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Michael J. Black received his B.Sc. from the University of British Columbia (1985), his M.S. from Stanford (1989), and his Ph.D. in computer science from Yale University (1992). After research at NASA Ames and post-doctoral research at the University of Toronto, he joined the Xerox Palo Alto Research Center in 1993 where he later managed the Image Understanding Area and founded the Digital Video Analysis group. From 2000 to 2010 he was on the faculty of Brown University in the Department of Computer Science (Assoc. Prof. 2000-2004, Prof. 2004-2010). He is a founding director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he leads the Perceiving Systems department. He is an honorary professor at the University of Tübingen, a visiting professor at ETH Zürich, and an adjunct professor (research) at Brown University.
Black is a foreign member of the Royal Swedish Academy of Sciences. He is a recipient of the 2010 Koenderink Prize for Fundamental Contributions in Computer Vision and the 2013 Helmholtz Prize for work that has stood the test of time. His work has won several paper awards including the IEEE Computer Society Outstanding Paper Award (CVPR'91). His work received Honorable Mention for the Marr Prize in 1999 and 2005. His early work on optical flow has been widely used in Hollywood films including for the Academy-Award-winning effects in “What Dreams May Come” and “The Matrix Reloaded.” He is a co-founder and member of the board of directors of Body Labs Inc., which is commercializing his team’s research on 3D human body shape.
Prof. Black's research interests in machine vision include optical flow estimation, 3D shape models, human shape and motion analysis, robust statistical methods, and probabilistic models of the visual world. In computational neuroscience his work focuses on probabilistic models of the neural code and applications of neural decoding in neural prosthetics.
Michael Black received his B.Sc. from the University of British Columbia (1985), his M.S. from Stanford (1989), and his Ph.D. from Yale University (1992). After post-doctoral research at the University of Toronto, he worked at Xerox PARC as a member of research staff and an area manager. From 2000 to 2010 he was on the faculty of Brown University in the Department of Computer Science (Assoc. Prof. 2000-2004, Prof. 2004-2010). He is one of the founding directors at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he leads the Perceiving Systems department. He is an Honorarprofessor at the University of Tuebingen, Visiting Professor at ETH Zürich, and Adjunct Professor (Research) at Brown University. His work has won several awards including the IEEE Computer Society Outstanding Paper Award (1991), Honorable Mention for the Marr Prize (1999 and 2005), the 2010 Koenderink Prize for Fundamental Contributions in Computer Vision, and the 2013 Helmholtz Prize for work that has stood the test of time. He is a foreign member of the Royal Swedish Academy of Sciences. He is also a co-founder and board member of Body Labs Inc.
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Yale University, New Haven, CT
Ph.D., Computer Science, 1992.
Stanford University, Stanford, CA
M.S., Computer Science, 1989.
The University of British Columbia, Vancouver, BC
B.Sc., Honours Computer Science, 1985.
Royal Swedish Academy of Sciences
Foreign member, Class for Engineering Sciences, since June 2015.2013 Helmholtz Prize
for the paper: Black, M. J., and Anandan, P., "A framework for the robust estimation of optical flow,'' IEEE International Conference on Computer Vision, ICCV, pages 231-236, Berlin, Germany. May 1993.2010 Koenderink Prize for Fundamental Contributions in Computer Vision,
for the paper: Sidenbladh, H., Black, M. J., and Fleet, D. J., "Stochastic tracking of 3D human figures using 2D image motion,'' European Conference on Computer Vision, 2000.
D. Vernon (Ed.), Springer Verlag, LNCS 1843, Dublin, Ireland, pp. 702-718, June 2000.Best Paper Award, INI-Graphics Net, 2008, First Prize Winner of Category Research,
with S. Roth for the paper "Steerable random fields."Commendation and Chief's Award, Henrico County Division of Police,
County of Henrico, Virginia, April 19, 2007.Best Paper Award, Fourth International Conference on
Articulated Motion and Deformable Objects (AMDO-e 2006),
with L. Sigal for the paper "Predicting 3D people from 2D pictures.''Marr Prize, Honorable Mention, Int. Conf. on Computer
Vision, ICCV-2005, Beijing, China, Oct. 2005
with S. Roth for the paper "On the spatial statistics of optical flow.''Marr Prize, Honorable Mention, Int. Conf. on Computer
Vision, ICCV-99, Corfu, Greece, Sept. 1999
with D. J. Fleet for the paper "Probabilistic detection and tracking of motion discontinuities.''University of Maryland, Invention of the Year, 1995,
"Tracking and Recognizing Facial Expressions,''
with Y. Yacoob.University of Toronto, Computer Science Students' Union Teaching Award for 1992-1993.
IEEE Computer Society, Outstanding Paper Award,
Conference on Computer Vision and Pattern Recognition,
Maui, Hawaii, June 1991
with P. Anandan for the paper "Robust dynamic motion estimation over time.''
Max Planck Institute for Intelligent Systems
Tübingen, Germany
Director, 1/11 - present
Managing Director, 2/13 - 6/15
Eberhard Karls Universität Tübingen, Faculty of Science, Department of Computer Science
Tübingen, Germany
Honorarprofessor, 05/22/12 - present
ETH Zürich, Dept. of Information Technology and Electrical Engineering
Zürich, Switzerland
Visting Professor, 04/2014 - present
Stanford University, Electrical Engineering
Stanford, CA
Visiting Professor, 5/11-4/12, 7/12-7/13.
Brown University, Department of Computer Science,
Providence, RI
Adjunct Professor (Research), 1/11-present
Professor, 7/04-12/10
Associate Professor, 7/00-6/04
My research addresses the problem of estimating and explaining motion in image sequences. My early work focused on the use of robust statistics and mixture models for recovering image motion in situations involving occlusion. As motion estimation has improved, I have focused on the recognition of human motion. Current work is focusing on 3D human motion, Bayesian methods, learning temporal models, and particle filtering techniques.Xerox Palo Alto Research Center, (Now PARC Inc.)
Palo Alto, CA
Area Manager, Image Understanding Area, 1/96-7/00
Member of Research Staff, 9/93-12/95
Research included the development of a theory of ``appearance change'' in image sequences. The approach involves modeling image changes (motion, illumination, specularity, occlusion, etc.) as a ``mixture'' of causes. In this context I am exploring a shift from motion estimation to motion explanation in my work on the modeling and recognition of motion ``features'' (occlusion boundaries, moving bars, etc.), human facial expressions and gestures, and motion ``texture'' (plants, fire, water, etc.). I am particularly interested in the modeling and recognition of multi-media and HCI applications such as video indexing, motion for video annotation, teleconferencing, and gestural user interfaces. Other research includes robust learning of image-based models, regularization with transparency, anisotropic diffusion, and the recovery of multiple shapes from transparent textures.
University of Toronto,
Toronto, Ontario
Assistant Professor, Department of Computer Science, (8/92 - 9/93).
Research included the application of mixture models to optical flow, detection and tracking of surface discontinuities using motion information, and robust surface recovery in dynamic environments.
Yale University, (9/89-8/92)
New Haven, CT
Research Assistant, Department of Computer Science.Research in the recovery of optical flow, incremental estimation, temporal continuity, applications of robust statistics to optical flow, the relationship between robust statistics and line processes, the early detection of motion discontinuities, and the role of representation in computer vision.
NASA Ames Research Center, (6/90-8/92)
Moffett Field, CA
Visiting Researcher, Aerospace Human Factors Research Division.Developed motion estimation algorithms in the context of an autonomous Mars landing and nap-of-the-earth helicopter flight and studied the psychophysical implications of a temporal continuity assumption.
Advanced Decision Systems, (12/86-6/89)
Mountain View, CA
Computer Scientist, Image Understanding Group.Research on spatial reasoning for robotic vehicle route planning and terrain analysis. Vision research including perceptual grouping, object-based translational motion processing, the integration of vision and control for an autonomous vehicle, object modeling using generalized cylinders, and the development of an object-oriented vision environment.
GTE Government Systems, (6/85-12/86)
Mountain View, CA
Engineer, Artificial Intelligence Group.Developed expert systems for multi-source data fusion and fault location.
Miscellaneous, ('78-'85)
Summer undergraduate researcher at UBC; park ranger's assistant; volunteer firefighter, busboy; and probably my worst job: cleaning dog kennels.
My Computer Vision Research addresses:I also do research on neural engineering for brain-machine interfaces and neural prostheses.
- the statistics of natural scenes and their motion;
- articulated human motion pose estimation and tracking;
- the estimation of human body shape from images and video;
- the representation and detection of motion discontinuities;
- the estimation of optical flow.
Jonas Wulff, MPI for Intelligent Systems, Tübingen
Matthew Loper, MPI for Intelligent Systems, Tübingen
Silvia Zuffi, Berstein post doctoral fellow, Center for Integrative Neuroscience, University of Tubingen
Thesis: Shape Models of the Human Body for Distributed Inference, Brown University, May 2015.
Aggeliki Tsoli, Post doctoral researcher, FORTH Institute, Crete,
Thesis: Modeling the Human body in 3D: Data Registration and Human Shape Representation, Department of Computer Science, Brown University, May 2014
Oren Freifeld, Post doctoral researcher, MIT,
Thesis: Statistics on Manifolds with Applications to Modeling Shape Deformations, Division of Applied Mathematics, Brown University, August 2013
Peng Guan, Google,
Thesis: Virtual Human Bodies with Clothing and Hair: From Images to Animation, Department of Computer Science, Brown University, December 2012
Deqing Sun, Post doctoral researcher, Harvard University,
Thesis: From Pixels to Layers: Joint Motion Estimation and Segmentation, Department of Computer Science, Brown University, July 2012
Alexandru Balan, Microsoft
Thesis: Detailed Human Shape and Pose from Images, Department of Computer Science, Brown University, May 2010
Leonid Sigal, Research Scientist, Disney Research Pittsburgh
Thesis: Continuous-state graphical models for object localization, pose estimation and tracking Department of Computer Science, Brown University, May 2008
Stefan Roth, Professor, Dept. of Computer Science, TU Darmstadt
Thesis: High-order Markov random fields for low-level vision. Dept. of Computer Science, Brown University,
May 2007 Winner of the Joukowsky Family Foundation Outstanding Dissertation Award
Frank Wood, Associate Professor, Department of Engineering, Oxford
Thesis: Nonparametric Bayesian modeling of neural data. Department of Computer Science, Brown University
Hulya Yalcin, Assistant Professor, Department of Electronics and Communications Engineering, Istanbul Technical University, Turkey
Thesis: Implicit models of moving and static surfaces, Division of Engineering, Brown University, May 2004
Wei Wu, Associate Professor, Dept. of Statistics, Florida State
Thesis: Statistical models of neural coding in motor cortex, Division of Applied Math, Brown University. Co-supervised with David Mumford. May 2004.
Fernando De la Torre, Research Associate Professor, CMU
Thesis: Robust subspace learning for computer vision, La Salle School of Engineering. Universitat Ramon Llull, Barcelona, Spain. Jan. 2002
Hedvig Kjellstrom (nee Sidenbladh), Associate Professor (Lektor) of Comptuer Science, KTH, Sweden
Thesis: Probabilistic Tracking and Reconstruction of 3D Human Motion in Monocular Video Sequences. Dept. of Numerical Analysis and Computer Science, KTH, Stockholm, Sweden 2001
Shanon Ju
Thesis: Estimating image motion in layers: The Skin and Bones model. University of Toronto. Jan. 1999
Chaohui Wang, Assistant Professor, Laboratoire d'Informatique Gaspard Monge, Université Paris-Est, Paris, France.
Gregrory Shakhnarovich, Assistant Professor, Toyota Technological Institute at Chicago.
Sung-Phil Kim, Assistant Professor, Department of Brain and Cognitive Engineering, Korea University.
Ronan Fablet, Professor, Telecom Bretange.
I belive that computer vision is advanced by careful evaluation and comparison. Consequently I have been involved in building several public datasets and evaluation websites.
3D human scans of multiple people in multiple poses with accurate ground truth correspondences: FAUST site.
Optical flow benchmark based on the animate film Sintel: MPI-Sintel site.
Annotated videos for action recognition: JHMDB site.
Image sequences, ground truth flow, and evaluation are all available on the Middlebury Flow site.
Multi-camera imagery with ground truth 3D human pose: HumanEva site.
Multi-camera imagery with ground truth 3D human pose. This predates HumanEva and the imagery is grayscale only. There is also software for partical filter tracking on the site.
My old Brown site has several image sequences used in my older publications. These include some classic sequences such as Yosemite, the Pepsi can, the SRI tree sequence, and the Flower Garden sequence.
1. The most recent and most accurate optical flow code in Matlab
This code is descrbed in
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles behind Them
Sun, D., Roth, S., and Black, M.J.
International Journal of Computer Vision (IJCV), 106(2):115-137, 2014.
(pdf)Secrets of optical flow estimation and their principles
Sun, D., Roth, S., and Black, M. J.,
IEEE Conf. on Computer Vision and Pattern Recog., CVPR, June 2010.
(pdf)This method implements many of the currently best known techniques for accurate optical flow and was once ranked #1 on the Middlebury evaluation (June 2010).
The software is made available for research pupropses. Please read the copyright statement and contact me for commerical licensing.
2. Matlab implmentation of the Black and Anandan dense optical flow method
The Matlab flow code is easier to use and more accurate than the original C code. The objective function being optimized is the same but the Matlab version uses more modern optimization methods:
The method in 1 above is more accurate and also implements Black and Anandan plus much more.Matlab implementation of Black and Anandan robust dense optical flow algorithm
3. Original Black and Anandan method implemented in C
The optical flow software here has been used by a number of graphics companies to make special effects for movies. This software is provided for research purposes only; any sale or use for commercial purposes is strictly prohibited.
Contact me for the password to download the software, stating that it is for research purposes.
Please contact me if you wish to use this code for commercial purpose.
If you are a commercial enterprise and would like assistance in using optical flow in your application, please contact me at my consulting address black@opticalflow.com.
This is EXPERIMENTAL software. It is provided to illustrate some ideas in the robust estimation of optical flow. Use at your own risk. No warranty is implied by this distribution.
There are two versions available. First, the original C code implementing the robust flow methods described in Black and Anandan '96:
Area-based optical flow: robust affine regression.
Dense optical flow: robust regularization.
Reference:
The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields,
Black, M. J. and Anandan, P.,
Computer Vision and Image Understanding, CVIU, 63(1), pp. 75-104, Jan. 1996.
(pdf), (pdf from publisher)
Software is from the ICCV'2001 paper with Fernando De la Torre.
De la Torre, F. and Black, M. J., Robust principal component analysis for computer vision, to appear: Int. Conf. on Computer Vision, ICCV-2001, Vancouver, BC. (postscript, 1.0MB)(pdf, 0.36MB), (abstract)
The code below provides a simple Matlab implementation of the Bayesian 3D person tracking system described in ECCV'00 and ICCV'01. It is too slow to be used to track the entire body but can be used to track various limbs and provides a basis for people who want to understand the methods better and extend them.
Learning image statistics for Bayesian tracking,
Sidenbladh, H. and Black, M. J.,
Int. Conf. on Computer Vision, ICCV-2001, Vancouver, BC, Vol. II, pp. 709-716.
(postscript, 2.8MB)(pdf, 0.38MB), (abstract)
Stochastic tracking of 3D human figures using 2D image motion,
Sidenbladh, H., Black, M. J., and Fleet, D.J.,
European Conference on Computer Vision, D. Vernon (Ed.), Springer Verlag, LNCS 1843, Dublin, Ireland, pp. 702-718 June 2000.
(postscript)(pdf), (abstract)
Software. (Note: if you uncompress and untar this on a PC using Winzip, the path names may be lost which will cause Matlab to fail when you load the .mat files. Instead uncompress/untar using gunzip and tar.)
Brown Institute for Brain Science (BIBS), Member
How to reach me:
Mailing address
Michael J. Black
Max Planck Institute for Intelligent Systems
Spemannstrasse 41
72076 Tübingen
Germany
For more information including our address and directions, see the department CONTACT page.
I receive more email than I can read, let alone respond to. I apologize if you do not get a response. If you do not hear from me, consider the following:
My assistant reads mail sent to me at black@is.mpg.de. If you have something particularly private, you can email me at black@cs.brown.edu and only I will read it.
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