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
Michael Stark
Homepage of Research Group
First Name
Last Name
Visual Object Recognition and Scene Interpretation
Former Groups
Research Mission
The interpretation of visual scenes through computer vision algorithms has gained increasing importance in a wide variety of applications, such as information retrieval, robotics, and autonomous driving. A major challenge lies in the severely under-constrained nature of the scene interpretation problem, since visual observations alone are inherently ambiguous - one and the same image could possibly have been generated by a multitude of different physical scenes. Equally, the same physical scene might give rise to different semantic interpretations, depending on contextual information available to the observer. In our research, we aim to meet this challenge by following and combining two orthogonal directions. The first direction focuses on the robust recognition of individual objects that together constitute a scene, imposing an initial set of constraints on the space of plausible scene interpretations. For that purpose, we intend to extend learning-based object class recognition techniques to provide accurate estimates of object pose, viewpoint, and three-dimensional extent. The second direction reasons about a scene in its entirety, drawing from prior knowledge about feasible scene geometry and interactions among multiple objects, which further constrains the space of plausible interpretations. The identification of maxima in this space will require the design of efficient inference procedures, which will be an important focus of our research. Since 2015 Michael Stark is a scientist at Vicarious, an A.I. company in San Francisco.
Name of Research Group
Visual Object Recognition and Scene Interpretation

Personal Info