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
Pierpaolo Baccichet
Homepage of Research Group
First Name
Last Name
Distributed Media Systems
Former Groups
Research Mission
Nowadays, several applications require the transmission of multimedia content to clients, possibly sparse all over the world. While multimedia data delivery presents very stringent requirements in terms of bandwidth, delay and jitter, today's networks often fail to provide the necessary "quality of service." Congestion and network failures could cause severe degradation in the quality perceived by the final users. In particular, the delivery of video content over an error-prone network poses some important challenges due to the predictive structure of compressed signal. In this case, an error affecting one image usually is usually propagated over several consequent pictures due to temporal prediction. The activity of this group is focused on the development of novel techniques for the transmission of compressed video to a large number of users either over the Internet or a wireless local area network. Group communication from one source to many destinations is often involved in the transmission of video over the Internet. In this scenario, the classical client-server architecture fails to scale with the number of clients attached to the system, mostly because of the fixed amount of outgoing bandwidth supported by the server. In this case, a peer-to-peer (P2P) network can be exploited to increase the performance. In fact, P2P networks provide a "zero-cost" and extremely flexible infrastructure for the delivery of multimedia content, since the amount of bandwidth available to each client allows the relay of the data to other peers in a distributed fashion. Obviously, the dynamics of P2P networks, join and leave frequency and the heterogeneity of the connections technologies still require a lot of research to develop stable solutions. Improvements may be introduced in the communication protocol, designing algorithms that ensure the connectivity without an excessive increase in terms of control overhead. Another interesting scenario is the transmission over a wireless network. In this case, the end-to-end delay can be low enough to allow to exploit network feedback. Information from lower levels in the network protocol stack can be used to dynamically adapt working parameters for both the source and the network coder. For instance, a network-aware rate control can be used to react to network congestion. These techniques are referred as "Cross-Layer" design, since they exploit the exchange of information among different layers in the network protocol stack. Finally, some novel solutions can also be introduced in the source coder to provide more "robust" representations of the signal. Techniques such as Forward Error Correction or Multiple Description Coding increase the resilience of the signal, providing a graceful degradation of the perceived video quality with worsening channel conditions.
Name of Research Group
Distributed Media Systems

Personal Info