| Foto | First Name | Last Name | Position |
|---|---|---|---|
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Vera | Demberg | Cognitive Models of Human Language Processing and their Application to Dialogue Systems |
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Elmar | Eisemann | Computer Graphics and Visualization |
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Markus | Flierl | Visual Sensor Networks |
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Markus | Flierl | Visual Sensor Networks |
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Stefan | Funke | Geometry-Guided Design and Analysis of Wireless Sensor Networks |
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Stefan | Funke | Geometry-Guided Design and Analysis of Wireless Sensor Networks |
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Joachim | Giesen | Learning of Geometry: Given samples obtained from a shape we want to learn some of its geometric and topological characteristics. A popular example that fits in this framework is surface reconstruction: to obtain a digital model of some solid one samples |
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Joachim | Giesen | Learning of Geometry |
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Stefan | Gumhold | 3D Animation Processing |
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Stefan | Gumhold | 3D Animation Processing |
Researcher
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Dr. Michael Zollhöfer |
Visual Computing, Deep Learning and Optimization
| Name of Research Group: | Visual Computing, Deep Learning and Optimization |
| Homepage Research Group: | web.stanford.edu/~zollhoef |
| Personal Homepage: | zollhoefer.com |
| Mentor Saarbrücken: | Hans-Peter Seidel |
| Mentor Stanford: | Pat Hanrahan |
| Research Mission: | The primary focus of my research is to teach computers to reconstruct and analyze our world at frame rate based on visual input. The extracted knowledge is the foundation for a broad range of applications not only in visual effects, computer animation, autonomous driving and man-machine interaction, but is also essential in other related fields such as medicine and biomechanics. Especially, with the increasing popularity of virtual, augmented and mixed reality, there comes a rising demand for real-time low latency solutions to the underlying core problems. My research tackles these challenges based on novel mathematical models and algorithms that enable computers to first reconstruct and subsequently analyze our world. The main focus is on fast and robust algorithms that approach the underlying reconstruction and machine learning problems for static as well as dynamic scenes. To this end, I develop key technology to invert the image formation models of computer graphics based on data-parallel optimization and state-of-the-art deep learning techniques. The extraction of 3D and 4D information from visual data is highly challenging and under-constraint, since image formation convolves multiple physical dimensions into flat color measurements. 3D and 4D reconstruction at real-time rates poses additional challenges, since it involves the solution of unique challenges at the intersection of multiple important research fields, namely computer graphics, computer vision, machine learning, optimization, and high-performance computing. However, a solution to these problems provides strong cues for the extraction of higher-order semantic knowledge. It is incredibly important to solve the underlying core problems, since this will have high impact in multiple important research fields and provide key technological insights that have the potential to transform the visual computing industry. In summer 2019 Michael Zollhöfer joined Facebook. |
Researcher
- Name of Researcher
- Carsten Stoll
- Homepage of Research Group
- First Name
- Carsten
- Last Name
- Stoll
- Foto
- Homepage
- Phone
- Position
- Optical Performace Capture
- Mentor in Saarbruecken
- Christian Theobalt
- Mentor in Stanford
- Categories
- Former Groups
- Research Mission
- Creating high-quality and realistic virtual content is nowadays an important task for modern virtual worldsMe games and movies. While it is possible to roughly capture the motion and static shape of performers and scenes, capturing subtle surface motion details and appearance of actors (such as muscle bulging, cloth motion or facial wrinkles) and details of real scenes (such as moving leaves on trees) is still a challenging problem. The group focuses on researching novel techniques for capturing and processing highly detailed models of performers and scenes in motion using optical methods. Recent years have seen remarkable advances in the field, and so called performance capture methods have been developed that are able to capture motion, coherent geometry and appearance from a sparse set of input videos. However, many algorithms to date still do not produce results on a quality level that would be necessary to apply them in production environments. Achieving high-quality reconstructions that satisfy the requirements of these environments with as little human interaction as possible provide a whole new set of research challenges. In our work we plan to develop new methods for simultaneous motion, geometry and appearance acquisition that bring us closer to the goal of reconstructing completely realistic virtual performances and scenes.
- mission_rtf
- Name of Research Group
- Optical Performace Capture
Personal Info
- Photo
- Website, Blog or Social Media Link
