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2019


AirCap -- Aerial Outdoor Motion Capture
AirCap – Aerial Outdoor Motion Capture

Ahmad, A., Price, E., Tallamraju, R., Saini, N., Lawless, G., Ludwig, R., Martinovic, I., Bülthoff, H. H., Black, M. J.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), Workshop on Aerial Swarms, November 2019 (misc)

Abstract
This paper presents an overview of the Grassroots project Aerial Outdoor Motion Capture (AirCap) running at the Max Planck Institute for Intelligent Systems. AirCap's goal is to achieve markerless, unconstrained, human motion capture (mocap) in unknown and unstructured outdoor environments. To that end, we have developed an autonomous flying motion capture system using a team of aerial vehicles (MAVs) with only on-board, monocular RGB cameras. We have conducted several real robot experiments involving up to 3 aerial vehicles autonomously tracking and following a person in several challenging scenarios using our approach of active cooperative perception developed in AirCap. Using the images captured by these robots during the experiments, we have demonstrated a successful offline body pose and shape estimation with sufficiently high accuracy. Overall, we have demonstrated the first fully autonomous flying motion capture system involving multiple robots for outdoor scenarios.

Talk slides Project Page Project Page [BibTex]

2019

Talk slides Project Page Project Page [BibTex]


Method for providing a three dimensional body model
Method for providing a three dimensional body model

Loper, M., Mahmood, N., Black, M.

September 2019, U.S.~Patent 10,417,818 (misc)

Abstract
A method for providing a three-dimensional body model which may be applied for an animation, based on a moving body, wherein the method comprises providing a parametric three-dimensional body model, which allows shape and pose variations; applying a standard set of body markers; optimizing the set of body markers by generating an additional set of body markers and applying the same for providing 3D coordinate marker signals for capturing shape and pose of the body and dynamics of soft tissue; and automatically providing an animation by processing the 3D coordinate marker signals in order to provide a personalized three-dimensional body model, based on estimated shape and an estimated pose of the body by means of predicted marker locations.

MoSh Project pdf [BibTex]


Perceiving Systems (2016-2018)
Perceiving Systems (2016-2018)
Scientific Advisory Board Report, 2019 (misc)

pdf [BibTex]

pdf [BibTex]

2012


Coregistration: Supplemental Material
Coregistration: Supplemental Material

Hirshberg, D., Loper, M., Rachlin, E., Black, M. J.

(No. 4), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf [BibTex]

2012

pdf [BibTex]


Lie Bodies: A Manifold Representation of {3D} Human Shape. Supplemental Material
Lie Bodies: A Manifold Representation of 3D Human Shape. Supplemental Material

Freifeld, O., Black, M. J.

(No. 5), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


MPI-Sintel Optical Flow Benchmark: Supplemental Material
MPI-Sintel Optical Flow Benchmark: Supplemental Material

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J.

(No. 6), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Exploiting pedestrian interaction via global optimization and social behaviors
Exploiting pedestrian interaction via global optimization and social behaviors

Leal-Taixé, L., Pons-Moll, G., Rosenhahn, B.

In Theoretic Foundations of Computer Vision: Outdoor and Large-Scale Real-World Scene Analysis, Springer, April 2012 (incollection)

pdf [BibTex]

pdf [BibTex]


HUMIM Software for Articulated Tracking
HUMIM Software for Articulated Tracking

Soren Hauberg, Kim S. Pedersen

(01/2012), Department of Computer Science, University of Copenhagen, January 2012 (techreport)

Code PDF [BibTex]

Code PDF [BibTex]


A geometric framework for statistics on trees
A geometric framework for statistics on trees

Aasa Feragen, Mads Nielsen, Soren Hauberg, Pechin Lo, Marleen de Bruijne, Francois Lauze

(11/02), Department of Computer Science, University of Copenhagen, January 2012 (techreport)

PDF [BibTex]

PDF [BibTex]


Data-driven Manifolds for Outdoor Motion Capture
Data-driven Manifolds for Outdoor Motion Capture

Pons-Moll, G., Leal-Taix’e, L., Gall, J., Rosenhahn, B.

In Outdoor and Large-Scale Real-World Scene Analysis, 7474, pages: 305-328, LNCS, (Editors: Dellaert, Frank and Frahm, Jan-Michael and Pollefeys, Marc and Rosenhahn, Bodo and Leal-Taix’e, Laura), Springer, 2012 (incollection)

video publisher's site pdf Project Page [BibTex]

video publisher's site pdf Project Page [BibTex]


Scan-Based Flow Modelling in Human Upper Airways
Scan-Based Flow Modelling in Human Upper Airways

Perumal Nithiarasu, Igor Sazonov, Si Yong Yeo

In Patient-Specific Modeling in Tomorrow’s Medicine, pages: 241 - 280, 0, (Editors: Amit Gefen), Springer, 2012 (inbook)

[BibTex]

[BibTex]


An Introduction to Random Forests for Multi-class Object Detection
An Introduction to Random Forests for Multi-class Object Detection

Gall, J., Razavi, N., van Gool, L.

In Outdoor and Large-Scale Real-World Scene Analysis, 7474, pages: 243-263, LNCS, (Editors: Dellaert, Frank and Frahm, Jan-Michael and Pollefeys, Marc and Rosenhahn, Bodo and Leal-Taix’e, Laura), Springer, 2012 (incollection)

code code for Hough forest publisher's site pdf Project Page [BibTex]

code code for Hough forest publisher's site pdf Project Page [BibTex]


Home {3D} body scans from noisy image and range data
Home 3D body scans from noisy image and range data

Weiss, A., Hirshberg, D., Black, M. J.

In Consumer Depth Cameras for Computer Vision: Research Topics and Applications, pages: 99-118, 6, (Editors: Andrea Fossati and Juergen Gall and Helmut Grabner and Xiaofeng Ren and Kurt Konolige), Springer-Verlag, 2012 (incollection)

Project Page [BibTex]

Project Page [BibTex]

2011


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ISocRob-MSL 2011 Team Description Paper for Middle Sized League

Messias, J., Ahmad, A., Reis, J., Sousa, J., Lima, P.

15th Annual RoboCup International Symposium 2011, July 2011 (techreport)

Abstract
This paper describes the status of the ISocRob MSL robotic soccer team as required by the RoboCup 2011 qualification procedures. The most relevant technical and scientifical developments carried out by the team, since its last participation in the RoboCup MSL competitions, are here detailed. These include cooperative localization, cooperative object tracking, planning under uncertainty, obstacle detection and improvements to self-localization.

link (url) [BibTex]

2011

link (url) [BibTex]


Benchmark datasets for pose estimation and tracking
Benchmark datasets for pose estimation and tracking

Andriluka, M., Sigal, L., Black, M. J.

In Visual Analysis of Humans: Looking at People, pages: 253-274, (Editors: Moesland and Hilton and Kr"uger and Sigal), Springer-Verlag, London, 2011 (incollection)

publisher's site Project Page [BibTex]

publisher's site Project Page [BibTex]


Fields of experts
Fields of experts

Roth, S., Black, M. J.

In Markov Random Fields for Vision and Image Processing, pages: 297-310, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)

Abstract
Fields of Experts are high-order Markov random field (MRF) models with potential functions that extend over large pixel neighborhoods. The clique potentials are modeled as a Product of Experts using nonlinear functions of many linear filter responses. In contrast to previous MRF approaches, all parameters, including the linear filters themselves, are learned from training data. A Field of Experts (FoE) provides a generic, expressive image prior that can capture the statistics of natural scenes, and can be used for a variety of machine vision tasks. The capabilities of FoEs are demonstrated with two example applications, image denoising and image inpainting, which are implemented using a simple, approximate inference scheme. While the FoE model is trained on a generic image database and is not tuned toward a specific application, the results compete with specialized techniques.

publisher site [BibTex]

publisher site [BibTex]


Dorsal Stream: From Algorithm to Neuroscience
Dorsal Stream: From Algorithm to Neuroscience

Jhuang, H.

PhD Thesis, MIT, 2011 (techreport)

pdf [BibTex]


Steerable random fields for image restoration and inpainting
Steerable random fields for image restoration and inpainting

Roth, S., Black, M. J.

In Markov Random Fields for Vision and Image Processing, pages: 377-387, (Editors: Blake, A. and Kohli, P. and Rother, C.), MIT Press, 2011 (incollection)

Abstract
This chapter introduces the concept of a Steerable Random Field (SRF). In contrast to traditional Markov random field (MRF) models in low-level vision, the random field potentials of a SRF are defined in terms of filter responses that are steered to the local image structure. This steering uses the structure tensor to obtain derivative responses that are either aligned with, or orthogonal to, the predominant local image structure. Analysis of the statistics of these steered filter responses in natural images leads to the model proposed here. Clique potentials are defined over steered filter responses using a Gaussian scale mixture model and are learned from training data. The SRF model connects random fields with anisotropic regularization and provides a statistical motivation for the latter. Steering the random field to the local image structure improves image denoising and inpainting performance compared with traditional pairwise MRFs.

publisher site [BibTex]

publisher site [BibTex]


Model-Based Pose Estimation
Model-Based Pose Estimation

Pons-Moll, G., Rosenhahn, B.

In Visual Analysis of Humans: Looking at People, pages: 139-170, 9, (Editors: T. Moeslund, A. Hilton, V. Krueger, L. Sigal), Springer, 2011 (inbook)

book page pdf [BibTex]

book page pdf [BibTex]

2007


Probabilistically modeling and decoding neural population activity in motor cortex
Probabilistically modeling and decoding neural population activity in motor cortex

Black, M. J., Donoghue, J. P.

In Toward Brain-Computer Interfacing, pages: 147-159, (Editors: Dornhege, G. and del R. Millan, J. and Hinterberger, T. and McFarland, D. and Muller, K.-R.), MIT Press, London, 2007 (incollection)

pdf [BibTex]

2007

pdf [BibTex]


Denoising archival films using a learned {Bayesian} model
Denoising archival films using a learned Bayesian model

Moldovan, T. M., Roth, S., Black, M. J.

(CS-07-03), Brown University, Department of Computer Science, 2007 (techreport)

pdf [BibTex]

pdf [BibTex]