MZ

FotoFirst NameLast NamePosition
Christian Theobalt Graphics, Vision, Video
Thorsten Thormählen Image-based 3D Scene Analysis
Peter Vajda Personalized TeleVision News
Michael Wand Statistical Geometry Processing
Tino Weinkauf Feature-Based Data Analysis for Computer Graphics and Visualization
Martin Wicke
Martin Wicke Methods for Large-Scale Physical Modeling and Animation
Thomas Wiegand Image Processing
Stefanie Wuhrer Non-Rigid Shape Analysis
Michael Zollhöfer Visual Computing, Deep Learning and Optimization

Researcher


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
Stefanie Wuhrer
Homepage of Research Group
First Name
Stefanie
Last Name
Wuhrer
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wp.mpi-inf.mpg.de/mpc-vcc/files/2013/10/Wuhrer-small.jpg
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Position
Non-Rigid Shape Analysis
Categories
Former Groups
Research Mission
Shape analysis aims to describe either a single shape or a population of shapes in an efficient and informative way. This is a key problem in various applications, such as mesh deformation and animation, object recognition, and mesh parameterization. In order to analyze a population of shapes, intrinsic correspondence information between the shapes is required. This group’s work focuses on automatically computing intrinsic correspondence information between non-rigid shapes and on applying this information. Automatically computing intrinsic correspondence information between non-rigid shapes is a challenging problem because local regions on the surface are often not distinctive, especially as the surface undergoes large non-rigid deformations. Hence, to solve this problem, we assume prior knowledge about the geometry of the shapes to be registered, or about the type of deformation applied to the shapes. Once the correspondence information is available, we may use it in applications, such as morphing one shape into another, segmenting a population of shapes automatically, or extracting meaningful information from a population of shapes. For instance, we can learn information from a population of shapes and use this information as prior knowledge to synthesize a new shape based on partial information, such as 2D image information, partial scans, or measurements. This is of particular interest since most methods for acquiring 3D data obtain only partial surface data. In February 2015 Stefanie Wuhrer joined INRIA Grenoble Rhône-Alpes in France as a research scientist.
mission_rtf
Name of Research Group
Non-Rigid Shape Analysis

Personal Info

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