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2019


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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.

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Talk slides Project Page Project Page [BibTex]

2019


Talk slides Project Page Project Page [BibTex]


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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.

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MoSh Project pdf [BibTex]


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

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pdf [BibTex]

pdf [BibTex]


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Analytical classical density functionals from an equation learning network

Lin, S., Martius, G., Oettel, M.

2019, arXiv preprint \url{https://arxiv.org/abs/1910.12752} (misc)

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[BibTex]

[BibTex]

2018


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A machine from machines

Fischer, P.

Nature Physics, 14, pages: 1072–1073, July 2018 (misc)

Abstract
Building spinning microrotors that self-assemble and synchronize to form a gear sounds like an impossible feat. However, it has now been achieved using only a single type of building block -- a colloid that self-propels.

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link (url) DOI [BibTex]

2018


link (url) DOI [BibTex]


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Co-Registration – Simultaneous Alignment and Modeling of Articulated 3D Shapes

Black, M., Hirshberg, D., Loper, M., Rachlin, E., Weiss, A.

Febuary 2018, U.S.~Patent 9,898,848 (misc)

Abstract
Present application refers to a method, a model generation unit and a computer program (product) for generating trained models (M) of moving persons, based on physically measured person scan data (S). The approach is based on a common template (T) for the respective person and on the measured person scan data (S) in different shapes and different poses. Scan data are measured with a 3D laser scanner. A generic personal model is used for co-registering a set of person scan data (S) aligning the template (T) to the set of person scans (S) while simultaneously training the generic personal model to become a trained person model (M) by constraining the generic person model to be scan-specific, person-specific and pose-specific and providing the trained model (M), based on the co registering of the measured object scan data (S).

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text [BibTex]