| Foto | First Name | Last Name | Position |
|---|---|---|---|
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Klaus | Hildebrandt | Applies Geometry |
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Matthias | Hullin | Computational Transient Imaging |
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Ivo | Ihrke | Generalized Image Acquisition and Analysis |
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Andreas | Karrenbauer | Discrete Optimization |
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Michael | Kerber | Topological and Geometric Computing |
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Haricharan | Lakshman | Immersive Video |
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Hendrik | Lensch | General Appearance Acquisition |
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Hendrik | Lensch | General Appearance Acquisition |
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Yangyan | Li | Semantic Reconstruction from Point Cloud |
|
|
Markus | Magnor | Graphics - Optics - Vision |
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
- Christian Theobalt
- Homepage of Research Group
- First Name
- Christian
- Last Name
- Theobalt
- Foto
- Homepage
- people.mpi-inf.mpg.de/~theobalt/
- Phone
- Position
- Graphics, Vision, Video
- Mentor in Saarbruecken
- Hans-Peter Seidel
- Mentor in Stanford
- Categories
- Former Groups
- Research Mission
- The research group "Graphics, Vision and Video" investigates problems that live on the boundary between the fields Computer Graphics and Computer Vision. One major line of research in Computer Vision aims at developing methods for acquiring dynamic scenes with video cameras and estimating model descriptions of the scenes from the recorded data. These model descriptions typically comprise of models of shape, models of motion or models of physical material properties. The main goal of Computer Graphics, on the other hand, has been to display such model descriptions realistically. In our work, we investigate the problems of acquisition, reconstruction and display of dynamic scenes in conjunction and develop novel algorithmic concepts for each of these questions. In particular we develop methods for dynamic shape and appearance reconstruction, motion estimation, animation of complex deformable models, and real-time rendering and relighting. Ultimately, it is our goal to generate realistic renderings of image- or video-captured dynamic scenes from arbitrary virtual camera views. One very young line of research that aims at putting this video-based rendering paradigm into practice, is 3D or Free-Viewpoint Video. Here, we will extend our previous work on model-based free-viewpoint video of human actors and develop novel algorithms that enable us to process more general scenes. Christian Theobalt is a Senior Researcher at MPI for Informatics and professor at Saarland University.
- mission_rtf
- Name of Research Group
- Graphics, Vision, Video
Personal Info
- Photo
- Website, Blog or Social Media Link
