| Description | This lecture covers the essentials of neural network training, spanning backpropagation, optimization, and regularization strategies. It examines core architectures, specifically Convolutional Neural Networks, Recurrent Neural Networks, and Transformers, alongside their applications. The course concludes with advanced generative models, including Autoencoders, GANs, and Diffusion. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner TAs: M.Sc. Yujin Chen, M.Sc. Haoxuan Li, Chandan Yeshwanth |
| Description | This lecture focuses on cutting-edge deep learning techniques for computer vision. Topics evolve each semester to reflect current research trends and include modern generative models as well as best practices for training and analyzing deep networks. The lecture is complemented by a semester-long project with a deep dive into state-of-the-art visual computing methods. |
|---|---|
| Contact | Prof. Dr. Matthias Nießner TAs: M.Sc. Ziya Erkoç, M.Sc. Nicolas von Lützow, M.Sc. Jonathan Schmidt, M.Sc. Bartłomiej Baranowski |
| 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. Katharina Schmid, M.Sc. Marc Benedí San Millán |
| Description | In this seminar course, students will investigate recent research in generative AI and world models, covering (but not limited to) the following topics: diffusion models for images, video, and 3D, autoregressive transformer models, large-scale reconstruction methods, and learned world models. |
|---|---|
| Contact | M.Sc. Yu Chi |
| 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 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. Simon Giebenhain, M.Sc. Antonio Oroz |
| Description | This practical course offers a comprehensive, hands-on exploration of modern generative and reconstructive AI methods for modeling visual and physical environments. Project topics cover diffusion models, world models, feed-forward reconstruction methods, and scene generation techniques within a semester-long project. |
|---|---|
| Contact | M.Sc. Umut Kocasarı, M.Sc. Robin Borth |
| Description | This colloquium invites prominent researchers to present new and exciting work across various fields in computer vision and artificial intelligence. |
|---|---|
| Contact | M.Sc. Jonathan Schmidt |
| 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. Katharina Schmid, M.Sc. Marc Benedí San Millán, M.Sc. Jiapeng Tang |
| 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. Simon Giebenhain, M.Sc. Tobias Kirschstein |
| 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. Ziya Erkoç, M.Sc. Lukas Höllein, M.Sc. Nicolas von Lützow |
| 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. Yujin Chen, M.Sc. Haoxuan Li, Chandan Yeshwanth |
| Description | This is the research seminar of the Visual Computing Group. We discuss ongoing work in the direction of Neural Radiance Fields (NeRFs) and Gaussian Splatting (GS). |
|---|---|
| Contact | M.Sc. Peter Kocsis, M.Sc. Yu Chi, M.Sc. Jiapeng Tang |
| Description | This colloquium invites prominent researchers to present new and exciting work across various fields in computer vision and artificial intelligence. |
|---|---|
| Contact | M.Sc. Jonathan Schmidt |
| Description | In this course, the students will autonomously investigate recent research on fundamental concepts as well as recently developed 3D Computer Vision techniques. Independent investigation for further reading, critical analysis, and evaluation of the topic is required. |
|---|---|
| Contact | M.Sc. Peter Kocsis, M.Sc. Yu Chi, M.Sc. Jiapeng Tang |
| 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. Marc Benedí San Millán, M.Sc. Jiapeng Tang |
| 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. Simon Giebenhain, M.Sc. Tobias Kirschstein |
| 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. David Rozenberszki, Dr. Lei Li, M.Sc. Ziya Erkoç, M.Sc. Lukas Höllein |
| 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 is the research seminar of the Visual Computing Group. We discuss ongoing work in the direction of Neural Radiance Fields (NeRFs) and Gaussian Splatting (GS). |
|---|---|
| Contact | M.Sc. Jiapeng Tang, M.Sc. Can Gümeli |
| Description | This colloquium invites prominent researchers to present new and exciting work across various fields in computer vision and artificial intelligence. |
|---|---|
| Contact | M.Sc. Jonathan Schmidt |
| Description | In this course, the students will autonomously investigate recent research on fundamental concepts as well as recently developed 3D Computer Vision techniques. Independent investigation for further reading, critical analysis, and evaluation of the topic is required. |
|---|---|
| Contact | M.Sc. Peter Kocsis, M.Sc. Jonathan Schmidt |
| 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. Marc Benedí San Millán, M.Sc. Haoxuan Li |
| 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. Simon Giebenhain, M.Sc. Tobias Kirschstein |
| 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. David Rozenberszki, Dr. Lei Li, M.Sc. Ziya Erkoç, M.Sc. Lukas Höllein |
| 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 is the research seminar of the Visual Computing Group. We discuss ongoing work in the direction of Neural Radiance Fields (NeRFs). |
|---|---|
| Contact | M.Sc. Jiapeng Tang |
| Description | This colloquium invites prominent researchers to present new and exciting work across various fields in computer vision and artificial intelligence. |
|---|---|
| Contact | M.Sc. Jonathan Schmidt |
| Description | In this course, the students will autonomously investigate recent research on fundamental concepts as well as recently developed 3D Computer Vision techniques. Independent investigation for further reading, critical analysis, and evaluation of the topic is required. |
|---|---|
| Contact | M.Sc. Peter Kocsis, M.Sc. Jonathan Schmidt |
| 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. Marc Benedí San Millán |
| 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. Simon Giebenhain, M.Sc. Tobias Kirschstein |
| 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. David Rozenberszki, Dr. Lei Li, M.Sc. Barbara Rössle, M.Sc. Ziya Erkoç |
| 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. Yujin Chen, M.Sc. Haoxuan Li, 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. Jiapeng Tang |
| Description | This colloquium invites prominent researchers to present new and exciting work across various fields in computer vision and artificial intelligence. |
|---|---|
| Contact | M.Sc. Shivangi Aneja |
| Description | In this course, the students will autonomously investigate recent research on fundamental concepts as well as recently developed 3D Computer Vision techniques. Independent investigation for further reading, critical analysis, and evaluation of the topic is required. |
|---|---|
| Contact | M.Sc. Peter Kocsis, 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. Marc Benedí San Millán |
| 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. Simon Giebenhain, M.Sc. Tobias Kirschstein |
| 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. Barbara Rössle, M.Sc. David Rozenberszki, M.Sc. Ziya Erkoç, Dr. Lei Li |
| 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. Yujin Chen, M.Sc. Guy Gafni, 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. Yawar Siddiqui, M.Sc. Shenhan Qian |
| Description | This colloquium invites prominent researchers to present new and exciting work across various fields in computer vision and artificial intelligence. |
|---|---|
| Contact | M.Sc. Shivangi Aneja |
| Description | In this course, the students will autonomously investigate recent research on fundamental concepts as well as recently developed 3D Computer Vision techniques. Independent investigation for further reading, critical analysis, and evaluation of the topic is required. |
|---|---|
| Contact | M.Sc. Shivangi Aneja, M.Sc. Peter Kocsis |
| 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. Simon Giebenhain, M.Sc. Tobias Kirschstein |
| 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 Rössle, 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. Matthias Nießner TAs: M.Sc. Junwen Huang, M.Sc. Yujin Chen, M.Sc. Guy Gafni, 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. Yawar Siddiqui, M.Sc. Shenhan Qian |
| Description | This colloquium invites prominent researchers to present new and exciting work across various fields in computer vision and artificial intelligence. |
|---|---|
| Contact | M.Sc. Shivangi Aneja |
| Description | In this course, the students will autonomously investigate recent research on fundamental concepts as well as recently developed 3D Computer Vision techniques. Independent investigation for further reading, critical analysis, and evaluation of the topic is required. |
|---|---|
| Contact | M.Sc. Shivangi Aneja, M.Sc. Peter Kocsis |
| 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 Rössle, 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 Rössle |
| 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 Rössle |
| 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 Rössle |
| 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. |
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| 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! |
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| 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. |
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| 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). |
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| 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. |
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| 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. |
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| Contact | Prof. Dr. Nils Thuerey, Prof. Dr. Rüdiger Westermann, Prof. Dr. Matthias Nießner, Dr. Kiwon Um |