdiger Westermann, Prof. Dr. Matthias Nießner, Dr. Kiwon Um |