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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]

2018


Method and Apparatus for Estimating Body Shape
Method and Apparatus for Estimating Body Shape

Black, M. J., Balan, A., Weiss, A., Sigal, L., Loper, M., St Clair, T.

June 2018, U.S.~Patent 10,002,460 (misc)

Abstract
A system and method of estimating the body shape of an individual from input data such as images or range maps. The body may appear in one or more poses captured at different times and a consistent body shape is computed for all poses. The body may appear in minimal tight-fitting clothing or in normal clothing wherein the described method produces an estimate of the body shape under the clothing. Clothed or bare regions of the body are detected via image classification and the fitting method is adapted to treat each region differently. Body shapes are represented parametrically and are matched to other bodies based on shape similarity and other features. Standard measurements are extracted using parametric or non-parametric functions of body shape. The system components support many applications in body scanning, advertising, social networking, collaborative filtering and Internet clothing shopping.

Google Patents Project Page [BibTex]

2018

Google Patents Project Page [BibTex]


Co-Registration -- Simultaneous Alignment and Modeling of Articulated {3D} Shapes
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).

text [BibTex]

2014


Advanced Structured Prediction
Advanced Structured Prediction

Nowozin, S., Gehler, P. V., Jancsary, J., Lampert, C. H.

Advanced Structured Prediction, pages: 432, Neural Information Processing Series, MIT Press, November 2014 (book)

Abstract
The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.

publisher link (url) [BibTex]

2014

publisher link (url) [BibTex]


Learning People Detectors for Tracking in Crowded Scenes.
Learning People Detectors for Tracking in Crowded Scenes.

Tang, S., Andriluka, M., Milan, A., Schindler, K., Roth, S., Schiele, B.

2014, Scene Understanding Workshop (SUNw, CVPR workshop) (unpublished)

[BibTex]

[BibTex]


Human Pose Estimation from Video and Inertial Sensors
Human Pose Estimation from Video and Inertial Sensors

Pons-Moll, G.

Ph.D Thesis, -, 2014 (book)

Abstract
The analysis and understanding of human movement is central to many applications such as sports science, medical diagnosis and movie production. The ability to automatically monitor human activity in security sensitive areas such as airports, lobbies or borders is of great practical importance. Furthermore, automatic pose estimation from images leverages the processing and understanding of massive digital libraries available on the Internet. We build upon a model based approach where the human shape is modelled with a surface mesh and the motion is parametrized by a kinematic chain. We then seek for the pose of the model that best explains the available observations coming from different sensors. In a first scenario, we consider a calibrated mult-iview setup in an indoor studio. To obtain very accurate results, we propose a novel tracker that combines information coming from video and a small set of Inertial Measurement Units (IMUs). We do so by locally optimizing a joint energy consisting of a term that measures the likelihood of the video data and a term for the IMU data. This is the first work to successfully combine video and IMUs information for full body pose estimation. When compared to commercial marker based systems the proposed solution is more cost efficient and less intrusive for the user. In a second scenario, we relax the assumption of an indoor studio and we tackle outdoor scenes with background clutter, illumination changes, large recording volumes and difficult motions of people interacting with objects. Again, we combine information from video and IMUs. Here we employ a particle based optimization approach that allows us to be more robust to tracking failures. To satisfy the orientation constraints imposed by the IMUs, we derive an analytic Inverse Kinematics (IK) procedure to sample from the manifold of valid poses. The generated hypothesis come from a lower dimensional manifold and therefore the computational cost can be reduced. Experiments on challenging sequences suggest the proposed tracker can be applied to capture in outdoor scenarios. Furthermore, the proposed IK sampling procedure can be used to integrate any kind of constraints derived from the environment. Finally, we consider the most challenging possible scenario: pose estimation of monocular images. Here, we argue that estimating the pose to the degree of accuracy as in an engineered environment is too ambitious with the current technology. Therefore, we propose to extract meaningful semantic information about the pose directly from image features in a discriminative fashion. In particular, we introduce posebits which are semantic pose descriptors about the geometric relationships between parts in the body. The experiments show that the intermediate step of inferring posebits from images can improve pose estimation from monocular imagery. Furthermore, posebits can be very useful as input feature for many computer vision algorithms.

pdf [BibTex]

2008


GNU Octave Manual Version 3
GNU Octave Manual Version 3

John W. Eaton, David Bateman, Soren Hauberg

Network Theory Ltd., October 2008 (book)

Publishers site GNU Octave [BibTex]

2008

Publishers site GNU Octave [BibTex]