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2016


Skinned multi-person linear model
Skinned multi-person linear model

Black, M.J., Loper, M., Mahmood, N., Pons-Moll, G., Romero, J.

December 2016, Application PCT/EP2016/064610 (misc)

Abstract
The invention comprises a learned model of human body shape and pose dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity- dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. The invention quantitatively evaluates variants of SMPL using linear or dual- quaternion blend skinning and show that both are more accurate than a Blend SCAPE model trained on the same data. In a further embodiment, the invention realistically models dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.

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Google Patents [BibTex]

2016


Google Patents [BibTex]


Perceiving Systems (2011-2015)
Perceiving Systems (2011-2015)
Scientific Advisory Board Report, 2016 (misc)

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pdf [BibTex]

pdf [BibTex]


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Extrapolation and learning equations

Martius, G., Lampert, C. H.

2016, arXiv preprint \url{https://arxiv.org/abs/1610.02995} (misc)

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Project Page [BibTex]

Project Page [BibTex]

2014


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2014, Scene Understanding Workshop (SUNw, CVPR workshop) (unpublished)

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[BibTex]

2014


[BibTex]


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Active Recognition and Manipulation for Mobile Robot Bin Picking

Holz, D., Nieuwenhuisen, M., Droeschel, D., Stueckler, J., Berner, A., Li, J., Klein, R., Behnke, S.

In Gearing Up and Accelerating Cross-fertilization between Academic and Industrial Robotics Research in Europe: Technology Transfer Experiments from the ECHORD Project, pages: 133-153, Springer, 2014 (inbook)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Increasing Flexibility of Mobile Manipulation and Intuitive Human-Robot Interaction in RoboCup@Home

Stueckler, J., Droeschel, D., Gräve, K., Holz, D., Schreiber, M., Topaldou-Kyniazopoulou, A., Schwarz, M., Behnke, S.

In RoboCup 2013, Robot Soccer World Cup XVII, pages: 135-146, Springer, 2014 (inbook)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Robot Learning by Guided Self-Organization

Martius, G., Der, R., Herrmann, J. M.

In Guided Self-Organization: Inception, 9, pages: 223-260, Emergence, Complexity and Computation, Springer Berlin Heidelberg, 2014 (incollection)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Simulated Annealing
Simulated Annealing

Gall, J.

In Encyclopedia of Computer Vision, pages: 737-741, 0, (Editors: Ikeuchi, K. ), Springer Verlag, 2014, to appear (inbook)

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[BibTex]

[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)

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pdf [BibTex]

2007


pdf [BibTex]