FotoFirst NameLast NamePosition
Mykhaylo Andriluka People Detection and Tracking
Roland Angst Vision, Geometry, and Computational Perception
Tamay Aykut
Vahid Babaei
Pierpaolo Baccichet Distributed Media Systems
Volker Blanz Learning-Based Modeling of Objects
Volker Blanz Learning-Based Modeling of Objects
Martin Bokeloh Inverse Procedural Modeling
Adrian Butscher Geometry Processing and Discrete Differential Geometry
Renjie Chen Images and Geometry


Dr. Michael Zollhöfer

Visual Computing, Deep Learning and Optimization

Name of Research Group: Visual Computing, Deep Learning and Optimization
Homepage Research Group:
Personal Homepage:
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.


Name of Researcher
Meinard Müller
Homepage of Research Group
First Name
Last Name
Multimedia Retrieval
Former Groups
Research Mission
Modern information society is experiencing an explosion of digital content, comprising text, audio, video and graphics. The challenge is to organize, understand, and search multimodal information in a robust, efficient and intelligent manner. One challenge arises from the fact that multimedia objects, even though they are similar from a structural or semantic viewpoint, often reveal significant spatial or temporal differences. This makes content-based multimedia retrieval a challenging research field with many unsolved problems. In my habilitation project conducted at Bonn University, we studied fundamental algorithms and concepts for the analysis, classification, indexing, and retrieval of time-dependent data streams by means of two different types of multimedia data: waveform-based music data and human motion data. In the music domain, we developed techniques for automatic music alignment, synchronization, and matching. The common goal of these tasks is to automatically link several types of music representations, thus coordinating the multiple information sources related to a given musical work. In the motion domain, we introduced a general and unified framework for motion analysis, retrieval, and classification using binary features to represent poses. By handling spatio-temporal motion deformations already on the feature level, we were able to adopt efficient indexing methods allowing for flexible and efficient content-based retrieval for large motion capture data sets.
Name of Research Group
Multimedia Retrieval

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