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 has contributed to several influential datasets including the Middlebury Flow dataset, HumanEva, and the Sintel dataset. He is a co-founder, science advisor, 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, science advisor, and board member of Body Labs Inc.
Royal Swedish Academy of Sciences
Foreign member, Class for Engineering Sciences, since June 2015.
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.
2010Koenderink Prize for Fundamental Contributions in Computer Vision,
with Sidenbladh, H. and Fleet, D. J. for the paper "Stochastic tracking of 3D human figures using 2D image motion,'' European Conference on Computer Vision, 2000.
"Dataset Award" at the Eurographics Symposium on Geometry Processing 2016, with F. Bogo, J. Romero, and M. Loper, for the paper "FAUST: Dataset and evaluation for 3D mesh registration," CVPR 2014.
Best Paper Award, International Conference on 3D Vision (3DV), 2015, with A. O. Ulusoy and A. Geiger, for the paper "Towards Probabilistic Volumetric Reconstruction using Ray Potentials."
Best Paper Award, INI-Graphics Net, 2008, First Prize Winner of Category Research,
with S. Roth for the paper "Steerable random fields."
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.''
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.''
Commendation and Chief's Award, Henrico County Division of Police,
County of Henrico, Virginia, April 19, 2007.
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.
My research addressed the problem of estimating and explaining motion in image sequences. I developed methods detecting and tracking 2D and 3D human motion including the introduction of particle filtering for 3D human tracking and belief propagation for 3D human pose estimation. I worked on probabilistic models of images include the high-order Field of Experts model. I worked on 3D human shape estimation from images and video and developed applications of this technology. I also developed mathematical models for decoding neural signals. This included the first uses of particle filtering and Kalman filtering for decoding motor cortical neural activity and the first point-and-click cortical neural brain-machine-interface for people with paralysis.
Research included modeling image changes (motion, illumination, specularity, occlusion, etc.) in video as a mixture of causes. I developed methods of motion explanation; that is, the extraction of mid-level or high-level concepts from motion.This included 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 applied these methods to problems in video indexing, motion for video annotation, teleconferencing, and gestural user interfaces. Other research included robust learning of image-based models, regularization with transparency, anisotropic diffusion, and the recovery of multiple shapes from transparent textures.
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.
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.
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.
Summer undergraduate researcher at UBC; park ranger's assistant; volunteer firefighter, busboy; and probably my worst job: cleaning dog kennels.
I am interested in motion. What does motion tell us about the structure of the world and how can we compute this from video? How do humans and animals move? How does the brain control complex movement? My work combines computer vision, graphics and neuroscience to develop new models and algorithms to capture and analyze the motion of the world.
My Computer Vision research addresses:
the estimation of scene structure and physical properties from video;
modeling the neural control of reaching and grasping;
novel neural decoding algorithms;
neural prostheses and cortical brain-machine interfaces;
markless animal motion capture.
What is maybe unique about my work is the combination of the these themes. For example I study human motion from the inside (decoding neural activity in paralyzed humans) and the outside (with novel motion capture techniques).
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 and Facebook,
Thesis: Robust subspace learning for computer vision, La Salle School of Engineering. Universitat Ramon Llull, Barcelona, Spain. Jan. 2002
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.
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.
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 firstname.lastname@example.org.
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.
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)
Robust Principal Component Analysis (PCA)
Software is from the ICCV'2001 paper with Fernando De la Torre.
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.
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.)
Wu, W., Black, M. J., Mumford, D., Gao, Y., Bienenstock, E., Donoghue, J.
IEEE Trans. Biomedical Engineering, 51(6):933-942, June 2004 (article)
We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A “hidden state” models the probability of each mixture component and evolves over time in a Markov chain. The model generalizes previous encoding and decoding methods, addresses
the non-Gaussian nature of firing rates, and can cope with crudely sorted neural data common in on-line prosthetic applications.
In Advances in Neural Information Processing Systems 16, NIPS, pages: 1539-1546, (Editors: S. Thrun and L. K. Saul and B. Schölkopf), MIT Press, 2003 (inproceedings)
The detection and pose estimation of people in images and video is made challenging by the variability of human appearance, the complexity of natural scenes, and the high dimensionality of articulated body models. To cope with these problems we represent the 3D human body as a graphical model in which the relationships between the body parts are represented by conditional probability distributions. We formulate the pose estimation problem as one of probabilistic inference over a graphical model where the random variables correspond to the individual limb parameters (position and orientation). Because the limbs are described by 6-dimensional vectors encoding pose in 3-space, discretization is impractical and the random variables in our model must be continuous-valued. To approximate belief propagation in such a graph we exploit a recently introduced generalization of the particle filter. This framework facilitates the automatic initialization of the body-model from low level cues and is robust to occlusion of body parts and scene clutter.
International Journal of Computer Vision, 54(1-3):183-209, August 2003 (article)
This paper address the problems of modeling the appearance of humans and distinguishing human appearance from the appearance of general scenes. We seek a model of appearance and motion that is generic in that it accounts for the ways in which people's appearance varies and, at the same time, is specific enough to be useful for tracking people in natural scenes. Given a 3D model of the person projected into an image we model the likelihood of observing various image cues conditioned on the predicted locations and orientations of the limbs. These cues are taken to be steered filter responses corresponding to edges, ridges, and motion-compensated temporal differences. Motivated by work on the statistics of natural scenes, the statistics of these filter responses for human limbs are learned from training images containing hand-labeled limb regions. Similarly, the statistics of the filter responses in general scenes are learned to define a “background” distribution. The likelihood of observing a scene given a predicted pose of a person is computed, for each limb, using the likelihood ratio between the learned foreground (person) and background distributions. Adopting a Bayesian formulation allows cues to be combined in a principled way. Furthermore, the use of learned distributions obviates the need for hand-tuned image noise models and thresholds. The paper provides a detailed analysis of the statistics of how people appear in scenes and provides a connection between work on natural image statistics and the Bayesian tracking of people.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems